SUVERY DATA ANALYSIS: ANALYZING COVID-19 SURVEY DATA
Lumumba Wandera Victor
2023-07-12
- ANALYZING SURVEY DATA
- ADDITIONAL INFORMATION
- Data descriptives
- Let’s examine our variables.
- Load the follwoing library
- Get Frequency Tables for the Categorical Variables
- Additional Frequency Tables
- Frequency Table for Ideology
- We can also examine crosstabs!
- Data recoding
- Create lables for covid_us
- Data analysis
- Dependent variable
- Estimate
the Model
- View the Results using tab_model() function
- Interpretation
- Standardized coefficients:
- We can create more readable coefficient names to use here:
- Sex (sex_rMale)
- Age Category (age_cat)
- Race
- Education (edu)
- Income (income)
- Political Party (party_r2Ind/Oth, party_r2Rep)
- Ideology (ideology)
- Specific Variables in Model 2
- Generate pretty tables for your papers:
- We can now save the table in a pretty format:
- ADDITIONAL INFORMATION
ANALYZING SURVEY DATA
Reading the data
To take a look at survey data, we will work with a dataset adaptedfrom Pew Research Center’s American Trends Panel, Wave 67, 05/2020 Available online at www.pewresearch.org/american-trends-panel-datasets. You can download the data and its description from: http://kateto.net/css/pew_data.zip
First we will read the data, which is stored in a CSV file. In their documentation, PEW note that “Refused” answers are coded as 99 As we read in the data, we can tell R to replace 99 with NA (missing)
| ID | covid_trump | covid_state | covid_local | covid_pubhealth | covid_media | covid_doctors | cov_threat_us | cov_threat_me | cov_threat_econ | cov_threat_fin | cov_evidence | cov_understand | soc_distance | covid_usa | covid_uk | covid_italy | covid_china | covid_vaccine | covid_drug | covid_outbreak | vaccinate | diagnose | vulnerable | know_covid | know_dead | employ | household | fired | paycut | fired_house | paycut_house | metro | region | division | age_cat | sex | edu_cat | edu | hispanic | race | race_ethn | citizen | marital | religion | evang | attendance | party | party_lean | party_comb | income | registered | ideology | covid_news | weight |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100197 | 4 | 1 | 2 | 3 | 2 | 1 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 4 | 3 | 3 | 4 | 2 | 2 | 2 | 1 | 2 | 3 | 2 | 2 | 3 | 1 | 2 | 2 | NA | NA | 1 | 2 | 4 | 4 | 2 | 2 | 3 | 2 | 1 | 1 | 1 | 3 | 2 | 2 | 1 | 2 | NA | 2 | 3 | 1 | 3 | 1 | 0.3358435 |
| 100260 | 1 | 3 | 3 | 1 | 4 | 1 | 2 | 2 | 1 | 3 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | NA | NA | 1 | 4 | 9 | 4 | 1 | 1 | 5 | 2 | 1 | 1 | 1 | 6 | 12 | NA | 5 | 1 | NA | 1 | 6 | 1 | 1 | 2 | 0.6853309 |
| 100314 | 1 | 3 | 3 | 1 | 4 | 1 | 2 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 4 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 4 | 1 | 2 | 3 | 2 | 1 | 1 | 1 | 2 | 1 | 2 | 5 | 1 | NA | 1 | 4 | 1 | 3 | 1 | 0.4115706 |
| 100363 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 2 | 2 | 3 | 4 | 2 | 1 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 4 | 2 | NA | 2 | 9 | 1 | 3 | 1 | 0.4192119 |
| 100446 | 4 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 3 | 3 | 1 | 3 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 3 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 3 | 2 | 1 | 6 | 2 | 3 | 5 | 1 | 6 | 11 | NA | 5 | 2 | NA | 2 | 8 | 1 | 4 | 1 | 0.4075220 |
| 100637 | 2 | 4 | 4 | 4 | 4 | 3 | 2 | 3 | 2 | 3 | NA | 3 | 2 | 3 | 4 | 4 | 4 | 2 | 2 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 3 | 2 | 1 | 2 | 2 | 1 | 3 | 5 | 3 | 2 | 2 | 4 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | 4 | 1 | 2 | 2 | 0.7107867 |
| 100803 | 3 | 1 | 1 | 1 | 3 | 1 | 1 | 3 | 1 | 3 | 1 | 2 | 1 | 2 | 3 | 4 | 4 | 1 | 1 | 2 | 3 | 2 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 4 | 3 | 2 | 1 | 5 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | NA | 1 | 9 | 1 | 2 | 2 | 0.3617818 |
| 101400 | 4 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | NA | 1 | 1 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | NA | NA | 1 | 2 | 4 | 3 | 2 | 1 | 5 | 2 | 1 | 1 | 1 | 6 | 9 | NA | 6 | 2 | NA | 2 | 6 | 1 | 4 | NA | 0.4702400 |
| 101437 | 1 | 2 | 2 | 2 | 3 | 2 | 3 | 3 | 1 | 3 | NA | 2 | 3 | 1 | 2 | 2 | 3 | 2 | 2 | 3 | 4 | 2 | 1 | 2 | 2 | 3 | 1 | 2 | 2 | NA | NA | 2 | 2 | 4 | 3 | 2 | 2 | 3 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 3 | 1 | 1 | 2 | 1 | 2 | 2 | 0.9032605 |
| 101472 | 4 | 3 | 3 | 2 | 3 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 3 | 3 | 3 | 4 | 2 | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 3 | 7 | 2 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 3 | 1 | 1 | 7 | 3 | 3 | 2 | 3.5604798 |
| 101493 | 1 | 4 | 4 | 4 | 3 | 1 | 2 | 2 | 1 | 1 | NA | 3 | 1 | 4 | 4 | 4 | 4 | 1 | 1 | NA | 1 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 4 | 9 | 3 | 1 | 2 | 3 | 1 | 1 | 3 | 1 | 1 | 10 | NA | 6 | 1 | NA | 1 | 6 | 1 | 2 | 1 | 0.7702067 |
| 102009 | 4 | 3 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 3 | 2 | 1 | 4 | 3 | 3 | 3 | 3 | 2 | 1 | 2 | 2 | 3 | 1 | 1 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 3 | 7 | 4 | 1 | 2 | 3 | 2 | 1 | 1 | 1 | 1 | 10 | NA | 6 | 2 | NA | 2 | 9 | 1 | 4 | 1 | 0.2650663 |
| 102932 | 4 | 4 | 3 | 2 | 2 | 3 | 1 | 1 | 1 | 3 | NA | 3 | 2 | 4 | 3 | 4 | 4 | 3 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 3 | NA | 2 | 2 | NA | NA | 2 | 3 | 7 | 4 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 12 | NA | 6 | 2 | NA | 2 | 9 | 1 | 5 | 1 | 0.1953622 |
| 103060 | 4 | 3 | 2 | 3 | 2 | 1 | 1 | 1 | 1 | 3 | NA | 2 | 1 | 4 | 4 | 4 | 3 | 3 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 3 | 5 | 4 | 2 | 2 | 4 | 2 | 1 | 1 | 1 | 1 | 12 | NA | 6 | 2 | NA | 2 | NA | 1 | 3 | 1 | 0.7646925 |
| 103379 | 1 | 2 | 4 | 2 | 4 | 2 | 1 | 1 | 1 | 2 | NA | 2 | 1 | 1 | 2 | 3 | 4 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 1 | 3 | 5 | 4 | 1 | 2 | 3 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | NA | 1 | 9 | 1 | 2 | 1 | 0.4511522 |
| 103406 | 4 | 4 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 2 | 3 | 2 | 1 | 3 | 3 | 4 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 5 | 2 | 2 | 2 | 2 | 1 | 3 | 5 | 2 | 2 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 3 | 2 | NA | 2 | 6 | 1 | 4 | 2 | 0.3455775 |
| 103519 | 4 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | NA | 2 | 1 | 2 | 2 | 3 | 1 | 2 | 1 | 1 | 1 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 3 | 3 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 3 | 3 | 2 | 2 | 9 | 1 | 3 | 1 | 0.2417055 |
| 103538 | 4 | 2 | 2 | 3 | 2 | 2 | 1 | 2 | 1 | 2 | NA | 2 | 1 | 3 | 3 | 3 | 3 | 2 | 2 | 3 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 4 | 8 | 3 | 1 | 1 | 5 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 3 | 2 | 2 | 9 | 1 | 3 | 2 | 0.5596834 |
| 103677 | 1 | 4 | 2 | 3 | 4 | 2 | 3 | 2 | 1 | 1 | NA | 2 | 3 | 2 | 2 | 3 | 4 | 2 | 3 | 3 | 1 | 2 | 1 | 1 | 1 | 3 | 4 | 2 | 1 | 2 | 1 | 1 | 1 | 2 | 2 | 1 | 2 | 4 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 4 | 4 | 1 | 1 | 9 | 1 | 1 | 1 | 0.8002977 |
| 103956 | 4 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | NA | 1 | 1 | 4 | 4 | 3 | 3 | 3 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 3 | 4 | 2 | 1 | 5 | 2 | 1 | 1 | 1 | 2 | 11 | NA | 5 | 2 | NA | 2 | 4 | 1 | 4 | 1 | 0.6997906 |
| 104210 | 4 | 2 | 2 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 4 | 2 | 4 | 4 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 3 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 3 | 2 | 2 | 1 | 1 | 1 | 2 | 12 | NA | 6 | 4 | 2 | 2 | 2 | 1 | 5 | 3 | 2.1818386 |
| 104272 | 4 | 2 | 2 | 2 | 3 | 1 | 1 | 3 | 1 | 3 | 1 | 2 | 1 | 3 | 3 | 4 | 2 | 1 | 1 | 3 | 2 | 2 | 2 | 1 | 1 | 1 | 4 | 2 | 2 | 2 | 1 | 1 | 2 | 3 | 2 | 1 | 2 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 2 | NA | 2 | 8 | 1 | 4 | 3 | 0.5020843 |
| 104368 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 3 | 3 | 3 | 3 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 3 | 1 | 1 | 1 | NA | NA | 1 | 3 | 7 | 4 | 1 | 3 | 2 | 2 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 3 | 1 | 1 | 3 | 1 | 2 | 1 | 0.9652520 |
| 104417 | 4 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 3 | 2 | 1 | 1 | 4 | 3 | 3 | 2 | 3 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 4 | 4 | 2 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 2 | NA | 2 | 8 | 1 | 4 | 1 | 0.3182606 |
| 104491 | 4 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 3 | NA | 2 | 1 | 4 | 2 | 1 | 3 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 3 | 1 | 2 | 2 | NA | NA | 1 | 4 | 9 | 4 | 2 | 2 | 3 | 2 | 1 | 1 | 1 | 1 | 12 | NA | 6 | 2 | NA | 2 | 6 | 1 | 5 | 2 | 0.8621568 |
| 104689 | 4 | 1 | 1 | 2 | 2 | 1 | 1 | 3 | 1 | 2 | NA | 1 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 3 | 4 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 4 | 2 | 3 | 2 | 2 | 1 | 1 | 1 | 5 | 1 | 2 | 2 | 2 | NA | 2 | 3 | 1 | 3 | 1 | 0.9241096 |
| 104727 | 2 | 3 | 2 | 2 | 4 | 2 | 2 | 2 | 1 | 1 | NA | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 3 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 3 | 3 | 1 | 3 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 1 | NA | 1 | 8 | 1 | 3 | 3 | 4.2711581 |
| 104937 | 4 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 2 | 3 | 1 | 1 | 3 | 3 | 3 | 2 | 2 | 3 | 2 | 1 | 2 | 2 | 1 | 2 | 3 | 1 | 2 | 2 | NA | NA | 1 | 3 | 5 | 4 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 3 | 12 | NA | 6 | 2 | NA | 2 | 8 | 1 | 3 | 1 | 0.4087059 |
| 105124 | 1 | 2 | 3 | 1 | 4 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 3 | 4 | 4 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 3 | 5 | 3 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | NA | 1 | 9 | 1 | 1 | 1 | 0.2966438 |
| 105205 | 1 | 4 | 1 | 3 | 4 | 1 | 2 | 3 | 1 | 1 | 3 | 2 | 2 | 2 | 2 | 2 | 4 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 3 | 3 | 2 | 2 | 4 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | NA | 1 | 8 | 1 | 2 | 2 | 0.6415607 |
| 105269 | 4 | 3 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | NA | 2 | 1 | 4 | 3 | 4 | 4 | 2 | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 4 | 4 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 3 | 3 | 2 | 2 | 6 | 1 | 3 | 1 | 0.2538791 |
| 105959 | 4 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | 4 | 3 | 3 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 4 | 9 | 3 | 2 | 1 | 6 | 2 | 1 | 1 | 1 | 2 | 2 | 2 | 5 | 2 | NA | 2 | 3 | 1 | 4 | 2 | 0.2506579 |
| 106068 | 4 | 4 | 4 | 3 | 3 | 2 | 1 | 2 | 1 | 1 | NA | 2 | 1 | 4 | 4 | 4 | 3 | 2 | 3 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 3 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 4 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 9 | NA | 6 | 2 | NA | 2 | 8 | 1 | 5 | 1 | 0.3323327 |
| 106180 | 2 | 2 | 2 | 4 | 4 | 1 | 2 | 3 | 1 | 2 | 1 | 3 | 3 | 2 | 2 | 2 | 4 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 1 | 4 | 2 | 2 | 2 | 2 | 1 | 3 | 7 | 3 | 1 | 2 | 4 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | NA | 1 | 6 | 1 | 1 | 2 | 0.6975281 |
| 106297 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 3 | 1 | 1 | 4 | 3 | 3 | 3 | 1 | 1 | 3 | 1 | 2 | 2 | 2 | 2 | 3 | 3 | 2 | 2 | 2 | 2 | 1 | 4 | 9 | 4 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 1 | NA | 2 | 2 | NA | 2 | NA | 1 | 3 | 2 | 0.1974483 |
| 106444 | 4 | 2 | 2 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | NA | 2 | 1 | 3 | 3 | 3 | 3 | 3 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 4 | 2 | 1 | 5 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 5 | 2 | NA | 2 | 8 | 1 | 4 | 1 | 0.4163360 |
| 106590 | 2 | 4 | 3 | 1 | 4 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 2 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 4 | 9 | 3 | 2 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 12 | NA | 5 | 3 | 1 | 1 | 8 | 1 | 3 | 2 | 0.3233775 |
| 106885 | 2 | 3 | 3 | 3 | 4 | 2 | 2 | 2 | 1 | 3 | NA | 2 | 1 | 2 | 2 | 3 | 4 | 2 | 2 | 2 | 4 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | 1 | 1 | 1 | 4 | 9 | 4 | 2 | 1 | 5 | 1 | 5 | 3 | 1 | 1 | 2 | 2 | 1 | 1 | NA | 1 | 9 | 1 | 1 | 1 | 0.3499207 |
| 106960 | 4 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 3 | 2 | 1 | 3 | 2 | 3 | NA | 2 | 2 | 1 | 1 | 2 | 3 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 3 | 4 | 2 | 2 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | NA | 2 | 5 | 1 | 3 | 2 | 0.7213331 |
| 107017 | 2 | 2 | 2 | 2 | 3 | 1 | 1 | 2 | 1 | 1 | NA | 2 | 1 | 2 | 2 | 3 | 3 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | NA | NA | 2 | 3 | 5 | 3 | 2 | 1 | 5 | 2 | 1 | 1 | 1 | 4 | 1 | 1 | 4 | 1 | NA | 1 | 7 | 1 | 3 | NA | 0.3550290 |
| 107023 | 4 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 3 | 1 | 3 | 2 | 1 | 2 | 3 | 3 | 1 | 2 | 2 | 3 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 3 | 5 | 3 | 2 | 2 | 4 | 2 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | 4 | 2 | 2 | 4 | 1 | 4 | 1 | 0.4072998 |
| 107329 | 4 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | NA | 2 | 1 | 4 | 3 | 2 | 2 | 3 | 3 | 1 | 3 | 2 | 1 | 1 | NA | 2 | 1 | 2 | 1 | NA | NA | 1 | 1 | 2 | 3 | 2 | 1 | 5 | 2 | 2 | 2 | 1 | 6 | 2 | 2 | 5 | 2 | NA | 2 | 5 | 1 | 5 | 1 | 0.7760014 |
| 107612 | 4 | 3 | 4 | 3 | 2 | 3 | 1 | 1 | 1 | 2 | NA | 4 | 2 | 4 | NA | NA | 4 | 4 | 4 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | NA | NA | 1 | 3 | 5 | 4 | 2 | 2 | 3 | 2 | 2 | 2 | 1 | 3 | 1 | 2 | 6 | 2 | NA | 2 | 6 | 1 | 4 | 1 | 0.5494154 |
| 107668 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | NA | 2 | 1 | 3 | 3 | 4 | 2 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | NA | NA | 2 | 3 | 7 | 3 | 2 | 1 | 5 | 2 | 1 | 1 | 1 | 6 | 1 | 2 | 6 | 1 | NA | 1 | 6 | 1 | 3 | 1 | 0.5397227 |
| 108169 | 4 | 1 | 3 | 3 | 3 | 2 | 1 | 1 | 1 | 1 | NA | 2 | 1 | 4 | 4 | 4 | 3 | 2 | 3 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 5 | 2 | 2 | 2 | 2 | 1 | 2 | 3 | 2 | 2 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 12 | NA | 6 | 2 | NA | 2 | 6 | 1 | 4 | 1 | 0.3397435 |
| 108348 | 2 | 2 | 2 | 2 | 4 | 2 | 2 | 3 | 1 | 3 | 2 | 3 | 2 | 2 | 2 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | NA | NA | 1 | 4 | 8 | 3 | 2 | 2 | 3 | 2 | 1 | 1 | 1 | 6 | 1 | 1 | 2 | 3 | 1 | 1 | 5 | 1 | 2 | 2 | 0.8002264 |
| 108435 | 2 | 2 | 2 | 3 | 4 | 3 | 1 | 2 | 1 | 2 | NA | 3 | 2 | 2 | 2 | 3 | 4 | 2 | 2 | 2 | 2 | 2 | 3 | 2 | 1 | 3 | 1 | 2 | 2 | NA | NA | 1 | 2 | 3 | 3 | 2 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 4 | 1 | NA | 1 | 9 | 1 | 2 | 1 | 0.7349014 |
| 108592 | 4 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | NA | 3 | 3 | 1 | 2 | 1 | 2 | 1 | 2 | 3 | 4 | 2 | 2 | 4 | 2 | 2 | 2 | 1 | 3 | 1 | 2 | 2 | 2 | NA | 2 | 4 | 1 | 3 | 1 | 0.4113958 |
| 108909 | 3 | 3 | 3 | 3 | 2 | 3 | 1 | 1 | 1 | 1 | 3 | 4 | 3 | 2 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 4 | 2 | 2 | 2 | 2 | 1 | 2 | 3 | 3 | 2 | 3 | 1 | 2 | 1 | 1 | 1 | 5 | 2 | 2 | 4 | 2 | NA | 2 | 2 | 3 | 4 | 3 | 1.6395016 |
| 109143 | 1 | 1 | 2 | 1 | 3 | 1 | 2 | 3 | 1 | 2 | NA | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 3 | 1 | 2 | 2 | 2 | 1 | 3 | 5 | 2 | 2 | 3 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 5 | 1 | NA | 1 | 6 | 1 | 3 | 2 | 0.7484475 |
Examine the data
[1] "ID" "covid_trump" "covid_state" "covid_local"
[5] "covid_pubhealth" "covid_media" "covid_doctors" "cov_threat_us"
[9] "cov_threat_me" "cov_threat_econ" "cov_threat_fin" "cov_evidence"
[13] "cov_understand" "soc_distance" "covid_usa" "covid_uk"
[17] "covid_italy" "covid_china" "covid_vaccine" "covid_drug"
[21] "covid_outbreak" "vaccinate" "diagnose" "vulnerable"
[25] "know_covid" "know_dead" "employ" "household"
[29] "fired" "paycut" "fired_house" "paycut_house"
[33] "metro" "region" "division" "age_cat"
[37] "sex" "edu_cat" "edu" "hispanic"
[41] "race" "race_ethn" "citizen" "marital"
[45] "religion" "evang" "attendance" "party"
[49] "party_lean" "party_comb" "income" "registered"
[53] "ideology" "covid_news" "weight" [1] 10642 55What type does each of the survey variables have in R? read.csv() chooses it based on the data it finds in each column. Using ‘sapply()’ as bellow will take our data and apply the same function to each variable/column in it, then combine the results (when possible):
ID covid_trump covid_state covid_local covid_pubhealth
"numeric" "integer" "integer" "integer" "integer"
covid_media covid_doctors cov_threat_us cov_threat_me cov_threat_econ
"integer" "integer" "integer" "integer" "integer"
cov_threat_fin cov_evidence cov_understand soc_distance covid_usa
"integer" "integer" "integer" "integer" "integer"
covid_uk covid_italy covid_china covid_vaccine covid_drug
"integer" "integer" "integer" "integer" "integer"
covid_outbreak vaccinate diagnose vulnerable know_covid
"integer" "integer" "integer" "integer" "integer"
know_dead employ household fired paycut
"integer" "integer" "integer" "integer" "integer"
fired_house paycut_house metro region division
"integer" "integer" "integer" "integer" "integer"
age_cat sex edu_cat edu hispanic
"integer" "integer" "integer" "integer" "integer"
race race_ethn citizen marital religion
"integer" "integer" "integer" "integer" "integer"
evang attendance party party_lean party_comb
"integer" "integer" "integer" "integer" "integer"
income registered ideology covid_news weight
"integer" "integer" "integer" "integer" "numeric" ID covid_trump covid_state covid_local covid_pubhealth
TRUE TRUE TRUE TRUE TRUE
covid_media covid_doctors cov_threat_us cov_threat_me cov_threat_econ
TRUE TRUE TRUE TRUE TRUE
cov_threat_fin cov_evidence cov_understand soc_distance covid_usa
TRUE TRUE TRUE TRUE TRUE
covid_uk covid_italy covid_china covid_vaccine covid_drug
TRUE TRUE TRUE TRUE TRUE
covid_outbreak vaccinate diagnose vulnerable know_covid
TRUE TRUE TRUE TRUE TRUE
know_dead employ household fired paycut
TRUE TRUE TRUE TRUE TRUE
fired_house paycut_house metro region division
TRUE TRUE TRUE TRUE TRUE
age_cat sex edu_cat edu hispanic
TRUE TRUE TRUE TRUE TRUE
race race_ethn citizen marital religion
TRUE TRUE TRUE TRUE TRUE
evang attendance party party_lean party_comb
TRUE TRUE TRUE TRUE TRUE
income registered ideology covid_news weight
TRUE TRUE TRUE TRUE TRUE Remember that: ‘integer’ is used for whole numbers, ‘numeric’ is used for numbers with a decimal point ‘character’ is used for text
ID covid_trump covid_state covid_local covid_pubhealth
FALSE TRUE TRUE TRUE TRUE
covid_media covid_doctors cov_threat_us cov_threat_me cov_threat_econ
TRUE TRUE TRUE TRUE TRUE
cov_threat_fin cov_evidence cov_understand soc_distance covid_usa
TRUE TRUE TRUE TRUE TRUE
covid_uk covid_italy covid_china covid_vaccine covid_drug
TRUE TRUE TRUE TRUE TRUE
covid_outbreak vaccinate diagnose vulnerable know_covid
TRUE TRUE TRUE TRUE TRUE
know_dead employ household fired paycut
TRUE TRUE TRUE TRUE TRUE
fired_house paycut_house metro region division
TRUE TRUE TRUE TRUE TRUE
age_cat sex edu_cat edu hispanic
TRUE TRUE TRUE TRUE TRUE
race race_ethn citizen marital religion
TRUE TRUE TRUE TRUE TRUE
evang attendance party party_lean party_comb
TRUE TRUE TRUE TRUE TRUE
income registered ideology covid_news weight
TRUE TRUE TRUE TRUE FALSE ID covid_trump covid_state covid_local covid_pubhealth
FALSE TRUE TRUE TRUE TRUE
covid_media covid_doctors cov_threat_us cov_threat_me cov_threat_econ
TRUE TRUE TRUE TRUE TRUE
cov_threat_fin cov_evidence cov_understand soc_distance covid_usa
TRUE TRUE TRUE TRUE TRUE
covid_uk covid_italy covid_china covid_vaccine covid_drug
TRUE TRUE TRUE TRUE TRUE
covid_outbreak vaccinate diagnose vulnerable know_covid
TRUE TRUE TRUE TRUE TRUE
know_dead employ household fired paycut
TRUE TRUE TRUE TRUE TRUE
fired_house paycut_house metro region division
TRUE TRUE TRUE TRUE TRUE
age_cat sex edu_cat edu hispanic
TRUE TRUE TRUE TRUE TRUE
race race_ethn citizen marital religion
TRUE TRUE TRUE TRUE TRUE
evang attendance party party_lean party_comb
TRUE TRUE TRUE TRUE TRUE
income registered ideology covid_news weight
TRUE TRUE TRUE TRUE FALSE Change all the integer columns to numeric (some of the functions we use below don’t like integer data) dat[, integer_columns] <- sapply(dat[, integer_columns], as.numeric)
ADDITIONAL INFORMATION
Note that we could also easily read the data from SPSS format. One
way to do that is by using the package ‘haven’, like so:
install.packages(“haven”)
library(haven) dat <-
read_sav(“DATA/pew_data.sav”)
Data descriptives
Some variables from our data include * sex: 1 Male, 2 Female *
age_cat: 1 Age 18-29, 2 Age 30-49, 3 Age 50-64, 4 Age 65+ * race_ethn: 1
White 2 Black 3 Hispanic 4 Other 5 Asian * party: 1 Republican, 2
Democrat, 3 Independent, 4 Other
* edu: 1 Less than high school, 2 High school graduate, 3 Some college,
no degree 4 Associate degree, 5 College graduate/some post-grad, 6
Postgraduate degree * income: family income, 9 categories from under 1
10K to 9 over $150K * ideology: ideology, 5 categories from 1 Very
conservative to 5 Very liberal * attend: religious service attendance, 1
more than once a week to 6 never
* covid_news: following COVID news, 1 very closely to 4 not at all
closely * covid_trump: approval of Trump’s COVID-19 response, 1
excellent to 4 poor
Let’s examine our variables.
In base R, we can get frequencies like so:
1 2
4772 5870
1 2
0.448412 0.551588 There are, however, more convenient ways to do this! There is a package for everything in R:
Load the follwoing library
Get Frequency Tables for the Categorical Variables
Frequencies
dat$sex
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 4772 44.84 44.84 44.84 44.84
2 5870 55.16 100.00 55.16 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$age_cat
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 1225 11.51 11.51 11.51 11.51
2 3489 32.79 44.30 32.79 44.30
3 3193 30.00 74.30 30.00 74.30
4 2735 25.70 100.00 25.70 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$race_ethn
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 7116 66.87 66.87 66.87 66.87
2 884 8.31 75.17 8.31 75.17
3 2034 19.11 94.29 19.11 94.29
4 300 2.82 97.11 2.82 97.11
5 308 2.89 100.00 2.89 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$region
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 1703 16.00 16.00 16.00 16.00
2 2224 20.90 36.90 20.90 36.90
3 4378 41.14 78.04 41.14 78.04
4 2337 21.96 100.00 21.96 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$party
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 2625 24.91 24.91 24.67 24.67
2 3805 36.10 61.01 35.75 60.42
3 3115 29.56 90.57 29.27 89.69
4 994 9.43 100.00 9.34 99.03
<NA> 103 0.97 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$edu
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 322 3.03 3.03 3.03 3.03
2 1300 12.24 15.27 12.22 15.24
3 2154 20.27 35.54 20.24 35.48
4 1037 9.76 45.30 9.74 45.23
5 3103 29.21 74.51 29.16 74.38
6 2708 25.49 100.00 25.45 99.83
<NA> 18 0.17 100.00
Total 10642 100.00 100.00 100.00 100.00Additional Frequency Tables
Frequencies
dat$income
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 493 4.81 4.81 4.63 4.63
2 689 6.72 11.53 6.47 11.11
3 840 8.19 19.72 7.89 19.00
4 843 8.22 27.94 7.92 26.92
5 893 8.71 36.65 8.39 35.31
6 1790 17.46 54.11 16.82 52.13
7 1517 14.79 68.90 14.25 66.39
8 1701 16.59 85.49 15.98 82.37
9 1488 14.51 100.00 13.98 96.35
<NA> 388 3.65 100.00
Total 10642 100.00 100.00 100.00 100.00Descriptive Statistics
dat$income
N: 10642
income
----------------- ----------
Mean 5.91
Std.Dev 2.35
Min 1.00
Q1 4.00
Median 6.00
Q3 8.00
Max 9.00
MAD 2.97
IQR 4.00
CV 0.40
Skewness -0.49
SE.Skewness 0.02
Kurtosis -0.81
N.Valid 10254.00
Pct.Valid 96.35Frequency Table for Ideology
Frequencies
dat$ideology
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 799 7.63 7.63 7.51 7.51
2 2415 23.07 30.70 22.69 30.20
3 3995 38.16 68.86 37.54 67.74
4 2216 21.17 90.03 20.82 88.56
5 1044 9.97 100.00 9.81 98.37
<NA> 173 1.63 100.00
Total 10642 100.00 100.00 100.00 100.00Descriptive Statistics
dat$ideology
N: 10642
ideology
----------------- ----------
Mean 3.03
Std.Dev 1.07
Min 1.00
Q1 2.00
Median 3.00
Q3 4.00
Max 5.00
MAD 1.48
IQR 2.00
CV 0.35
Skewness 0.06
SE.Skewness 0.02
Kurtosis -0.53
N.Valid 10469.00
Pct.Valid 98.37Frequency Table for Religious Service Attendance
Frequencies
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 798 7.52 7.52 7.50 7.50
2 2076 19.55 27.07 19.51 27.01
3 882 8.31 35.37 8.29 35.29
4 1724 16.24 51.61 16.20 51.49
5 2478 23.34 74.95 23.29 74.78
6 2660 25.05 100.00 25.00 99.77
<NA> 24 0.23 100.00
Total 10642 100.00 100.00 100.00 100.00Descriptive Statistics
dat$value
N: 10642
value
----------------- ----------
Mean 4.03
Std.Dev 1.67
Min 1.00
Q1 2.00
Median 4.00
Q3 6.00
Max 6.00
MAD 2.97
IQR 4.00
CV 0.41
Skewness -0.37
SE.Skewness 0.02
Kurtosis -1.21
N.Valid 10618.00
Pct.Valid 99.77Frequency Table for Following Covid-19 News (1= closely, 4 = Not at all)
Frequencies
dat$covid_news
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 4836 53.58 53.58 45.44 45.44
2 3456 38.29 91.88 32.48 77.92
3 627 6.95 98.83 5.89 83.81
4 106 1.17 100.00 1.00 84.81
<NA> 1617 15.19 100.00
Total 10642 100.00 100.00 100.00 100.00Descriptive Statistics
dat$covid_news
N: 10642
covid_news
----------------- ------------
Mean 1.56
Std.Dev 0.68
Min 1.00
Q1 1.00
Median 1.00
Q3 2.00
Max 4.00
MAD 0.00
IQR 1.00
CV 0.43
Skewness 1.04
SE.Skewness 0.03
Kurtosis 0.78
N.Valid 9025.00
Pct.Valid 84.81Frequency Tables for the approval of Trump’s COVID-19 response, 1 excellent to 4 poor
Frequencies
dat$covid_trump
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 1852 17.48 17.48 17.40 17.40
2 2011 18.98 36.46 18.90 36.30
3 1510 14.25 50.71 14.19 50.49
4 5222 49.29 100.00 49.07 99.56
<NA> 47 0.44 100.00
Total 10642 100.00 100.00 100.00 100.00Descriptive Statistics
dat$covid_trump
N: 10642
covid_trump
----------------- -------------
Mean 2.95
Std.Dev 1.17
Min 1.00
Q1 2.00
Median 3.00
Q3 4.00
Max 4.00
MAD 1.48
IQR 2.00
CV 0.40
Skewness -0.56
SE.Skewness 0.02
Kurtosis -1.27
N.Valid 10595.00
Pct.Valid 99.56We can also examine crosstabs!
1 2 3 4
1 166 742 890 827
2 497 1231 1102 975
3 357 1051 934 773
4 193 433 237 131Cross-Tabulation, Row Proportions
factor(dat$party) * factor(dat$age_cat)
------------------- --------------------- -------------- -------------- -------------- -------------- ----------------
factor(dat$age_cat) 1 2 3 4 Total
factor(dat$party)
1 166 ( 6.3%) 742 (28.3%) 890 (33.9%) 827 (31.5%) 2625 (100.0%)
2 497 (13.1%) 1231 (32.4%) 1102 (29.0%) 975 (25.6%) 3805 (100.0%)
3 357 (11.5%) 1051 (33.7%) 934 (30.0%) 773 (24.8%) 3115 (100.0%)
4 193 (19.4%) 433 (43.6%) 237 (23.8%) 131 (13.2%) 994 (100.0%)
<NA> 12 (11.7%) 32 (31.1%) 30 (29.1%) 29 (28.2%) 103 (100.0%)
Total 1225 (11.5%) 3489 (32.8%) 3193 (30.0%) 2735 (25.7%) 10642 (100.0%)
------------------- --------------------- -------------- -------------- -------------- -------------- ----------------Or, the prettier format of “summarytools” package:
Cross-Tabulation, Row Proportions
factor(dat$party) * factor(dat$age_cat)
------------------- --------------------- -------------- -------------- -------------- -------------- ----------------
factor(dat$age_cat) 1 2 3 4 Total
factor(dat$party)
1 166 ( 6.3%) 742 (28.3%) 890 (33.9%) 827 (31.5%) 2625 (100.0%)
2 497 (13.1%) 1231 (32.4%) 1102 (29.0%) 975 (25.6%) 3805 (100.0%)
3 357 (11.5%) 1051 (33.7%) 934 (30.0%) 773 (24.8%) 3115 (100.0%)
4 193 (19.4%) 433 (43.6%) 237 (23.8%) 131 (13.2%) 994 (100.0%)
<NA> 12 (11.7%) 32 (31.1%) 30 (29.1%) 29 (28.2%) 103 (100.0%)
Total 1225 (11.5%) 3489 (32.8%) 3193 (30.0%) 2735 (25.7%) 10642 (100.0%)
------------------- --------------------- -------------- -------------- -------------- -------------- ----------------Note that we enclose the variables in factor() to tell R that they should be treated as categorical and not numbers.
Data recoding
Let’s recode some variables to make them more convenient to use:
Frequencies
dat$sex_r
Type: Character
Freq % Valid % Valid Cum. % Total % Total Cum.
------------ ------- --------- -------------- --------- --------------
Female 5870 55.16 55.16 55.16 55.16
Male 4772 44.84 100.00 44.84 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Notice what is happening here. First, we are creating a 2-element vector with c(“Male”,“Female”). Then, we use dat\(sex to index the 2-element vector we created. c("Male", "Female")[dat\)sex] means:
Create Lables for Ages
Frequencies
dat$age_r
Type: Character
Freq % Valid % Valid Cum. % Total % Total Cum.
--------------- ------- --------- -------------- --------- --------------
Age 18-29 1225 11.51 11.51 11.51 11.51
Age 30-49 3489 32.79 44.30 32.79 44.30
Age 50-64 3193 30.00 74.30 30.00 74.30
Age 65+ 2735 25.70 100.00 25.70 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Create Lables for Races
Frequencies
dat$race_r
Type: Character
Freq % Valid % Valid Cum. % Total % Total Cum.
-------------- ------- --------- -------------- --------- --------------
Asian 308 2.89 2.89 2.89 2.89
Black 884 8.31 11.20 8.31 11.20
Hispanic 2034 19.11 30.31 19.11 30.31
Other 300 2.82 33.13 2.82 33.13
White 7116 66.87 100.00 66.87 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Create Label for Part
Frequencies
dat$party_r
Type: Character
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
Dem 3805 36.10 36.10 35.75 35.75
Ind 3115 29.56 65.66 29.27 65.03
Oth 994 9.43 75.09 9.34 74.37
Rep 2625 24.91 100.00 24.67 99.03
<NA> 103 0.97 100.00
Total 10642 100.00 100.00 100.00 100.00What if we wanted to combine “Independents” and “Other” in the party variable? Well, we can just make element 3 and 4 below to be the same thing!
Create lables for covid_us
Frequencies
dat$covid_usa_r_f
Type: Character
Freq % Valid % Valid Cum. % Total % Total Cum.
--------------- ------- --------- -------------- --------- --------------
Excellent 905 8.58 8.58 8.50 8.50
Good 3659 34.67 43.25 34.38 42.89
Only Fair 3531 33.46 76.71 33.18 76.07
Poor 2458 23.29 100.00 23.10 99.16
<NA> 89 0.84 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$party_r2
Type: Character
Freq % Valid % Valid Cum. % Total % Total Cum.
------------- ------- --------- -------------- --------- --------------
Dem 3805 36.10 36.10 35.75 35.75
Ind/Oth 4109 38.99 75.09 38.61 74.37
Rep 2625 24.91 100.00 24.67 99.03
<NA> 103 0.97 100.00
Total 10642 100.00 100.00 100.00 100.00Data crosstabs are now easier to understand: For each party, what % are in certain age groups?
Cross-Tabulation, Row Proportions
party_r * age_r
Data Frame: dat
--------- ------- -------------- -------------- -------------- -------------- ----------------
age_r Age 18-29 Age 30-49 Age 50-64 Age 65+ Total
party_r
Dem 497 (13.1%) 1231 (32.4%) 1102 (29.0%) 975 (25.6%) 3805 (100.0%)
Ind 357 (11.5%) 1051 (33.7%) 934 (30.0%) 773 (24.8%) 3115 (100.0%)
Oth 193 (19.4%) 433 (43.6%) 237 (23.8%) 131 (13.2%) 994 (100.0%)
Rep 166 ( 6.3%) 742 (28.3%) 890 (33.9%) 827 (31.5%) 2625 (100.0%)
<NA> 12 (11.7%) 32 (31.1%) 30 (29.1%) 29 (28.2%) 103 (100.0%)
Total 1225 (11.5%) 3489 (32.8%) 3193 (30.0%) 2735 (25.7%) 10642 (100.0%)
--------- ------- -------------- -------------- -------------- -------------- ----------------For each age group, what % are in a particular party?
Cross-Tabulation, Row Proportions
age_r * party_r
Data Frame: dat
----------- --------- -------------- -------------- ------------- -------------- ------------ ----------------
party_r Dem Ind Oth Rep <NA> Total
age_r
Age 18-29 497 (40.6%) 357 (29.1%) 193 (15.8%) 166 (13.6%) 12 (1.0%) 1225 (100.0%)
Age 30-49 1231 (35.3%) 1051 (30.1%) 433 (12.4%) 742 (21.3%) 32 (0.9%) 3489 (100.0%)
Age 50-64 1102 (34.5%) 934 (29.3%) 237 ( 7.4%) 890 (27.9%) 30 (0.9%) 3193 (100.0%)
Age 65+ 975 (35.6%) 773 (28.3%) 131 ( 4.8%) 827 (30.2%) 29 (1.1%) 2735 (100.0%)
Total 3805 (35.8%) 3115 (29.3%) 994 ( 9.3%) 2625 (24.7%) 103 (1.0%) 10642 (100.0%)
----------- --------- -------------- -------------- ------------- -------------- ------------ ----------------Some numerical variables in the data could also use recoding. For example, covid_news ranges 1 (follow closely) to 4 (not at all closely) We usually want our variables coded such that higher means more. So here, we’d like to reverse-code:
Frequencies
dat$covid_news
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 4836 53.58 53.58 45.44 45.44
2 3456 38.29 91.88 32.48 77.92
3 627 6.95 98.83 5.89 83.81
4 106 1.17 100.00 1.00 84.81
<NA> 1617 15.19 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$covid_news_r
Type: Numeric
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 106 1.17 1.17 1.00 1.00
2 627 6.95 8.12 5.89 6.89
3 3456 38.29 46.42 32.48 39.36
4 4836 53.58 100.00 45.44 84.81
<NA> 1617 15.19 100.00
Total 10642 100.00 100.00 100.00 100.00Similarly, the following variables represent approval of the COVID response for various actors. They range from 1 (excellent) to 4 (poor). Let’s reverse them so that 1 is poor and 4 is excellent.
Frequencies
dat$covid_trump
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 1852 17.48 17.48 17.40 17.40
2 2011 18.98 36.46 18.90 36.30
3 1510 14.25 50.71 14.19 50.49
4 5222 49.29 100.00 49.07 99.56
<NA> 47 0.44 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$covid_trump
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 1852 17.48 17.48 17.40 17.40
2 2011 18.98 36.46 18.90 36.30
3 1510 14.25 50.71 14.19 50.49
4 5222 49.29 100.00 49.07 99.56
<NA> 47 0.44 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$covid_usa
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 905 8.58 8.58 8.50 8.50
2 3659 34.67 43.25 34.38 42.89
3 3531 33.46 76.71 33.18 76.07
4 2458 23.29 100.00 23.10 99.16
<NA> 89 0.84 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$covid_usa_r
Type: Numeric
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 2458 23.29 23.29 23.10 23.10
2 3531 33.46 56.75 33.18 56.28
3 3659 34.67 91.42 34.38 90.66
4 905 8.58 100.00 8.50 99.16
<NA> 89 0.84 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$covid_china
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 645 6.21 6.21 6.06 6.06
2 2887 27.80 34.01 27.13 33.19
3 2998 28.87 62.89 28.17 61.36
4 3854 37.11 100.00 36.21 97.58
<NA> 258 2.42 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$covid_china_r
Type: Numeric
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 3854 37.11 37.11 36.21 36.21
2 2998 28.87 65.99 28.17 64.39
3 2887 27.80 93.79 27.13 91.51
4 645 6.21 100.00 6.06 97.58
<NA> 258 2.42 100.00
Total 10642 100.00 100.00 100.00 100.00Data analysis
Some basic tests we often perform with survey data…
Correlations:
Is people’s ideology associated with approval for Trump’s covid response?
Pearson's product-moment correlation
data: dat$ideology and dat$covid_trump
t = 81.826, df = 10434, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.6133723 0.6367493
sample estimates:
cor
0.625201 R normally uses scientific notation, e.g. 2.546e-15. If you are so inclined, you can ask it to stop doing that:
One-sample t-test to check if mean ideology is significantly different from 3.00:
One Sample t-test
data: dat$ideology
t = 2.6569, df = 10468, p-value = 0.007898
alternative hypothesis: true mean is not equal to 3
95 percent confidence interval:
3.007289 3.048304
sample estimates:
mean of x
3.027796 Paired samples t-test comparing mean approval for US and China’s COVID-19 response:
Paired t-test
data: dat$covid_usa_r and dat$covid_china_r
t = 18.317, df = 10367, p-value < 0.00000000000000022
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
0.2253128 0.2793169
sample estimates:
mean difference
0.2523148 Descriptive Statistics
dat$covid_usa_r
N: 10642
covid_usa_r
----------------- -------------
Mean 2.29
Std.Dev 0.92
Min 1.00
Q1 2.00
Median 2.00
Q3 3.00
Max 4.00
MAD 1.48
IQR 1.00
CV 0.40
Skewness 0.07
SE.Skewness 0.02
Kurtosis -0.92
N.Valid 10553.00
Pct.Valid 99.16Descriptive Statistics
dat$covid_china_r
N: 10642
covid_china_r
----------------- ---------------
Mean 2.03
Std.Dev 0.95
Min 1.00
Q1 1.00
Median 2.00
Q3 3.00
Max 4.00
MAD 1.48
IQR 2.00
CV 0.47
Skewness 0.38
SE.Skewness 0.02
Kurtosis -1.01
N.Valid 10384.00
Pct.Valid 97.58Independent samples t-test
Are men and women different in terms of political ideology?
[1] 2.938227[1] 3.101533
Welch Two Sample t-test
data: dat$ideology by dat$sex_r
t = 7.771, df = 9984.3, p-value = 0.000000000000008552
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
0.1221124 0.2044983
sample estimates:
mean in group Female mean in group Male
3.101533 2.938227 independent samples t-test – note the use of formula as its first parameter. Many statistical test functions in R can use a formula as a parameter. An object of class formula looks something like this: y ~ x1 + x2 where y is a dependent variable, while x1 and x2 are independent variables. Notice also that in a formula, we use just the names of the columns (without dat$) t.test(ideology ~ sex_r, data=dat)
Regressions in R similarly use formulas to specify the desired model: Here the predicted variable is approval of USA’s COVID-19 response, and the predictors include respondent sex, age, race, party, and ideology.
Dependent variable
Frequencies
dat$covid_usa_r
Type: Numeric
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 2458 23.29 23.29 23.10 23.10
2 3531 33.46 56.75 33.18 56.28
3 3659 34.67 91.42 34.38 90.66
4 905 8.58 100.00 8.50 99.16
<NA> 89 0.84 100.00
Total 10642 100.00 100.00 100.00 100.00Estimate the Model
# weights: 52 (36 variable)
initial value 13843.535490
iter 10 value 11449.006685
iter 20 value 11203.807885
iter 30 value 11028.658201
iter 40 value 10986.648850
final value 10984.029231
convergedCall:
multinom(formula = covid_usa_r ~ sex_r + age_cat + race_r + edu +
income + party_r2 + ideology, data = dat)
Coefficients:
(Intercept) sex_rMale age_cat race_rBlack race_rHispanic race_rOther
2 1.367193 -0.2689228 0.04005407 0.20251302 0.2064210 0.1319034
3 2.335754 -0.4541872 0.23009627 0.37462060 0.6499526 0.8361207
4 1.829208 -0.3230650 0.43169949 0.03390917 0.7299886 0.2685993
race_rWhite edu income party_r2Ind/Oth party_r2Rep ideology
2 0.4578814 -0.01398983 -0.007744406 0.1874743 0.8442158 -0.3927417
3 0.9041967 -0.10711432 -0.061217377 0.5242988 2.0492202 -0.9164768
4 0.4563683 -0.22176597 -0.105596632 0.5973720 2.6710984 -1.2810966
Residual Deviance: 21968.06
AIC: 22040.06 View the Results using tab_model() function
| covid_usa_r | ||||||
|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | SE | 95% CI | Statistic | p-value | Response |
| (Intercept) | 3.92 | 0.89 | 0.00 – Inf | 6.00 | <0.001 | 2 |
| sex r [Male] | 0.76 | 0.04 | 0.00 – Inf | -4.75 | <0.001 | 2 |
| age cat | 1.04 | 0.03 | 0.00 – Inf | 1.36 | 0.174 | 2 |
| race r [Black] | 1.22 | 0.20 | 0.00 – Inf | 1.24 | 0.217 | 2 |
| race r [Hispanic] | 1.23 | 0.19 | 0.00 – Inf | 1.36 | 0.173 | 2 |
| race r [Other] | 1.14 | 0.24 | 0.00 – Inf | 0.62 | 0.536 | 2 |
| race r [White] | 1.58 | 0.22 | 0.00 – Inf | 3.22 | 0.001 | 2 |
| edu | 0.99 | 0.02 | 0.00 – Inf | -0.63 | 0.526 | 2 |
| income | 0.99 | 0.01 | 0.00 – Inf | -0.57 | 0.568 | 2 |
| party r2 [Ind/Oth] | 1.21 | 0.07 | 0.00 – Inf | 3.03 | 0.002 | 2 |
| party r2 [Rep] | 2.33 | 0.30 | 0.00 – Inf | 6.55 | <0.001 | 2 |
| ideology | 0.68 | 0.02 | 0.00 – Inf | -11.70 | <0.001 | 2 |
| (Intercept) | 10.34 | 2.80 | 0.00 – Inf | 8.63 | <0.001 | 3 |
| sex r [Male] | 0.63 | 0.04 | 0.00 – Inf | -7.19 | <0.001 | 3 |
| age cat | 1.26 | 0.04 | 0.00 – Inf | 7.00 | <0.001 | 3 |
| race r [Black] | 1.45 | 0.31 | 0.00 – Inf | 1.77 | 0.077 | 3 |
| race r [Hispanic] | 1.92 | 0.38 | 0.00 – Inf | 3.31 | 0.001 | 3 |
| race r [Other] | 2.31 | 0.58 | 0.00 – Inf | 3.34 | 0.001 | 3 |
| race r [White] | 2.47 | 0.46 | 0.00 – Inf | 4.81 | <0.001 | 3 |
| edu | 0.90 | 0.02 | 0.00 – Inf | -4.52 | <0.001 | 3 |
| income | 0.94 | 0.01 | 0.00 – Inf | -4.10 | <0.001 | 3 |
| party r2 [Ind/Oth] | 1.69 | 0.12 | 0.00 – Inf | 7.30 | <0.001 | 3 |
| party r2 [Rep] | 7.76 | 0.99 | 0.00 – Inf | 16.06 | <0.001 | 3 |
| ideology | 0.40 | 0.02 | 0.00 – Inf | -23.92 | <0.001 | 3 |
| (Intercept) | 6.23 | 2.68 | 0.00 – Inf | 4.25 | <0.001 | 4 |
| sex r [Male] | 0.72 | 0.07 | 0.00 – Inf | -3.48 | 0.001 | 4 |
| age cat | 1.54 | 0.08 | 0.00 – Inf | 8.61 | <0.001 | 4 |
| race r [Black] | 1.03 | 0.38 | 0.00 – Inf | 0.09 | 0.927 | 4 |
| race r [Hispanic] | 2.08 | 0.68 | 0.00 – Inf | 2.24 | 0.025 | 4 |
| race r [Other] | 1.31 | 0.55 | 0.00 – Inf | 0.63 | 0.526 | 4 |
| race r [White] | 1.58 | 0.50 | 0.00 – Inf | 1.44 | 0.149 | 4 |
| edu | 0.80 | 0.03 | 0.00 – Inf | -6.58 | <0.001 | 4 |
| income | 0.90 | 0.02 | 0.00 – Inf | -4.82 | <0.001 | 4 |
| party r2 [Ind/Oth] | 1.82 | 0.25 | 0.00 – Inf | 4.31 | <0.001 | 4 |
| party r2 [Rep] | 14.46 | 2.50 | 0.00 – Inf | 15.47 | <0.001 | 4 |
| ideology | 0.28 | 0.02 | 0.00 – Inf | -21.96 | <0.001 | 4 |
| Observations | 9986 | |||||
| R2 / R2 adjusted | 0.189 / 0.189 | |||||
Interpretation
In this multinomial logistic regression model, the reference category is 1, and the responses 2, 3, and 4 indicate higher ratings for how well the U.S. handled the COVID-19 outbreak. Significant predictors include sex, age category, race, education, income, political party affiliation, and political ideology.
For Response 2, the odds ratios indicate that males (OR = 0.76, p < 0.001) are less likely to rate the U.S. response favorably compared to females. Whites (OR = 1.58, p = 0.001) and Republicans (OR = 2.33, p < 0.001) are more likely to rate the response favorably. The effect of age is not significant (OR = 1.04, p = 0.174). Education and income show no significant effects. The model suggests that political affiliation and race are strong determinants of perceptions of the U.S. response.
For Response 3, the odds ratios show a stronger effect of age (OR = 1.26, p < 0.001) and a significant effect of being male (OR = 0.63, p < 0.001). Hispanics (OR = 1.92, p = 0.001), and those identifying as “Other” race (OR = 2.31, p = 0.001) are more likely to rate the U.S. response favorably. Education and income negatively influence favorable ratings, and Republicans (OR = 7.76, p < 0.001) are significantly more likely to rate favorably. Ideology shows a substantial negative impact (OR = 0.40, p < 0.001).
For Response 4, age again shows a strong positive effect (OR = 1.54, p < 0.001), and males are less likely to rate favorably (OR = 0.72, p = 0.001). Hispanics (OR = 2.08, p = 0.025) are more likely to rate the response favorably, but the effect for Blacks is not significant. Education (OR = 0.80, p < 0.001) and income (OR = 0.90, p < 0.001) have significant negative impacts. Political affiliation again shows strong effects, with Republicans (OR = 14.46, p < 0.001
We can store the regression output in an object. The built-in summary() function will show us a better summary of the results:
Call:
glm(formula = covid_usa_r ~ sex_r + age_cat + race_r + edu +
income + party_r2 + ideology, data = dat)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.846824 0.067056 42.454 < 0.0000000000000002 ***
sex_rMale -0.094508 0.015804 -5.980 0.00000000230657349 ***
age_cat 0.079999 0.008294 9.645 < 0.0000000000000002 ***
race_rBlack 0.052098 0.053096 0.981 0.326512
race_rHispanic 0.186418 0.048596 3.836 0.000126 ***
race_rOther 0.172544 0.064271 2.685 0.007273 **
race_rWhite 0.187990 0.046198 4.069 0.00004753570079814 ***
edu -0.041517 0.005910 -7.025 0.00000000000228070 ***
income -0.021008 0.003762 -5.584 0.00000002407483930 ***
party_r2Ind/Oth 0.152483 0.019227 7.931 0.00000000000000241 ***
party_r2Rep 0.594893 0.025440 23.384 < 0.0000000000000002 ***
ideology -0.267742 0.009096 -29.436 < 0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.5891177)
Null deviance: 8403.0 on 9985 degrees of freedom
Residual deviance: 5875.9 on 9974 degrees of freedom
(656 observations deleted due to missingness)
AIC: 23069
Number of Fisher Scoring iterations: 2| covid_usa_r | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | SE | 95% CI | Statistic | p-value |
| (Intercept) | 2.85 | 0.07 | -Inf – Inf | 42.45 | <0.001 |
| sex r [Male] | -0.09 | 0.02 | -Inf – Inf | -5.98 | <0.001 |
| age cat | 0.08 | 0.01 | -Inf – Inf | 9.64 | <0.001 |
| race r [Black] | 0.05 | 0.05 | -Inf – Inf | 0.98 | 0.326 |
| race r [Hispanic] | 0.19 | 0.05 | -Inf – Inf | 3.84 | <0.001 |
| race r [Other] | 0.17 | 0.06 | -Inf – Inf | 2.68 | 0.007 |
| race r [White] | 0.19 | 0.05 | -Inf – Inf | 4.07 | <0.001 |
| edu | -0.04 | 0.01 | -Inf – Inf | -7.02 | <0.001 |
| income | -0.02 | 0.00 | -Inf – Inf | -5.58 | <0.001 |
| party r2 [Ind/Oth] | 0.15 | 0.02 | -Inf – Inf | 7.93 | <0.001 |
| party r2 [Rep] | 0.59 | 0.03 | -Inf – Inf | 23.38 | <0.001 |
| ideology | -0.27 | 0.01 | -Inf – Inf | -29.44 | <0.001 |
| Observations | 9986 | ||||
| R2 | 0.301 | ||||
You will notice that by default, the generalized linear model (glm) function will treat character variables as categorical. It will include in our model dummy variables for all variable categories except one, which will serve as the reference category that all the rest are compared to.
By default the last category is used as a reference. But what if we wanted to choose a different reference category? For example, we may want to drop “White” from the model and use it as our reference category for race. We can create a categorical variable (a so-called ‘factor’) and specify that.
Frequencies
dat$covid_usa_r
Type: Numeric
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 2458 23.29 23.29 23.10 23.10
2 3531 33.46 56.75 33.18 56.28
3 3659 34.67 91.42 34.38 90.66
4 905 8.58 100.00 8.50 99.16
<NA> 89 0.84 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$covid_usa
Type: Integer
Freq % Valid % Valid Cum. % Total % Total Cum.
----------- ------- --------- -------------- --------- --------------
1 905 8.58 8.58 8.50 8.50
2 3659 34.67 43.25 34.38 42.89
3 3531 33.46 76.71 33.18 76.07
4 2458 23.29 100.00 23.10 99.16
<NA> 89 0.84 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$race_r_f
Type: Factor
Freq % Valid % Valid Cum. % Total % Total Cum.
-------------- ------- --------- -------------- --------- --------------
White 7116 66.87 66.87 66.87 66.87
Asian 308 2.89 69.76 2.89 69.76
Black 884 8.31 78.07 8.31 78.07
Hispanic 2034 19.11 97.18 19.11 97.18
Other 300 2.82 100.00 2.82 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00See how the regression output changes:
Call:
glm(formula = covid_usa_r ~ sex_r + age_cat + race_r_f + edu +
income + party_r2 + ideology, data = dat)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.034815 0.052076 58.277 < 0.0000000000000002 ***
sex_rMale -0.094508 0.015804 -5.980 0.00000000230657349 ***
age_cat 0.079999 0.008294 9.645 < 0.0000000000000002 ***
race_r_fAsian -0.187990 0.046198 -4.069 0.00004753570079813 ***
race_r_fBlack -0.135892 0.029898 -4.545 0.00000555442687018 ***
race_r_fHispanic -0.001573 0.021023 -0.075 0.940
race_r_fOther -0.015446 0.046899 -0.329 0.742
edu -0.041517 0.005910 -7.025 0.00000000000228070 ***
income -0.021008 0.003762 -5.584 0.00000002407483930 ***
party_r2Ind/Oth 0.152483 0.019227 7.931 0.00000000000000241 ***
party_r2Rep 0.594893 0.025440 23.384 < 0.0000000000000002 ***
ideology -0.267742 0.009096 -29.436 < 0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.5891177)
Null deviance: 8403.0 on 9985 degrees of freedom
Residual deviance: 5875.9 on 9974 degrees of freedom
(656 observations deleted due to missingness)
AIC: 23069
Number of Fisher Scoring iterations: 2| covid_usa_r | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | SE | 95% CI | Statistic | p-value |
| (Intercept) | 3.03 | 0.05 | -Inf – Inf | 58.28 | <0.001 |
| sex r [Male] | -0.09 | 0.02 | -Inf – Inf | -5.98 | <0.001 |
| age cat | 0.08 | 0.01 | -Inf – Inf | 9.64 | <0.001 |
| race r f [Asian] | -0.19 | 0.05 | -Inf – Inf | -4.07 | <0.001 |
| race r f [Black] | -0.14 | 0.03 | -Inf – Inf | -4.55 | <0.001 |
| race r f [Hispanic] | -0.00 | 0.02 | -Inf – Inf | -0.07 | 0.940 |
| race r f [Other] | -0.02 | 0.05 | -Inf – Inf | -0.33 | 0.742 |
| edu | -0.04 | 0.01 | -Inf – Inf | -7.02 | <0.001 |
| income | -0.02 | 0.00 | -Inf – Inf | -5.58 | <0.001 |
| party r2 [Ind/Oth] | 0.15 | 0.02 | -Inf – Inf | 7.93 | <0.001 |
| party r2 [Rep] | 0.59 | 0.03 | -Inf – Inf | 23.38 | <0.001 |
| ideology | -0.27 | 0.01 | -Inf – Inf | -29.44 | <0.001 |
| Observations | 9986 | ||||
| R2 | 0.301 | ||||
Remember we can also add more relevant variables from the data into our regression:
Call:
glm(formula = covid_usa_r ~ sex_r + age_cat + race_r_f + edu +
income + party_r2 + ideology + covid_trump_r + covid_news_r,
data = dat)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7505321 0.0650425 26.914 < 0.0000000000000002 ***
sex_rMale -0.1000170 0.0149620 -6.685 0.0000000000246043 ***
age_cat 0.0617632 0.0081114 7.614 0.0000000000000294 ***
race_r_fAsian -0.1844469 0.0429839 -4.291 0.0000179797840673 ***
race_r_fBlack 0.0007666 0.0287185 0.027 0.97870
race_r_fHispanic -0.0071730 0.0216881 -0.331 0.74085
race_r_fOther -0.0543268 0.0432993 -1.255 0.20963
edu -0.0077178 0.0057010 -1.354 0.17585
income -0.0088808 0.0035996 -2.467 0.01364 *
party_r2Ind/Oth -0.0279994 0.0187251 -1.495 0.13488
party_r2Rep -0.0128419 0.0272820 -0.471 0.63786
ideology -0.0993492 0.0093609 -10.613 < 0.0000000000000002 ***
covid_trump_r 0.4532929 0.0092669 48.915 < 0.0000000000000002 ***
covid_news_r -0.0306819 0.0114040 -2.690 0.00715 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.4454372)
Null deviance: 7060.8 on 8454 degrees of freedom
Residual deviance: 3759.9 on 8441 degrees of freedom
(2187 observations deleted due to missingness)
AIC: 17173
Number of Fisher Scoring iterations: 2As usual, there is an R package the will offer us better and more informative formatting:
Result table:
| Observations | 8455 (2187 missing obs. deleted) |
| Dependent variable | covid_usa_r |
| Type | Linear regression |
| χ²(13) | 3300.84 |
| Pseudo-R² (Cragg-Uhler) | 0.50 |
| Pseudo-R² (McFadden) | 0.24 |
| AIC | 17172.69 |
| BIC | 17278.33 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 1.75 | 0.07 | 26.91 | 0.00 |
| sex_rMale | -0.10 | 0.01 | -6.68 | 0.00 |
| age_cat | 0.06 | 0.01 | 7.61 | 0.00 |
| race_r_fAsian | -0.18 | 0.04 | -4.29 | 0.00 |
| race_r_fBlack | 0.00 | 0.03 | 0.03 | 0.98 |
| race_r_fHispanic | -0.01 | 0.02 | -0.33 | 0.74 |
| race_r_fOther | -0.05 | 0.04 | -1.25 | 0.21 |
| edu | -0.01 | 0.01 | -1.35 | 0.18 |
| income | -0.01 | 0.00 | -2.47 | 0.01 |
| party_r2Ind/Oth | -0.03 | 0.02 | -1.50 | 0.13 |
| party_r2Rep | -0.01 | 0.03 | -0.47 | 0.64 |
| ideology | -0.10 | 0.01 | -10.61 | 0.00 |
| covid_trump_r | 0.45 | 0.01 | 48.92 | 0.00 |
| covid_news_r | -0.03 | 0.01 | -2.69 | 0.01 |
| Standard errors: MLE |
Standardized coefficients:
| Observations | 8455 (2187 missing obs. deleted) |
| Dependent variable | covid_usa_r |
| Type | Linear regression |
| χ²(13) | 3300.84 |
| Pseudo-R² (Cragg-Uhler) | 0.50 |
| Pseudo-R² (McFadden) | 0.24 |
| AIC | 17172.69 |
| BIC | 17278.33 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.33 | 0.02 | 139.64 | 0.00 |
| sex_rMale | -0.10 | 0.01 | -6.68 | 0.00 |
| age_cat | 0.06 | 0.01 | 7.61 | 0.00 |
| race_r_fAsian | -0.18 | 0.04 | -4.29 | 0.00 |
| race_r_fBlack | 0.00 | 0.03 | 0.03 | 0.98 |
| race_r_fHispanic | -0.01 | 0.02 | -0.33 | 0.74 |
| race_r_fOther | -0.05 | 0.04 | -1.25 | 0.21 |
| edu | -0.01 | 0.01 | -1.35 | 0.18 |
| income | -0.02 | 0.01 | -2.47 | 0.01 |
| party_r2Ind/Oth | -0.03 | 0.02 | -1.50 | 0.13 |
| party_r2Rep | -0.01 | 0.03 | -0.47 | 0.64 |
| ideology | -0.11 | 0.01 | -10.61 | 0.00 |
| covid_trump_r | 0.53 | 0.01 | 48.92 | 0.00 |
| covid_news_r | -0.02 | 0.01 | -2.69 | 0.01 |
| Standard errors: MLE; Continuous predictors are mean-centered and scaled by 1 s.d. The outcome variable remains in its original units. |
Plot the regression results:
Plot the results from both models:
We can create more readable coefficient names to use here:
We can also use our own model names if we wish:
Make the plot with better names
The plot provided is a coefficient plot comparing two models (Model 1 and Model 2) for various predictors. The estimates (coefficients) are plotted on the x-axis, and each predictor is listed on the y-axis. Both models’ estimates are shown with confidence intervals.
Sex (sex_rMale)
Both models suggest that being male has a negative estimate, indicating males are less likely to rate the response favorably compared to females. The estimates and confidence intervals are close, showing consistent results across models.
Age Category (age_cat)
Both models show a positive estimate for age category, meaning older age groups are more likely to rate the response favorably. The confidence intervals are overlapping and fairly narrow, indicating a stable effect.
Race
For race variables (race_r_fAsian, race_r_fBlack, race_r_fHispanic, race_r_fOther), there are differences in the estimates and confidence intervals between the two models. For instance, the effect of being Black (race_r_fBlack) has a slight positive estimate in Model 2 but is less clear in Model 1. The effect of being Hispanic (race_r_fHispanic) is positive in both models but with slightly different magnitudes. The confidence intervals for these estimates are wider, indicating less certainty about the exact effect sizes.
Education (edu)
Education has a negative estimate in both models, indicating that higher education levels are associated with less favorable ratings of the response. The estimates and confidence intervals are very similar across both models.
Income (income)
The effect of income is negative in both models, meaning higher income levels are associated with less favorable ratings. The confidence intervals are narrow and overlapping, showing consistency.
Political Party (party_r2Ind/Oth, party_r2Rep)
Political party affiliation shows a significant effect. Being independent/other (party_r2Ind/Oth) has a positive estimate in both models, indicating they are more likely to rate the response favorably. The effect of being Republican (party_r2Rep) is very strong and positive in both models, with non-overlapping confidence intervals, indicating a significant and strong effect.
Ideology (ideology)
Political ideology has a strong negative effect in both models, meaning more conservative ideology is associated with less favorable ratings. The confidence intervals are non-overlapping and consistent across models, highlighting a significant effect.
Specific Variables in Model 2
Model 2 includes additional variables (covid_trump_r and covid_news_r). The estimate for covid_trump_r is positive, indicating that favorable views on Trump’s handling of COVID-19 are associated with favorable ratings of the response. The covid_news_r variable has a smaller effect and is close to zero, suggesting news sources’ influence might be minimal or neutral.
Both models are largely consistent in the direction and magnitude of the effects for most predictors, with some differences in the specific effects of race and the additional variables in Model 2. The strong effects of political affiliation and ideology are clear and significant across both models. The additional variables in Model 2 provide further insights, particularly regarding views on Trump’s handling of COVID-19.
Generate pretty tables for your papers:
| Model w/o Trump | Model with Trump | |
|---|---|---|
| Male | -0.09 *** | -0.10 *** |
| (0.02) | (0.01) | |
| Age | 0.08 *** | 0.06 *** |
| (0.01) | (0.01) | |
| Education | -0.04 *** | -0.01 |
| (0.01) | (0.01) | |
| Income | -0.02 *** | -0.01 * |
| (0.00) | (0.00) | |
| Race/Ethnicity: Asian | -0.19 *** | -0.18 *** |
| (0.05) | (0.04) | |
| Race/Ethnicity: Black | -0.14 *** | 0.00 |
| (0.03) | (0.03) | |
| Race/Ethnicity: Hispanic | -0.00 | -0.01 |
| (0.02) | (0.02) | |
| Race/Ethnicity: Other | -0.02 | -0.05 |
| (0.05) | (0.04) | |
| Party: Republican | 0.59 *** | -0.01 |
| (0.03) | (0.03) | |
| Party: Indep/Other | 0.15 *** | -0.03 |
| (0.02) | (0.02) | |
| Ideology | -0.27 *** | -0.10 *** |
| (0.01) | (0.01) | |
| Trump COVID-19 approval | 0.45 *** | |
| (0.01) | ||
| COVID-19 news use | -0.03 ** | |
| (0.01) | ||
| N | 9986 | 8455 |
| AIC | 23069.15 | 17172.69 |
| BIC | 23162.86 | 17278.33 |
| Pseudo R2 | 0.32 | 0.50 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. | ||
We can now save the table in a pretty format:
| Model w/o Trump | Model with Trump | |
|---|---|---|
| Male | -0.09 *** | -0.10 *** |
| (0.02) | (0.01) | |
| Age | 0.08 *** | 0.06 *** |
| (0.01) | (0.01) | |
| Education | -0.04 *** | -0.01 |
| (0.01) | (0.01) | |
| Income | -0.02 *** | -0.01 * |
| (0.00) | (0.00) | |
| Race/Ethnicity: Asian | -0.19 *** | -0.18 *** |
| (0.05) | (0.04) | |
| Race/Ethnicity: Black | -0.14 *** | 0.00 |
| (0.03) | (0.03) | |
| Race/Ethnicity: Hispanic | -0.00 | -0.01 |
| (0.02) | (0.02) | |
| Race/Ethnicity: Other | -0.02 | -0.05 |
| (0.05) | (0.04) | |
| Party: Republican | 0.59 *** | -0.01 |
| (0.03) | (0.03) | |
| Party: Indep/Other | 0.15 *** | -0.03 |
| (0.02) | (0.02) | |
| Ideology | -0.27 *** | -0.10 *** |
| (0.01) | (0.01) | |
| Trump COVID-19 approval | 0.45 *** | |
| (0.01) | ||
| COVID-19 news use | -0.03 ** | |
| (0.01) | ||
| N | 9986 | 8455 |
| AIC | 23069.15 | 17172.69 |
| BIC | 23162.86 | 17278.33 |
| Pseudo R2 | 0.32 | 0.50 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. | ||
ADDITIONAL INFORMATION
Note that we can also use glm() for other types of regression by including an additional ‘family’ parameter, like so: * glm( y ~ x1 + x2 + x3, data=dat, family = “binomial”) # logistic regression * glm( y ~ x1 + x2 + x3, data=dat, family = “poisson”) # Poisson regression
Using survey weights
As we will discuss later on, we often want to use survey weights to improve the representativeness of our data. The pew survey data already includes weights. To apply them in a regression, we can simply add a parameter to the glm() function:
Call:
glm(formula = covid_usa_r ~ sex_r + age_cat + race_r_f + edu +
income + party_r2 + ideology + covid_trump_r + covid_news_r,
data = dat, weights = dat$weight)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.688831 0.061531 27.447 < 0.0000000000000002 ***
sex_rMale -0.086690 0.015412 -5.625 0.0000000192 ***
age_cat 0.068488 0.008195 8.357 < 0.0000000000000002 ***
race_r_fAsian -0.158580 0.037878 -4.187 0.0000286005 ***
race_r_fBlack 0.073285 0.026645 2.750 0.00596 **
race_r_fHispanic 0.012305 0.025450 0.484 0.62875
race_r_fOther 0.095890 0.036477 2.629 0.00858 **
edu -0.020610 0.005576 -3.696 0.00022 ***
income -0.009872 0.003566 -2.768 0.00565 **
party_r2Ind/Oth -0.038464 0.019851 -1.938 0.05271 .
party_r2Rep 0.004173 0.027338 0.153 0.87868
ideology -0.085329 0.009320 -9.155 < 0.0000000000000002 ***
covid_trump_r 0.430155 0.009033 47.619 < 0.0000000000000002 ***
covid_news_r -0.004119 0.011002 -0.374 0.70814
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.459697)
Null deviance: 6876.7 on 8454 degrees of freedom
Residual deviance: 3880.3 on 8441 degrees of freedom
(2187 observations deleted due to missingness)
AIC: 21071
Number of Fisher Scoring iterations: 2| Observations | 8455 (2187 missing obs. deleted) |
| Dependent variable | covid_usa_r |
| Type | Linear regression |
| χ²(13) | 2996.39 |
| Pseudo-R² (Cragg-Uhler) | 0.46 |
| Pseudo-R² (McFadden) | 0.19 |
| AIC | 21071.38 |
| BIC | 21177.01 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 1.69 | 0.06 | 27.45 | 0.00 |
| sex_rMale | -0.09 | 0.02 | -5.62 | 0.00 |
| age_cat | 0.07 | 0.01 | 8.36 | 0.00 |
| race_r_fAsian | -0.16 | 0.04 | -4.19 | 0.00 |
| race_r_fBlack | 0.07 | 0.03 | 2.75 | 0.01 |
| race_r_fHispanic | 0.01 | 0.03 | 0.48 | 0.63 |
| race_r_fOther | 0.10 | 0.04 | 2.63 | 0.01 |
| edu | -0.02 | 0.01 | -3.70 | 0.00 |
| income | -0.01 | 0.00 | -2.77 | 0.01 |
| party_r2Ind/Oth | -0.04 | 0.02 | -1.94 | 0.05 |
| party_r2Rep | 0.00 | 0.03 | 0.15 | 0.88 |
| ideology | -0.09 | 0.01 | -9.16 | 0.00 |
| covid_trump_r | 0.43 | 0.01 | 47.62 | 0.00 |
| covid_news_r | -0.00 | 0.01 | -0.37 | 0.71 |
| Standard errors: MLE |
But what if we want to do other types of analysis with weights? And what if we want to create our own weights? Good news: there is a package for that too!
We can create a “survey” object by using the svydesign() function. The ‘ids’ parameter specifies cluster IDs in a clustered design. When there are no clusters, we just put ids~1 The ‘data’ parameter is the data frame we have been using. the ‘weights’ parameter is the survey weight we want to use
Create a survey object:
Examine the weighted values for a variable:
race_r
Asian Black Hispanic Other White
433.2480 1246.0735 1555.6334 480.7359 6876.3042
Asian Black Hispanic Other White
308 884 2034 300 7116 Crosstabs:
sex_r
race_r Female Male
Asian 175.5732 257.6748
Black 779.6850 466.3885
Hispanic 764.8747 790.7587
Other 254.7233 226.0125
White 3579.3412 3296.9630
Female Male
Asian 133 175
Black 602 282
Hispanic 1153 881
Other 175 125
White 3807 3309Examine weighted mean and standard deviation for a variable. the ‘na.rm’ parameter if true tells the functions used here to ignore missing data if there is any of it present.
mean SE
ideology 2.9243 0.0153[1] 3.027796 std. dev.
ideology 1.054[1] 1.070451We can use the function ‘svyby’ to get weighted means by category – e.g. weighted ideology mean for men/women:
| sex_r | ideology | se |
|---|---|---|
| Female | 2.96 | 0.0203 |
| Male | 2.88 | 0.0231 |
We can do weighted t-tests:
Design-based t-test
data: ideology ~ sex_r
t = -2.6921, df = 10467, p-value = 0.007111
alternative hypothesis: true difference in mean is not equal to 0
95 percent confidence interval:
-0.14285548 -0.02247483
sample estimates:
difference in mean
-0.08266515 And, as we did before, we can estimate regression models.
Model 1: with weights, using the ‘survey’ package
Call:
svyglm(formula = covid_usa_r ~ sex_r + age_cat + race_r_f + party_r2,
design = dat.w)
Survey design:
svydesign(ids = ~1, data = dat, weights = dat$weight)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.66911 0.04396 37.965 < 0.0000000000000002 ***
sex_rMale -0.06974 0.02572 -2.712 0.00670 **
age_cat 0.13488 0.01321 10.209 < 0.0000000000000002 ***
race_r_fAsian -0.29657 0.06481 -4.576 0.00000479 ***
race_r_fBlack 0.06271 0.05198 1.206 0.22767
race_r_fHispanic 0.10718 0.03927 2.729 0.00636 **
race_r_fOther 0.13529 0.08001 1.691 0.09089 .
party_r2Ind/Oth 0.28370 0.03104 9.141 < 0.0000000000000002 ***
party_r2Rep 0.92242 0.03245 28.426 < 0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.681278)
Number of Fisher Scoring iterations: 2Model 2: also with weights, but using glm() instead
Call:
glm(formula = covid_usa_r ~ sex_r + age_cat + race_r_f + party_r2,
data = dat, weights = dat$weight)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.669106 0.029349 56.870 < 0.0000000000000002 ***
sex_rMale -0.069744 0.016310 -4.276 0.00001919246439 ***
age_cat 0.134883 0.008257 16.335 < 0.0000000000000002 ***
race_r_fAsian -0.296569 0.041600 -7.129 0.00000000000108 ***
race_r_fBlack 0.062708 0.027136 2.311 0.020859 *
race_r_fHispanic 0.107180 0.024463 4.381 0.00001191173846 ***
race_r_fOther 0.135294 0.039352 3.438 0.000588 ***
party_r2Ind/Oth 0.283696 0.019862 14.283 < 0.0000000000000002 ***
party_r2Rep 0.922417 0.022500 40.997 < 0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.6785958)
Null deviance: 8797.3 on 10459 degrees of freedom
Residual deviance: 7092.0 on 10451 degrees of freedom
(182 observations deleted due to missingness)
AIC: 30098
Number of Fisher Scoring iterations: 2Model 3: using glm but no weights
Call:
glm(formula = covid_usa_r ~ sex_r + age_cat + race_r_f + party_r2,
data = dat)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.604386 0.028695 55.911 < 0.0000000000000002 ***
sex_rMale -0.100394 0.016104 -6.234 0.000000000472 ***
age_cat 0.112723 0.008451 13.338 < 0.0000000000000002 ***
race_r_fAsian -0.223996 0.047889 -4.677 0.000002942205 ***
race_r_fBlack 0.006204 0.030074 0.206 0.837
race_r_fHispanic 0.087655 0.021218 4.131 0.000036383182 ***
race_r_fOther 0.033863 0.048197 0.703 0.482
party_r2Ind/Oth 0.372972 0.018536 20.121 < 0.0000000000000002 ***
party_r2Rep 1.059060 0.021190 49.979 < 0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.6548246)
Null deviance: 8802.5 on 10459 degrees of freedom
Residual deviance: 6843.6 on 10451 degrees of freedom
(182 observations deleted due to missingness)
AIC: 25267
Number of Fisher Scoring iterations: 2We also can do other things, such as plot variable histograms:
Creating survey weights
Following-up class discussion on post-stratification weights, here is how we can generate our own weights. First, we need to select the variables we want to use for weighting. Question: how does our sample differ from the population in ways that affect our key research questions? (and what is the population?) Let’s say we want to create weights based on gender and race. What are the distributions of gender and race in our population? We need to know that so we can adjust accordingly in our data. Looking up population data (see census.gov), we find that the US is 48% male and 52% female. We also see that we seem to have a total of 63% White, 17% Hispanic, 12% Black, 6% Asian, and 2% other Americans. How does our data compare to the general population?
Frequencies
dat$sex_r
Type: Character
Freq % Valid % Valid Cum. % Total % Total Cum.
------------ ------- --------- -------------- --------- --------------
Female 5870 55.16 55.16 55.16 55.16
Male 4772 44.84 100.00 44.84 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Frequencies
dat$race_r
Type: Character
Freq % Valid % Valid Cum. % Total % Total Cum.
-------------- ------- --------- -------------- --------- --------------
Asian 308 2.89 2.89 2.89 2.89
Black 884 8.31 11.20 8.31 11.20
Hispanic 2034 19.11 30.31 19.11 30.31
Other 300 2.82 33.13 2.82 33.13
White 7116 66.87 100.00 66.87 100.00
<NA> 0 0.00 100.00
Total 10642 100.00 100.00 100.00 100.00Ok, now let’s create some weights to adjust for that.
Create a survey object with no weights specified:
Calculate how many people of each sex or race we would expect to see in our data if it was distributed like the US population:
[1] 10642[1] 5108.16 5533.84[1] 6704.46 1809.14 1277.04 638.52 212.84Each of the following data frames has two columns: one with the name of the variable we will use to create weights, and a second column named “Freq”. The first column contains all possible categories of that variable. The second gives us the number of people from that category we would have in the data if our sample followed the same proportions that we see in the entire population (those are ‘num_by_sex’ and ‘num_by_race’ above)
| sex_r | Freq |
|---|---|
| Male | 5.11e+03 |
| Female | 5.53e+03 |
| race_r | Freq |
|---|---|
| White | 6.7e+03 |
| Hispanic | 1.81e+03 |
| Black | 1.28e+03 |
| Asian | 639 |
| Other | 213 |
Finally, we use the rake() function to calculate weights based on the population values for each of the variables we have decided to use. Note that we if we have any missing values in the sex or race data,we will get an error at this point.Here, ‘sample.margins’ is a list with formulas for all the weighing variables we are using. The ‘population.margins’ parameter contains the data frame we created above (one for each variable).
Independent Sampling design (with replacement)
rake(design = dat.s, sample.margins = list(~sex_r, ~race_r),
population.margins = list(sex_dist, race_dist))We can extract the weight vector from our data if we need it:
[1] 0.8835964 1.0095522 1.0095522 0.8835964 1.9177880 0.8835964 0.8835964
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[57] 0.8377255 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
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[113] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.9571424
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[127] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
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[141] 1.0095522 0.8835964 1.9177880 0.8835964 1.0095522 1.0095522 0.8377255
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[162] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[169] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[176] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[183] 1.0095522 1.0095522 1.0095522 1.5787520 0.8835964 1.0095522 0.8835964
[190] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
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[204] 0.8835964 0.8835964 1.3817806 0.7651538 0.8377255 1.0095522 0.8835964
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[225] 1.0095522 1.5787520 0.8835964 1.3817806 1.0095522 0.8377255 0.8835964
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[239] 1.0095522 0.6696901 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[246] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[253] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8377255
[260] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[267] 0.8377255 1.3817806 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[274] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[281] 1.5787520 0.8835964 1.0095522 1.0095522 0.7651538 1.0095522 1.0095522
[288] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[295] 1.3817806 0.8835964 0.8835964 1.3817806 1.3817806 1.0095522 1.0095522
[302] 0.8835964 1.0095522 1.5787520 0.8835964 1.0095522 1.0095522 0.8835964
[309] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[316] 1.9177880 1.0095522 2.1911668 1.0095522 1.0095522 0.7651538 0.6696901
[323] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.3817806
[330] 1.0095522 1.5787520 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522
[337] 1.0095522 1.3817806 0.6696901 0.8835964 0.8835964 0.8835964 1.0095522
[344] 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[351] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.8377255
[358] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[365] 1.0095522 1.0095522 1.0095522 2.1911668 0.8835964 1.9177880 0.8835964
[372] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[379] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[386] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[393] 1.0095522 0.7651538 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[400] 1.0095522 0.9571424 1.3817806 1.0095522 1.0095522 0.8835964 0.8835964
[407] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.5787520 1.0095522
[414] 0.8835964 0.8835964 0.9571424 0.8835964 1.0095522 0.8835964 0.8835964
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[449] 0.6696901 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
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[463] 1.3817806 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[470] 1.0095522 0.8835964 0.6696901 1.0095522 1.0095522 1.0095522 1.0095522
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[512] 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522 1.5787520 0.8835964
[519] 0.8377255 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522
[526] 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 0.9571424 0.8835964
[533] 0.9571424 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[540] 0.7651538 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[547] 1.0095522 0.8835964 0.8835964 0.8835964 0.6696901 0.8835964 0.8835964
[554] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[561] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.6696901 0.8835964
[568] 0.8835964 0.6696901 0.8835964 1.0095522 0.7651538 1.0095522 1.0095522
[575] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[582] 0.8377255 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[589] 0.8835964 1.5787520 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[596] 1.0095522 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522 0.8377255
[603] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.9571424
[610] 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964
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[624] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 1.9177880 1.0095522
[631] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[638] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[645] 1.0095522 0.8835964 0.8377255 0.8835964 1.0095522 1.0095522 0.8835964
[652] 1.0095522 0.8835964 1.5787520 1.0095522 1.0095522 0.8835964 1.5787520
[659] 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[666] 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[673] 1.0095522 0.8835964 0.8835964 0.8835964 1.3817806 1.5787520 1.0095522
[680] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[687] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[694] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522
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[708] 0.8835964 1.3817806 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[715] 1.0095522 0.8835964 0.8835964 1.0095522 2.1911668 0.8377255 0.6696901
[722] 1.0095522 1.0095522 0.8377255 1.5787520 0.8835964 0.8835964 1.0095522
[729] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964
[736] 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[743] 0.8835964 0.8835964 1.0095522 1.3817806 1.0095522 0.8835964 1.0095522
[750] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[757] 1.0095522 1.0095522 0.8835964 1.3817806 1.3817806 1.0095522 0.8835964
[764] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[771] 0.8835964 0.8835964 0.6696901 0.8835964 1.0095522 1.0095522 0.8835964
[778] 1.0095522 0.9571424 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[785] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[792] 1.0095522 1.0095522 0.7651538 0.8835964 1.0095522 0.8835964 1.3817806
[799] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8377255
[806] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.9571424 0.8835964
[813] 0.8835964 1.0095522 1.0095522 0.8835964 0.8377255 0.8835964 1.0095522
[820] 1.5787520 1.0095522 0.8835964 0.8835964 0.6696901 0.8835964 1.0095522
[827] 1.0095522 1.3817806 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[834] 1.0095522 0.8377255 0.8835964 0.8835964 0.8835964 1.5787520 0.8835964
[841] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8377255 0.8835964
[848] 0.8835964 0.8835964 1.9177880 1.0095522 1.0095522 0.8835964 1.0095522
[855] 1.0095522 1.0095522 0.8377255 0.8835964 1.0095522 1.5787520 0.9571424
[862] 1.3817806 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522
[869] 1.0095522 0.8377255 0.8835964 0.9571424 0.6696901 1.0095522 1.0095522
[876] 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[883] 1.0095522 1.0095522 0.8835964 0.7651538 1.3817806 1.0095522 1.5787520
[890] 0.7651538 1.0095522 0.6696901 1.9177880 0.8377255 0.8835964 1.0095522
[897] 0.8377255 1.3817806 0.9571424 1.0095522 0.8835964 1.0095522 0.8835964
[904] 0.6696901 0.7651538 1.0095522 1.0095522 1.5787520 0.8835964 1.0095522
[911] 1.5787520 1.0095522 0.8835964 1.0095522 0.6696901 0.8377255 0.8835964
[918] 0.8835964 1.5787520 1.0095522 2.1911668 0.8377255 0.8835964 1.0095522
[925] 1.0095522 0.8377255 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522
[932] 1.3817806 0.8835964 1.0095522 1.0095522 0.8835964 0.8377255 0.8835964
[939] 0.8835964 0.8835964 0.9571424 1.0095522 0.8377255 0.8835964 1.0095522
[946] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.7651538 0.8377255
[953] 1.3817806 0.8835964 1.0095522 1.0095522 0.8377255 1.0095522 1.0095522
[960] 1.3817806 0.8835964 1.5787520 0.8835964 0.8835964 1.0095522 0.8835964
[967] 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[974] 1.5787520 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[981] 1.0095522 0.8835964 0.8835964 0.8835964 1.9177880 1.0095522 1.0095522
[988] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 1.5787520
[995] 1.0095522 0.8835964 0.9571424 1.0095522 1.5787520 0.8835964 1.0095522
[1002] 1.5787520 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.9571424
[1009] 1.0095522 1.0095522 0.8377255 0.8835964 0.8835964 0.8835964 0.8835964
[1016] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522
[1023] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[1030] 1.0095522 0.9571424 0.8835964 1.0095522 1.3817806 0.8377255 0.8835964
[1037] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[1044] 0.8835964 0.8835964 0.8377255 1.0095522 1.0095522 1.0095522 0.8835964
[1051] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[1058] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[1065] 1.0095522 1.0095522 1.3817806 0.9571424 1.0095522 1.0095522 0.8835964
[1072] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[1079] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[1086] 0.9571424 1.3817806 1.0095522 0.8835964 0.8377255 0.9571424 1.0095522
[1093] 1.0095522 0.8835964 2.1911668 0.9571424 2.1911668 0.7651538 1.0095522
[1100] 1.0095522 1.0095522 0.6696901 1.0095522 1.0095522 1.0095522 0.8835964
[1107] 0.8835964 0.8377255 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[1114] 0.8835964 0.8377255 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[1121] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.7651538 1.0095522
[1128] 0.8835964 0.8835964 0.8377255 1.5787520 0.8835964 0.8835964 0.8835964
[1135] 0.8835964 1.0095522 0.7651538 0.8835964 0.8835964 0.8835964 0.8835964
[1142] 0.8835964 0.8835964 1.0095522 1.0095522 1.3817806 0.8835964 1.5787520
[1149] 1.5787520 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522
[1156] 0.8835964 1.0095522 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522
[1163] 1.0095522 0.9571424 1.3817806 1.0095522 0.8835964 0.8377255 1.0095522
[1170] 0.8835964 1.0095522 1.0095522 1.5787520 1.0095522 1.0095522 0.8835964
[1177] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[1184] 1.0095522 1.0095522 1.0095522 0.8835964 0.7651538 1.5787520 1.5787520
[1191] 1.0095522 1.0095522 0.8835964 1.5787520 2.1911668 1.0095522 1.0095522
[1198] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.9571424
[1205] 0.8835964 1.0095522 0.9571424 1.5787520 1.0095522 0.8835964 0.8835964
[1212] 1.0095522 1.0095522 1.0095522 0.8835964 0.8377255 0.8835964 1.0095522
[1219] 1.5787520 1.3817806 1.3817806 1.0095522 1.0095522 1.0095522 0.7651538
[1226] 1.0095522 1.3817806 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[1233] 2.1911668 1.9177880 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[1240] 0.8377255 0.8835964 0.9571424 1.9177880 0.8835964 0.8835964 0.8377255
[1247] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8377255 1.0095522
[1254] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 0.9571424
[1261] 0.8377255 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[1268] 0.9571424 0.8835964 0.8377255 0.8835964 1.0095522 0.8835964 0.8835964
[1275] 1.0095522 1.0095522 1.0095522 1.0095522 1.9177880 0.8835964 2.1911668
[1282] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.3817806 1.0095522
[1289] 1.0095522 0.8377255 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[1296] 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.9571424 0.8835964
[1303] 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[1310] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.9571424
[1317] 1.0095522 1.0095522 0.8835964 0.8377255 0.8835964 1.0095522 0.8835964
[1324] 1.0095522 1.0095522 1.0095522 0.8835964 1.5787520 1.0095522 1.0095522
[1331] 0.8835964 0.8835964 0.8835964 0.9571424 1.0095522 1.0095522 0.8835964
[1338] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 1.5787520 0.8835964
[1345] 0.8835964 1.0095522 0.6696901 0.8835964 1.0095522 1.0095522 1.0095522
[1352] 0.8835964 0.9571424 1.0095522 0.8377255 0.8835964 1.0095522 0.8835964
[1359] 1.0095522 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964 0.8835964
[1366] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.6696901
[1373] 0.8835964 1.0095522 1.0095522 0.8377255 1.0095522 1.0095522 0.9571424
[1380] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[1387] 2.1911668 1.0095522 2.1911668 0.8835964 1.0095522 1.0095522 0.8835964
[1394] 0.9571424 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.6696901
[1401] 1.0095522 1.5787520 0.8835964 1.3817806 1.3817806 1.0095522 2.1911668
[1408] 0.8835964 0.8835964 1.3817806 0.8377255 1.0095522 1.0095522 1.0095522
[1415] 1.0095522 0.8835964 0.8835964 1.3817806 1.3817806 2.1911668 1.0095522
[1422] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[1429] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8377255 0.8835964
[1436] 1.0095522 1.0095522 2.1911668 1.3817806 1.0095522 1.0095522 1.9177880
[1443] 1.0095522 0.8835964 0.8835964 1.5787520 0.8835964 1.5787520 1.0095522
[1450] 1.0095522 0.6696901 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[1457] 1.0095522 1.0095522 1.0095522 0.8377255 1.0095522 1.0095522 1.0095522
[1464] 1.0095522 0.8835964 1.0095522 0.6696901 1.0095522 0.8835964 1.0095522
[1471] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[1478] 0.8835964 0.8377255 0.8835964 0.8835964 1.3817806 1.0095522 2.1911668
[1485] 0.9571424 1.3817806 1.0095522 1.5787520 0.8377255 2.1911668 1.0095522
[1492] 1.0095522 1.3817806 0.8835964 0.8835964 2.1911668 1.0095522 1.3817806
[1499] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[1506] 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964 1.3817806 0.6696901
[1513] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[1520] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806
[1527] 1.0095522 0.8377255 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[1534] 1.0095522 1.0095522 0.8835964 0.9571424 0.8835964 0.8835964 0.8835964
[1541] 0.8835964 0.8377255 0.8835964 0.7651538 1.0095522 1.0095522 1.5787520
[1548] 0.8377255 0.8835964 1.3817806 0.8835964 1.0095522 0.8835964 0.6696901
[1555] 0.8835964 1.0095522 1.0095522 1.9177880 1.0095522 1.0095522 0.8835964
[1562] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.3817806
[1569] 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 1.3817806
[1576] 1.0095522 0.8835964 0.8835964 0.9571424 0.8835964 1.0095522 1.0095522
[1583] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[1590] 0.8835964 0.8377255 1.0095522 0.8377255 0.8835964 1.5787520 1.0095522
[1597] 1.0095522 0.8835964 1.0095522 1.0095522 1.3817806 0.8835964 0.8835964
[1604] 0.8835964 0.8835964 1.9177880 1.0095522 1.0095522 1.0095522 1.0095522
[1611] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[1618] 1.0095522 1.0095522 0.8377255 0.8835964 1.0095522 1.0095522 0.6696901
[1625] 0.8835964 0.7651538 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[1632] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[1639] 1.5787520 2.1911668 0.9571424 0.8835964 1.0095522 0.8835964 0.9571424
[1646] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[1653] 1.3817806 1.0095522 0.9571424 0.8835964 1.0095522 2.1911668 1.0095522
[1660] 1.0095522 1.0095522 0.8835964 1.0095522 0.7651538 0.8835964 0.8835964
[1667] 1.5787520 1.0095522 0.9571424 1.0095522 0.8377255 1.0095522 0.8835964
[1674] 0.8835964 1.0095522 0.8835964 0.6696901 0.8835964 0.8835964 0.8835964
[1681] 0.8835964 1.5787520 0.7651538 0.8835964 0.8835964 0.9571424 0.6696901
[1688] 0.8835964 0.8835964 1.0095522 0.8835964 1.9177880 1.5787520 0.8835964
[1695] 0.8377255 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[1702] 2.1911668 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.7651538
[1709] 0.8835964 0.9571424 0.8835964 0.8835964 1.5787520 0.8835964 1.0095522
[1716] 0.8835964 0.8377255 0.8835964 1.0095522 1.5787520 0.9571424 0.8835964
[1723] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[1730] 0.8835964 0.8835964 0.8835964 0.8835964 1.5787520 0.8835964 1.0095522
[1737] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[1744] 0.8835964 0.8377255 1.0095522 0.8835964 2.1911668 0.8835964 0.7651538
[1751] 0.8377255 0.8835964 1.3817806 1.0095522 0.8835964 0.8835964 0.8835964
[1758] 0.8835964 0.8835964 0.9571424 0.8835964 0.8835964 0.8835964 1.0095522
[1765] 2.1911668 1.0095522 1.0095522 0.8377255 2.1911668 1.0095522 1.0095522
[1772] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.3817806 0.9571424
[1779] 0.8377255 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[1786] 0.6696901 0.8835964 0.8835964 1.0095522 0.8835964 1.5787520 2.1911668
[1793] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522
[1800] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.9571424 0.8835964
[1807] 1.5787520 1.3817806 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[1814] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[1821] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[1828] 1.0095522 1.0095522 1.0095522 1.0095522 0.8377255 0.8835964 0.8835964
[1835] 0.8835964 0.9571424 1.0095522 1.0095522 1.3817806 1.0095522 1.0095522
[1842] 0.8835964 1.0095522 0.7651538 0.8377255 0.8835964 1.5787520 1.0095522
[1849] 1.0095522 0.8835964 1.0095522 1.0095522 1.9177880 1.5787520 0.8835964
[1856] 0.8835964 0.6696901 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522
[1863] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[1870] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964
[1877] 0.8835964 1.3817806 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[1884] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.3817806 0.6696901
[1891] 0.8377255 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[1898] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.3817806 1.0095522
[1905] 1.0095522 1.0095522 0.8377255 0.7651538 1.3817806 1.0095522 0.8835964
[1912] 1.0095522 0.8835964 1.0095522 1.0095522 1.3817806 1.0095522 0.9571424
[1919] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 1.3817806 1.0095522
[1926] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.3817806
[1933] 1.0095522 0.8835964 1.0095522 0.8835964 0.6696901 0.8835964 0.8835964
[1940] 0.8835964 0.8835964 0.9571424 0.8835964 0.8835964 0.8835964 1.0095522
[1947] 0.8835964 2.1911668 0.8835964 0.8377255 0.8835964 0.8377255 1.0095522
[1954] 1.0095522 1.5787520 0.8835964 0.8835964 0.8835964 1.5787520 0.7651538
[1961] 0.6696901 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.3817806
[1968] 1.3817806 0.8835964 0.8835964 1.0095522 0.9571424 0.8835964 1.0095522
[1975] 1.0095522 1.0095522 1.0095522 0.8835964 0.7651538 0.9571424 1.0095522
[1982] 1.0095522 1.0095522 1.5787520 1.0095522 0.8835964 1.0095522 0.9571424
[1989] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.3817806
[1996] 0.7651538 1.0095522 1.5787520 1.0095522 1.0095522 0.8835964 1.0095522
[2003] 0.6696901 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.3817806
[2010] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[2017] 0.8835964 0.9571424 1.5787520 1.0095522 1.0095522 0.8835964 1.0095522
[2024] 1.0095522 1.0095522 0.8835964 1.5787520 0.8835964 1.0095522 1.0095522
[2031] 1.0095522 0.8835964 1.5787520 1.0095522 1.0095522 1.0095522 1.0095522
[2038] 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522 2.1911668 0.8835964
[2045] 0.8835964 1.5787520 1.0095522 0.9571424 0.8835964 1.0095522 0.8835964
[2052] 1.5787520 0.7651538 1.3817806 1.0095522 1.0095522 0.8835964 0.8835964
[2059] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[2066] 1.0095522 1.5787520 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[2073] 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[2080] 0.8835964 0.8835964 1.0095522 1.5787520 0.8835964 0.6696901 0.8835964
[2087] 0.8835964 1.3817806 0.8835964 0.8835964 0.8835964 0.7651538 0.8835964
[2094] 0.7651538 0.8835964 0.8835964 0.8835964 1.3817806 0.8835964 1.3817806
[2101] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[2108] 0.8835964 1.3817806 0.8835964 0.8835964 0.8835964 1.3817806 1.0095522
[2115] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.5787520 1.0095522
[2122] 0.8835964 0.8835964 0.9571424 0.6696901 0.8835964 1.0095522 0.8835964
[2129] 1.0095522 0.8835964 0.8835964 0.8835964 1.5787520 1.0095522 1.5787520
[2136] 1.0095522 0.8835964 1.3817806 1.0095522 1.0095522 1.0095522 0.9571424
[2143] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[2150] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[2157] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.8377255
[2164] 0.8835964 1.5787520 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[2171] 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8377255 1.5787520
[2178] 1.0095522 0.8835964 0.8835964 1.0095522 0.7651538 0.8835964 0.8835964
[2185] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[2192] 0.8835964 1.3817806 1.3817806 0.8835964 1.0095522 2.1911668 1.0095522
[2199] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8377255
[2206] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[2213] 0.8835964 1.0095522 1.5787520 0.8835964 1.3817806 1.0095522 0.8835964
[2220] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[2227] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 1.5787520
[2234] 1.0095522 0.7651538 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[2241] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 0.8377255
[2248] 0.8835964 1.5787520 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[2255] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[2262] 0.8835964 0.8835964 2.1911668 1.0095522 1.0095522 0.8835964 1.9177880
[2269] 0.8377255 0.9571424 0.8835964 1.0095522 1.3817806 1.3817806 0.8835964
[2276] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.3817806
[2283] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8377255 1.3817806
[2290] 1.3817806 0.8835964 0.8835964 1.5787520 1.0095522 1.0095522 1.0095522
[2297] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[2304] 1.0095522 1.5787520 1.5787520 1.0095522 0.8835964 0.8835964 0.8835964
[2311] 1.0095522 1.0095522 0.8835964 0.8377255 0.9571424 2.1911668 0.8835964
[2318] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[2325] 0.9571424 1.0095522 1.0095522 1.5787520 1.5787520 0.8835964 0.8835964
[2332] 1.0095522 1.0095522 1.0095522 0.9571424 1.0095522 0.8835964 1.0095522
[2339] 1.0095522 1.0095522 0.6696901 1.5787520 0.8835964 1.0095522 0.8377255
[2346] 1.5787520 1.0095522 1.0095522 1.5787520 0.8835964 0.8835964 0.9571424
[2353] 1.0095522 0.8835964 1.3817806 1.5787520 1.0095522 0.9571424 1.5787520
[2360] 0.7651538 1.0095522 1.3817806 1.0095522 1.0095522 1.0095522 0.8835964
[2367] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[2374] 1.0095522 0.8835964 0.8835964 0.8835964 0.9571424 0.8835964 2.1911668
[2381] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8377255 0.8835964
[2388] 1.0095522 1.0095522 0.9571424 1.0095522 1.9177880 1.0095522 0.8835964
[2395] 1.0095522 1.0095522 0.8835964 1.3817806 1.0095522 1.0095522 1.0095522
[2402] 1.5787520 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[2409] 1.5787520 0.8835964 0.8835964 0.9571424 0.8835964 0.8835964 0.8835964
[2416] 1.0095522 1.0095522 0.9571424 0.8835964 1.0095522 0.8835964 1.0095522
[2423] 1.0095522 0.8377255 1.0095522 0.8377255 0.8835964 0.9571424 1.0095522
[2430] 1.0095522 0.8835964 1.0095522 0.7651538 1.0095522 0.8377255 0.8835964
[2437] 1.0095522 0.8835964 1.3817806 1.0095522 0.8377255 1.0095522 1.5787520
[2444] 0.8835964 0.9571424 0.9571424 0.7651538 1.0095522 2.1911668 0.8835964
[2451] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[2458] 0.8377255 1.3817806 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[2465] 1.3817806 0.9571424 1.0095522 0.8835964 1.0095522 1.9177880 0.8835964
[2472] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.9571424 1.0095522
[2479] 0.9571424 1.0095522 1.0095522 2.1911668 1.0095522 1.0095522 0.8835964
[2486] 0.8835964 1.0095522 0.8835964 0.9571424 1.0095522 0.8835964 0.8835964
[2493] 1.0095522 0.8835964 0.8835964 0.8377255 1.0095522 0.8835964 0.8835964
[2500] 0.8835964 0.8835964 2.1911668 1.0095522 1.0095522 0.8835964 0.8835964
[2507] 0.8835964 0.9571424 0.8835964 1.0095522 0.9571424 1.0095522 0.8835964
[2514] 1.0095522 1.5787520 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[2521] 0.8377255 1.0095522 0.8835964 0.8835964 0.9571424 0.9571424 1.0095522
[2528] 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964 0.9571424 0.8835964
[2535] 1.0095522 0.9571424 1.0095522 1.0095522 0.8835964 1.0095522 1.3817806
[2542] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[2549] 1.0095522 1.0095522 0.8835964 0.8835964 1.5787520 1.3817806 1.0095522
[2556] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[2563] 1.0095522 0.8835964 1.0095522 0.6696901 1.0095522 1.0095522 1.0095522
[2570] 0.8377255 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522 1.3817806
[2577] 2.1911668 1.0095522 1.0095522 1.0095522 2.1911668 0.8835964 1.0095522
[2584] 0.8835964 0.8835964 0.8835964 1.3817806 1.0095522 1.0095522 1.0095522
[2591] 0.8835964 1.9177880 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[2598] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[2605] 0.8835964 0.8835964 0.8377255 0.8835964 1.0095522 1.0095522 0.8835964
[2612] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[2619] 0.8835964 0.8377255 1.5787520 1.5787520 1.0095522 0.8835964 0.8835964
[2626] 1.0095522 1.3817806 1.0095522 1.5787520 1.0095522 1.0095522 0.8835964
[2633] 0.8835964 1.0095522 1.0095522 0.9571424 1.0095522 0.8835964 0.8835964
[2640] 0.9571424 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964
[2647] 1.3817806 0.9571424 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[2654] 0.8835964 0.8377255 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[2661] 1.0095522 0.8377255 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522
[2668] 0.9571424 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.5787520
[2675] 0.8835964 1.0095522 0.8377255 0.8835964 1.0095522 0.8377255 0.8835964
[2682] 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522
[2689] 0.8835964 0.9571424 0.8377255 1.5787520 1.0095522 1.0095522 0.8835964
[2696] 1.0095522 1.0095522 1.3817806 1.3817806 0.8835964 1.0095522 0.8835964
[2703] 1.0095522 1.0095522 1.5787520 0.8835964 1.0095522 0.8835964 0.8835964
[2710] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 1.5787520 0.8377255
[2717] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[2724] 1.0095522 0.8835964 1.0095522 0.8377255 0.8835964 1.0095522 0.8835964
[2731] 0.8835964 1.0095522 1.0095522 1.0095522 0.9571424 0.8835964 1.5787520
[2738] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[2745] 0.8835964 0.6696901 0.8835964 2.1911668 1.0095522 1.0095522 0.9571424
[2752] 0.8835964 0.9571424 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[2759] 1.0095522 1.3817806 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964
[2766] 1.0095522 1.5787520 0.7651538 0.8835964 0.9571424 0.6696901 1.5787520
[2773] 0.9571424 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[2780] 0.9571424 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[2787] 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 1.5787520 0.8835964
[2794] 1.0095522 1.0095522 1.5787520 0.8835964 0.9571424 1.0095522 0.9571424
[2801] 1.0095522 0.8835964 2.1911668 0.8835964 0.8835964 1.3817806 0.8835964
[2808] 1.0095522 0.8835964 1.0095522 0.9571424 0.8835964 1.0095522 0.8835964
[2815] 0.8835964 1.0095522 1.9177880 1.0095522 1.0095522 1.0095522 1.0095522
[2822] 0.8835964 0.8835964 1.3817806 1.0095522 0.8835964 2.1911668 1.3817806
[2829] 0.7651538 1.0095522 1.0095522 1.0095522 0.8835964 0.9571424 1.0095522
[2836] 0.8835964 0.8835964 2.1911668 1.0095522 0.6696901 0.8835964 0.9571424
[2843] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 2.1911668 1.0095522
[2850] 0.7651538 1.0095522 1.0095522 0.8835964 1.0095522 1.9177880 1.9177880
[2857] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[2864] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 1.5787520
[2871] 1.0095522 1.0095522 1.5787520 1.3817806 1.0095522 1.0095522 1.0095522
[2878] 1.0095522 1.0095522 1.3817806 1.0095522 1.0095522 1.0095522 1.0095522
[2885] 1.5787520 1.5787520 0.8835964 1.0095522 1.0095522 0.9571424 0.8835964
[2892] 0.8835964 0.8835964 0.8835964 2.1911668 1.0095522 1.5787520 0.7651538
[2899] 1.0095522 0.8835964 1.0095522 1.0095522 1.3817806 0.8835964 1.0095522
[2906] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964
[2913] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[2920] 1.0095522 0.6696901 1.0095522 1.0095522 1.0095522 0.8835964 0.6696901
[2927] 1.0095522 1.0095522 0.8377255 0.8835964 0.8835964 1.0095522 1.3817806
[2934] 0.8835964 0.8835964 0.8835964 0.8835964 0.9571424 1.0095522 1.0095522
[2941] 0.6696901 0.8835964 0.9571424 1.0095522 1.0095522 0.8835964 1.0095522
[2948] 0.8835964 1.0095522 0.6696901 1.0095522 1.0095522 1.0095522 1.3817806
[2955] 1.0095522 1.3817806 1.0095522 1.0095522 1.0095522 0.8377255 0.8835964
[2962] 1.3817806 0.8835964 0.8835964 0.8377255 1.0095522 0.8835964 0.8377255
[2969] 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964 0.8835964
[2976] 0.8377255 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[2983] 0.8835964 0.7651538 0.8377255 1.0095522 1.3817806 0.8835964 1.0095522
[2990] 1.0095522 1.0095522 1.3817806 0.8835964 1.0095522 1.0095522 1.5787520
[2997] 2.1911668 1.0095522 0.9571424 1.0095522 0.8835964 0.8377255 0.7651538
[3004] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[3011] 0.8835964 1.0095522 0.7651538 1.0095522 0.8835964 1.3817806 0.8835964
[3018] 0.9571424 1.0095522 1.0095522 1.3817806 0.8835964 0.8835964 0.8835964
[3025] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 1.5787520
[3032] 0.8835964 1.9177880 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522
[3039] 1.5787520 1.5787520 1.5787520 0.8835964 1.3817806 0.8835964 1.5787520
[3046] 0.9571424 0.9571424 0.9571424 0.8835964 0.8835964 0.9571424 0.6696901
[3053] 1.9177880 0.8835964 0.8835964 0.8835964 1.5787520 0.8835964 1.3817806
[3060] 0.8835964 1.0095522 1.0095522 1.0095522 0.8377255 0.8377255 1.0095522
[3067] 1.0095522 1.0095522 1.0095522 1.0095522 0.8377255 0.9571424 1.0095522
[3074] 0.6696901 1.9177880 1.0095522 2.1911668 1.0095522 1.0095522 1.0095522
[3081] 0.6696901 1.5787520 1.0095522 0.8835964 0.8377255 0.8377255 0.8835964
[3088] 1.0095522 1.3817806 0.9571424 1.0095522 1.0095522 1.5787520 0.8835964
[3095] 0.8377255 1.0095522 1.0095522 1.0095522 1.0095522 0.9571424 0.8835964
[3102] 0.9571424 1.5787520 1.0095522 1.5787520 0.8835964 1.3817806 1.0095522
[3109] 1.0095522 0.7651538 1.5787520 1.5787520 0.8835964 0.8835964 0.8377255
[3116] 0.8835964 0.8835964 0.8377255 1.0095522 0.8835964 1.5787520 1.3817806
[3123] 0.8835964 1.0095522 0.8835964 1.0095522 0.8377255 1.0095522 0.7651538
[3130] 1.0095522 1.0095522 0.8835964 0.9571424 1.0095522 1.0095522 1.0095522
[3137] 0.8377255 0.8835964 1.3817806 1.0095522 1.0095522 0.6696901 1.0095522
[3144] 1.5787520 2.1911668 0.8377255 0.8835964 0.8835964 0.9571424 1.5787520
[3151] 1.0095522 0.8835964 0.8835964 1.0095522 1.5787520 1.0095522 1.0095522
[3158] 1.0095522 0.8835964 0.8835964 0.9571424 0.9571424 2.1911668 0.6696901
[3165] 0.9571424 1.0095522 0.9571424 1.0095522 0.8377255 0.8835964 0.7651538
[3172] 0.8835964 1.0095522 1.5787520 0.8377255 1.5787520 1.3817806 1.0095522
[3179] 1.0095522 0.7651538 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[3186] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.6696901 0.8377255
[3193] 1.0095522 0.8835964 1.3817806 1.0095522 1.0095522 0.8835964 2.1911668
[3200] 0.9571424 0.8835964 1.3817806 1.3817806 0.7651538 1.3817806 1.0095522
[3207] 0.8835964 0.8835964 0.8835964 1.9177880 1.5787520 0.7651538 1.0095522
[3214] 1.9177880 1.0095522 1.0095522 0.8835964 1.3817806 1.0095522 0.8835964
[3221] 0.6696901 1.3817806 1.0095522 2.1911668 0.8835964 1.5787520 0.8835964
[3228] 0.8835964 0.8377255 0.8835964 1.3817806 1.5787520 2.1911668 0.9571424
[3235] 0.8835964 1.3817806 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[3242] 0.6696901 0.8377255 0.8835964 0.8377255 0.8835964 0.8835964 1.3817806
[3249] 0.8835964 1.0095522 0.7651538 1.0095522 1.5787520 0.8835964 1.0095522
[3256] 1.0095522 1.0095522 0.8377255 0.9571424 1.0095522 0.8835964 1.0095522
[3263] 0.8835964 0.8835964 1.0095522 0.8835964 0.9571424 1.3817806 1.0095522
[3270] 1.0095522 1.3817806 0.8835964 1.9177880 1.5787520 1.0095522 0.8835964
[3277] 1.0095522 1.3817806 0.8835964 0.8835964 1.5787520 0.9571424 0.8835964
[3284] 0.9571424 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8377255
[3291] 0.8377255 1.5787520 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[3298] 0.8835964 1.0095522 0.8835964 0.8835964 2.1911668 1.0095522 1.0095522
[3305] 1.0095522 0.8835964 2.1911668 1.9177880 1.0095522 1.3817806 0.8835964
[3312] 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 1.5787520 1.0095522
[3319] 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8377255 1.0095522
[3326] 1.0095522 1.0095522 1.3817806 0.9571424 2.1911668 0.8835964 1.0095522
[3333] 0.8835964 1.3817806 1.0095522 1.0095522 1.0095522 1.3817806 0.9571424
[3340] 1.0095522 0.8377255 0.8377255 0.8835964 0.6696901 0.7651538 1.0095522
[3347] 1.0095522 0.8835964 0.8835964 2.1911668 1.0095522 1.0095522 0.8835964
[3354] 0.9571424 1.0095522 1.0095522 0.8835964 0.7651538 0.9571424 0.9571424
[3361] 0.8377255 0.9571424 1.0095522 0.8835964 0.9571424 1.5787520 1.0095522
[3368] 1.5787520 1.3817806 1.0095522 0.8835964 1.0095522 1.0095522 1.5787520
[3375] 0.8377255 1.5787520 1.0095522 1.0095522 1.3817806 1.0095522 0.8835964
[3382] 0.8377255 0.7651538 1.3817806 1.0095522 1.0095522 1.0095522 1.0095522
[3389] 0.8377255 1.0095522 0.8835964 0.8835964 1.0095522 2.1911668 1.0095522
[3396] 0.8835964 0.9571424 1.5787520 1.3817806 1.3817806 0.7651538 0.9571424
[3403] 1.9177880 0.7651538 1.0095522 1.0095522 0.9571424 1.0095522 0.8835964
[3410] 0.9571424 0.8835964 0.8835964 1.0095522 0.8835964 0.9571424 0.9571424
[3417] 0.8835964 1.0095522 0.8835964 0.8835964 0.8377255 1.0095522 0.7651538
[3424] 1.5787520 1.0095522 1.3817806 0.8377255 0.8377255 0.8377255 0.8377255
[3431] 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[3438] 0.8377255 0.8377255 0.8377255 0.8835964 0.8377255 1.9177880 0.9571424
[3445] 0.8377255 0.8377255 0.8835964 1.0095522 0.8835964 0.8377255 0.8377255
[3452] 0.8377255 0.8377255 0.8835964 0.9571424 0.8835964 0.9571424 0.8377255
[3459] 1.0095522 1.3817806 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424
[3466] 0.8377255 0.8377255 0.9571424 0.8835964 0.9571424 0.8377255 0.8377255
[3473] 0.9571424 0.6696901 0.8377255 0.9571424 0.8377255 0.8377255 0.8835964
[3480] 0.8835964 0.9571424 0.8835964 0.8377255 0.8835964 0.8377255 0.9571424
[3487] 1.0095522 0.8377255 0.8835964 0.8377255 0.8377255 0.9571424 0.8835964
[3494] 0.8377255 0.8835964 0.9571424 0.8377255 0.8835964 0.8377255 0.8377255
[3501] 1.0095522 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[3508] 0.6696901 0.8835964 0.8377255 0.9571424 0.9571424 0.8835964 2.1911668
[3515] 1.9177880 0.9571424 0.8377255 1.3817806 0.8377255 0.8835964 0.8377255
[3522] 0.8377255 0.8377255 0.8377255 0.8835964 1.0095522 0.8835964 0.8835964
[3529] 1.0095522 0.8835964 0.9571424 0.8835964 1.0095522 0.8835964 0.9571424
[3536] 0.8377255 0.8377255 0.8835964 0.9571424 0.8377255 0.8835964 0.8835964
[3543] 0.9571424 0.9571424 1.5787520 0.8377255 1.0095522 0.8835964 1.0095522
[3550] 0.8377255 0.8377255 0.8377255 0.8835964 1.0095522 1.0095522 1.0095522
[3557] 0.9571424 0.8835964 0.8377255 1.3817806 0.9571424 0.8835964 1.0095522
[3564] 0.8835964 1.3817806 0.9571424 0.8377255 0.8835964 1.0095522 0.9571424
[3571] 0.8377255 0.9571424 1.0095522 0.9571424 0.9571424 0.8377255 0.9571424
[3578] 0.8835964 0.9571424 0.8377255 0.8377255 0.8377255 0.8835964 0.8377255
[3585] 1.5787520 0.9571424 1.0095522 0.8835964 1.0095522 0.8377255 0.8835964
[3592] 1.3817806 0.7651538 0.8377255 0.8377255 0.8835964 0.9571424 1.0095522
[3599] 1.0095522 0.8377255 1.5787520 1.0095522 0.8377255 0.8835964 0.9571424
[3606] 1.9177880 1.0095522 0.9571424 0.8377255 0.8377255 0.9571424 1.5787520
[3613] 0.9571424 1.5787520 1.9177880 0.8377255 0.8835964 0.8835964 0.8377255
[3620] 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8835964
[3627] 1.3817806 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424 0.6696901
[3634] 0.8835964 0.8835964 0.8835964 0.9571424 1.3817806 0.9571424 0.9571424
[3641] 0.8835964 0.8835964 0.9571424 0.8377255 0.8377255 0.8377255 1.0095522
[3648] 0.8835964 0.6696901 1.0095522 0.8835964 0.9571424 1.0095522 0.8377255
[3655] 1.9177880 0.8835964 0.8835964 0.8377255 1.5787520 1.0095522 0.8835964
[3662] 2.1911668 2.1911668 1.0095522 1.0095522 1.0095522 0.8835964 0.9571424
[3669] 0.8835964 0.8377255 0.9571424 1.9177880 0.8377255 1.5787520 0.9571424
[3676] 1.3817806 0.8835964 0.8377255 0.9571424 0.8835964 0.9571424 0.9571424
[3683] 0.8835964 0.8835964 1.0095522 0.8835964 0.8377255 0.8835964 1.0095522
[3690] 0.8835964 0.8835964 2.1911668 0.9571424 0.8377255 0.9571424 0.9571424
[3697] 0.8377255 0.8835964 1.3817806 0.9571424 0.8835964 0.8835964 0.8377255
[3704] 1.0095522 0.8835964 0.8377255 0.8835964 0.9571424 0.9571424 0.8377255
[3711] 0.8377255 1.0095522 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[3718] 0.8835964 0.8377255 0.8377255 1.0095522 0.9571424 1.0095522 0.8377255
[3725] 0.8835964 0.8377255 0.9571424 1.3817806 1.0095522 0.8377255 0.8377255
[3732] 0.9571424 0.9571424 0.9571424 0.9571424 0.6696901 0.8377255 0.9571424
[3739] 0.8377255 0.8377255 0.8377255 0.8377255 1.0095522 0.8377255 0.9571424
[3746] 0.9571424 0.8835964 2.1911668 0.9571424 0.9571424 0.8377255 0.8377255
[3753] 0.8377255 0.8377255 2.1911668 0.8377255 0.9571424 0.9571424 0.8377255
[3760] 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8835964
[3767] 0.8835964 0.9571424 0.8835964 0.8377255 0.8377255 0.8377255 0.9571424
[3774] 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.8835964 0.8835964
[3781] 1.0095522 0.9571424 0.8377255 1.0095522 0.9571424 0.8835964 0.8377255
[3788] 0.8835964 0.8835964 0.6696901 0.9571424 0.9571424 0.8835964 0.8377255
[3795] 0.8377255 0.8835964 1.0095522 0.9571424 0.8835964 0.8377255 0.8377255
[3802] 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.8835964 0.8835964
[3809] 0.8377255 0.8377255 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[3816] 1.0095522 0.8377255 0.8377255 0.8377255 0.8835964 0.8377255 1.0095522
[3823] 0.8377255 0.8377255 0.8377255 0.8835964 0.8377255 0.6696901 0.8377255
[3830] 0.8377255 1.9177880 0.8377255 0.9571424 0.8835964 0.8835964 1.0095522
[3837] 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255
[3844] 0.9571424 0.8377255 0.8377255 1.0095522 0.8377255 0.9571424 0.9571424
[3851] 0.9571424 0.8377255 1.0095522 0.9571424 0.8835964 0.8377255 2.1911668
[3858] 1.3817806 1.0095522 0.9571424 0.8835964 0.8377255 0.8377255 2.1911668
[3865] 0.8377255 0.9571424 0.9571424 0.8835964 0.8377255 0.8377255 0.9571424
[3872] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 1.0095522
[3879] 1.0095522 0.8835964 1.0095522 1.9177880 0.8377255 0.6696901 0.8377255
[3886] 0.8377255 0.8377255 0.9571424 0.8377255 0.8835964 0.8835964 0.8377255
[3893] 0.8377255 0.8377255 0.8835964 1.0095522 0.9571424 0.8835964 0.9571424
[3900] 0.7651538 0.8377255 0.9571424 2.1911668 0.8377255 0.8377255 0.8377255
[3907] 1.0095522 0.8377255 0.9571424 0.8377255 0.8377255 0.8835964 0.9571424
[3914] 0.8377255 0.9571424 0.9571424 0.8377255 1.9177880 0.9571424 0.8377255
[3921] 2.1911668 0.9571424 0.9571424 0.9571424 0.9571424 1.9177880 0.8377255
[3928] 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8835964
[3935] 0.8835964 0.8377255 0.9571424 1.9177880 0.9571424 0.8377255 0.8835964
[3942] 2.1911668 0.8835964 0.6696901 0.8835964 0.8835964 0.8835964 1.0095522
[3949] 1.0095522 1.3817806 0.8835964 1.0095522 0.8835964 1.5787520 1.0095522
[3956] 0.8377255 0.8377255 1.3817806 1.3817806 1.3817806 1.3817806 1.3817806
[3963] 1.3817806 2.1911668 1.3817806 1.0095522 1.3817806 1.3817806 0.8835964
[3970] 1.3817806 1.3817806 1.3817806 0.8835964 0.9571424 1.3817806 1.3817806
[3977] 1.3817806 0.8835964 1.3817806 1.0095522 1.3817806 1.5787520 1.3817806
[3984] 0.8835964 1.3817806 1.3817806 1.5787520 1.3817806 1.3817806 0.8835964
[3991] 1.5787520 1.3817806 0.9571424 0.8835964 1.3817806 1.3817806 1.3817806
[3998] 1.3817806 1.0095522 0.8835964 1.3817806 1.3817806 1.3817806 1.3817806
[4005] 1.3817806 1.5787520 1.3817806 1.3817806 1.0095522 1.0095522 1.5787520
[4012] 1.0095522 1.3817806 0.8835964 1.0095522 1.5787520 1.3817806 1.0095522
[4019] 1.3817806 1.3817806 1.5787520 1.0095522 0.8835964 1.3817806 1.3817806
[4026] 1.0095522 1.3817806 0.8835964 1.3817806 0.8377255 1.0095522 1.5787520
[4033] 1.3817806 1.3817806 1.3817806 1.3817806 1.5787520 0.8835964 1.3817806
[4040] 1.3817806 0.8835964 0.8377255 1.5787520 1.3817806 1.5787520 1.3817806
[4047] 1.3817806 1.3817806 1.3817806 0.8377255 1.3817806 1.3817806 1.0095522
[4054] 1.0095522 2.1911668 1.3817806 1.0095522 1.5787520 1.5787520 0.8835964
[4061] 1.3817806 1.3817806 0.8377255 0.8835964 0.9571424 0.9571424 0.8377255
[4068] 1.3817806 1.5787520 0.8835964 0.8835964 1.3817806 1.0095522 1.3817806
[4075] 1.3817806 1.5787520 1.3817806 0.8835964 1.5787520 1.3817806 0.8835964
[4082] 1.3817806 1.3817806 1.0095522 1.3817806 1.0095522 1.0095522 0.6696901
[4089] 1.0095522 1.3817806 1.0095522 1.3817806 1.3817806 1.5787520 1.3817806
[4096] 1.0095522 1.0095522 1.5787520 1.5787520 1.3817806 1.3817806 1.0095522
[4103] 1.0095522 1.3817806 0.8835964 1.3817806 0.8835964 1.3817806 1.5787520
[4110] 1.3817806 1.3817806 0.8835964 0.7651538 1.0095522 1.5787520 0.9571424
[4117] 1.5787520 1.5787520 0.8835964 0.8835964 0.8835964 1.3817806 0.8835964
[4124] 1.3817806 1.0095522 0.8835964 1.5787520 1.0095522 1.0095522 0.8835964
[4131] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.3817806 0.6696901
[4138] 0.8835964 1.5787520 1.0095522 1.3817806 1.3817806 1.5787520 1.3817806
[4145] 1.3817806 0.7651538 0.8377255 1.3817806 0.8835964 1.0095522 1.5787520
[4152] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[4159] 0.8835964 1.5787520 1.3817806 1.3817806 1.3817806 1.0095522 1.3817806
[4166] 0.8835964 0.8835964 1.3817806 0.8835964 0.8377255 0.8835964 0.9571424
[4173] 1.5787520 0.6696901 1.3817806 0.8835964 1.0095522 0.8835964 0.7651538
[4180] 0.8835964 0.6696901 0.8835964 1.0095522 1.5787520 0.8835964 1.0095522
[4187] 0.8835964 0.8835964 1.5787520 1.5787520 1.0095522 1.5787520 1.5787520
[4194] 0.8835964 1.3817806 1.3817806 0.9571424 0.8835964 1.0095522 1.3817806
[4201] 0.8835964 0.8835964 1.0095522 0.8835964 1.5787520 1.3817806 0.8835964
[4208] 1.3817806 1.5787520 1.3817806 1.0095522 1.0095522 0.8835964 1.3817806
[4215] 0.8835964 0.8377255 1.3817806 1.3817806 1.3817806 1.0095522 0.8835964
[4222] 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964 0.8835964
[4229] 1.3817806 1.5787520 1.3817806 1.3817806 0.8835964 0.8835964 0.6696901
[4236] 1.3817806 0.8835964 1.0095522 1.0095522 1.5787520 0.9571424 1.0095522
[4243] 1.5787520 1.3817806 1.0095522 0.8835964 0.8835964 1.3817806 1.3817806
[4250] 1.5787520 1.3817806 0.8835964 1.0095522 1.5787520 1.3817806 1.3817806
[4257] 1.3817806 1.3817806 1.0095522 0.8377255 1.3817806 1.0095522 1.3817806
[4264] 1.3817806 1.5787520 1.0095522 0.8835964 0.8377255 0.8377255 1.3817806
[4271] 1.3817806 0.8835964 0.8835964 0.9571424 1.0095522 1.3817806 0.6696901
[4278] 1.5787520 1.3817806 0.9571424 0.8835964 1.3817806 1.3817806 1.3817806
[4285] 0.8835964 1.3817806 1.3817806 1.5787520 1.3817806 0.8835964 1.0095522
[4292] 0.8835964 0.8835964 1.9177880 1.0095522 1.0095522 0.6696901 0.8835964
[4299] 0.6696901 1.9177880 0.8835964 0.8835964 1.3817806 1.3817806 1.3817806
[4306] 0.8835964 1.3817806 1.3817806 0.8377255 1.3817806 0.8835964 0.6696901
[4313] 0.6696901 0.7651538 1.3817806 1.3817806 0.8835964 1.3817806 1.3817806
[4320] 1.3817806 1.3817806 0.8835964 0.8835964 1.3817806 0.8835964 1.0095522
[4327] 1.3817806 0.8835964 0.8835964 1.0095522 1.3817806 1.3817806 1.5787520
[4334] 0.8835964 1.0095522 1.3817806 1.0095522 1.3817806 0.8835964 1.3817806
[4341] 0.6696901 1.0095522 1.0095522 1.3817806 1.3817806 0.8835964 1.0095522
[4348] 0.8835964 1.0095522 0.8835964 1.0095522 1.3817806 1.0095522 1.3817806
[4355] 1.3817806 1.3817806 0.8835964 1.5787520 1.3817806 0.8835964 0.8377255
[4362] 1.0095522 0.8377255 0.8835964 1.0095522 0.6696901 1.0095522 1.0095522
[4369] 1.3817806 1.5787520 0.6696901 0.8835964 1.0095522 1.9177880 1.0095522
[4376] 1.0095522 1.5787520 1.3817806 1.0095522 0.8835964 0.8835964 0.8377255
[4383] 2.1911668 1.3817806 0.8835964 1.3817806 1.5787520 1.3817806 1.3817806
[4390] 1.0095522 1.3817806 1.0095522 0.8835964 0.8835964 1.5787520 0.8835964
[4397] 1.0095522 0.8835964 1.0095522 1.0095522 1.5787520 0.8835964 1.5787520
[4404] 1.3817806 0.8835964 1.0095522 1.0095522 1.0095522 1.5787520 0.8835964
[4411] 0.8835964 0.8835964 0.8377255 1.0095522 1.3817806 1.3817806 1.3817806
[4418] 0.8835964 0.8835964 0.8835964 1.3817806 1.3817806 1.0095522 1.3817806
[4425] 1.3817806 1.3817806 1.0095522 1.3817806 1.0095522 1.0095522 1.3817806
[4432] 1.3817806 0.8835964 2.1911668 1.3817806 1.3817806 1.0095522 0.8835964
[4439] 1.3817806 1.0095522 1.3817806 1.3817806 1.3817806 1.3817806 0.8835964
[4446] 1.5787520 2.1911668 1.3817806 1.0095522 0.8377255 0.8835964 1.0095522
[4453] 1.3817806 1.3817806 1.3817806 0.8835964 0.8377255 0.8835964 0.8835964
[4460] 0.8835964 0.8377255 1.0095522 1.0095522 0.8835964 1.0095522 1.3817806
[4467] 0.8377255 0.8835964 0.6696901 1.3817806 1.3817806 0.8377255 1.3817806
[4474] 1.3817806 1.9177880 1.3817806 0.8835964 0.8377255 1.0095522 0.8835964
[4481] 0.8835964 1.0095522 0.7651538 0.8835964 1.0095522 1.0095522 1.0095522
[4488] 0.8835964 1.0095522 0.8835964 0.8835964 0.7651538 1.0095522 0.8835964
[4495] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[4502] 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522 1.0095522 1.0095522
[4509] 2.1911668 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[4516] 0.8835964 1.0095522 1.9177880 0.8835964 0.8835964 0.8835964 1.3817806
[4523] 1.0095522 1.9177880 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522
[4530] 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[4537] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[4544] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[4551] 0.8835964 0.8835964 0.8377255 0.8835964 0.8835964 0.8835964 1.0095522
[4558] 0.8835964 0.8835964 0.8835964 0.8835964 1.3817806 1.0095522 1.0095522
[4565] 1.9177880 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[4572] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[4579] 1.0095522 1.0095522 1.0095522 1.3817806 1.0095522 1.0095522 0.8835964
[4586] 1.0095522 0.8835964 1.5787520 0.8835964 0.8835964 0.8835964 0.8835964
[4593] 1.3817806 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 1.3817806
[4600] 2.1911668 0.8835964 0.8835964 0.6696901 1.0095522 0.8835964 0.8835964
[4607] 1.5787520 0.8835964 0.6696901 0.8835964 0.8835964 0.6696901 1.0095522
[4614] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[4621] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.3817806
[4628] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8377255 0.8835964
[4635] 1.0095522 1.0095522 1.0095522 0.8835964 0.7651538 1.0095522 0.8835964
[4642] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[4649] 0.8835964 1.0095522 1.9177880 1.0095522 0.8835964 2.1911668 0.8835964
[4656] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522
[4663] 0.6696901 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[4670] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[4677] 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 2.1911668 0.8835964
[4684] 0.8835964 1.0095522 1.0095522 1.0095522 0.6696901 0.8835964 1.0095522
[4691] 0.8835964 0.8835964 1.9177880 0.8835964 1.0095522 1.3817806 0.8835964
[4698] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[4705] 1.0095522 1.0095522 1.0095522 1.0095522 0.6696901 1.0095522 0.8835964
[4712] 0.8835964 0.8835964 1.0095522 0.8835964 1.9177880 0.8835964 1.0095522
[4719] 0.8835964 0.8835964 1.0095522 0.8835964 0.6696901 0.6696901 1.0095522
[4726] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[4733] 0.8835964 1.3817806 1.0095522 1.0095522 0.8377255 1.0095522 0.8377255
[4740] 0.8377255 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964
[4747] 1.3817806 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[4754] 0.8835964 0.8835964 0.8835964 0.6696901 0.8835964 0.9571424 0.8835964
[4761] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8377255
[4768] 1.5787520 0.8835964 2.1911668 0.6696901 2.1911668 1.9177880 1.9177880
[4775] 0.6696901 2.1911668 1.0095522 0.8835964 1.0095522 1.0095522 2.1911668
[4782] 1.9177880 1.0095522 1.9177880 0.6696901 1.0095522 0.8835964 1.9177880
[4789] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[4796] 0.8835964 0.8835964 0.6696901 0.8835964 0.8835964 0.8835964 1.0095522
[4803] 2.1911668 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964 0.8835964
[4810] 0.8835964 1.0095522 0.8835964 0.8377255 1.0095522 0.8835964 0.8835964
[4817] 1.0095522 0.8835964 0.9571424 1.0095522 1.0095522 1.5787520 1.0095522
[4824] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8377255 1.0095522
[4831] 1.0095522 1.9177880 2.1911668 1.0095522 1.0095522 2.1911668 0.8835964
[4838] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[4845] 1.0095522 1.0095522 1.0095522 1.3817806 0.8835964 0.8835964 0.6696901
[4852] 0.8377255 2.1911668 0.8835964 1.9177880 1.0095522 1.9177880 1.0095522
[4859] 1.0095522 0.8835964 0.8835964 0.8835964 0.8377255 0.8835964 0.8835964
[4866] 1.3817806 0.8835964 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522
[4873] 0.8835964 0.8835964 1.0095522 2.1911668 1.0095522 1.0095522 0.9571424
[4880] 1.0095522 2.1911668 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522
[4887] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[4894] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[4901] 0.8835964 0.8835964 0.7651538 0.8835964 1.0095522 0.8835964 0.8835964
[4908] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[4915] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[4922] 0.8835964 0.8835964 1.0095522 0.8835964 0.6696901 0.8835964 1.0095522
[4929] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[4936] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[4943] 1.0095522 0.8835964 1.3817806 0.8835964 1.0095522 0.8835964 1.0095522
[4950] 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522
[4957] 1.0095522 1.0095522 1.9177880 1.0095522 1.0095522 1.0095522 1.3817806
[4964] 1.0095522 0.8835964 0.8835964 0.8835964 0.8377255 0.8835964 0.8835964
[4971] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[4978] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[4985] 0.8835964 0.8835964 0.8377255 0.8835964 1.3817806 1.3817806 1.0095522
[4992] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[4999] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[5006] 1.0095522 0.8377255 0.8835964 0.7651538 1.0095522 0.8835964 0.8835964
[5013] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[5020] 0.8377255 1.0095522 1.0095522 1.0095522 2.1911668 0.8835964 1.0095522
[5027] 0.8835964 0.8835964 1.0095522 1.0095522 1.5787520 0.8835964 0.8835964
[5034] 1.0095522 0.8835964 0.8835964 0.8835964 1.3817806 1.3817806 1.0095522
[5041] 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 1.5787520 1.0095522
[5048] 0.8377255 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[5055] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[5062] 0.9571424 1.0095522 1.0095522 1.9177880 1.3817806 0.8835964 1.9177880
[5069] 0.8377255 1.0095522 0.8835964 0.8835964 0.6696901 0.8835964 1.0095522
[5076] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[5083] 1.0095522 1.9177880 1.3817806 1.9177880 0.8835964 0.8835964 0.8377255
[5090] 1.9177880 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[5097] 1.0095522 1.5787520 1.0095522 0.8835964 1.0095522 0.9571424 0.7651538
[5104] 0.8835964 0.8835964 1.9177880 0.8835964 1.0095522 0.8835964 0.8835964
[5111] 0.8835964 1.0095522 0.8835964 1.9177880 0.8835964 1.9177880 2.1911668
[5118] 1.0095522 1.0095522 0.9571424 1.0095522 0.8835964 1.0095522 0.8835964
[5125] 0.8835964 1.3817806 1.0095522 0.8835964 0.8835964 0.8835964 0.9571424
[5132] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[5139] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.6696901
[5146] 1.0095522 0.8835964 0.8835964 1.3817806 0.8377255 0.8835964 0.8835964
[5153] 0.8835964 1.0095522 0.8835964 0.7651538 0.8835964 0.7651538 1.0095522
[5160] 1.0095522 0.8835964 0.8835964 0.8835964 0.6696901 0.8835964 1.0095522
[5167] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[5174] 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[5181] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[5188] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[5195] 1.0095522 1.0095522 0.8835964 1.3817806 1.0095522 0.8835964 0.8835964
[5202] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.9571424
[5209] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[5216] 0.6696901 1.0095522 0.8835964 0.8835964 1.3817806 1.0095522 0.8835964
[5223] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[5230] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[5237] 0.8835964 2.1911668 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[5244] 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964 0.8835964 0.8835964
[5251] 1.9177880 1.0095522 1.3817806 0.8835964 1.0095522 0.8835964 1.0095522
[5258] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[5265] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.5787520 0.8835964
[5272] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[5279] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522
[5286] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[5293] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[5300] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522
[5307] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[5314] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[5321] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.6696901
[5328] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[5335] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[5342] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[5349] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[5356] 1.9177880 1.0095522 0.8835964 0.8835964 1.0095522 0.8377255 1.0095522
[5363] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[5370] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[5377] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[5384] 1.0095522 1.0095522 1.0095522 1.3817806 0.8835964 0.8835964 1.0095522
[5391] 0.8835964 0.8835964 0.8835964 0.8835964 0.8377255 1.5787520 0.8835964
[5398] 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[5405] 1.0095522 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522 1.0095522
[5412] 0.8835964 0.8835964 0.8835964 1.3817806 1.0095522 1.0095522 1.0095522
[5419] 0.8835964 1.0095522 1.9177880 0.8835964 0.8835964 0.8835964 1.0095522
[5426] 1.5787520 0.8835964 0.9571424 1.0095522 2.1911668 1.0095522 0.9571424
[5433] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[5440] 1.0095522 1.0095522 0.8835964 2.1911668 0.8835964 1.9177880 1.0095522
[5447] 0.8835964 0.8377255 1.3817806 2.1911668 0.8835964 0.8835964 1.5787520
[5454] 1.3817806 0.8835964 1.5787520 0.8835964 0.8835964 1.0095522 0.8835964
[5461] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.5787520 0.8835964
[5468] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[5475] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[5482] 1.0095522 0.8835964 1.3817806 0.8835964 1.0095522 0.8835964 1.0095522
[5489] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[5496] 1.0095522 0.8835964 1.0095522 0.8835964 1.3817806 1.5787520 0.8835964
[5503] 1.0095522 0.8835964 1.0095522 1.3817806 1.0095522 0.8835964 0.8835964
[5510] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.6696901 0.8835964
[5517] 0.8835964 0.8835964 1.5787520 0.8835964 1.0095522 1.0095522 0.8835964
[5524] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[5531] 0.8835964 1.0095522 1.5787520 0.8835964 1.0095522 1.3817806 1.9177880
[5538] 0.8835964 1.9177880 1.0095522 1.0095522 2.1911668 0.8835964 0.7651538
[5545] 1.3817806 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.3817806
[5552] 1.0095522 0.8835964 1.0095522 0.7651538 0.8835964 1.0095522 0.8835964
[5559] 0.8377255 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[5566] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.3817806 1.5787520
[5573] 1.0095522 1.0095522 1.3817806 1.3817806 1.0095522 0.8835964 0.8835964
[5580] 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[5587] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.6696901 0.8835964
[5594] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[5601] 0.6696901 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[5608] 0.8835964 1.0095522 1.0095522 0.8377255 1.0095522 0.8835964 1.3817806
[5615] 1.3817806 1.5787520 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[5622] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.3817806
[5629] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964
[5636] 1.3817806 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[5643] 1.0095522 1.5787520 0.8835964 0.8835964 1.3817806 1.0095522 0.8835964
[5650] 1.0095522 1.3817806 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[5657] 1.0095522 0.8835964 0.8835964 0.8835964 1.3817806 0.8835964 0.8835964
[5664] 1.0095522 0.8835964 2.1911668 0.8835964 1.3817806 0.6696901 0.8835964
[5671] 0.8835964 1.3817806 2.1911668 0.8835964 0.6696901 0.8835964 1.3817806
[5678] 0.8377255 1.0095522 1.5787520 1.0095522 1.0095522 1.3817806 1.5787520
[5685] 1.0095522 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522
[5692] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 2.1911668 0.6696901
[5699] 1.0095522 1.0095522 1.3817806 0.7651538 0.8835964 0.8835964 1.3817806
[5706] 0.8835964 1.5787520 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[5713] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[5720] 1.0095522 2.1911668 1.5787520 1.0095522 0.8835964 0.6696901 1.3817806
[5727] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[5734] 1.0095522 1.3817806 0.8835964 0.8835964 0.8835964 0.6696901 0.8835964
[5741] 1.0095522 0.8835964 1.0095522 1.3817806 1.0095522 1.0095522 0.8835964
[5748] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[5755] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.5787520 1.0095522
[5762] 0.8835964 1.3817806 1.0095522 1.5787520 0.8835964 0.8835964 1.0095522
[5769] 1.3817806 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.3817806
[5776] 1.5787520 0.8835964 0.8835964 1.0095522 0.6696901 1.3817806 1.9177880
[5783] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.5787520
[5790] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[5797] 1.0095522 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522
[5804] 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 1.0095522 1.3817806
[5811] 1.0095522 0.8835964 1.0095522 1.3817806 0.8835964 0.8835964 1.0095522
[5818] 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 1.5787520 0.8835964
[5825] 0.8835964 1.5787520 0.8835964 0.8835964 1.0095522 1.0095522 1.5787520
[5832] 1.0095522 1.3817806 0.8835964 1.0095522 1.0095522 0.8835964 0.9571424
[5839] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.3817806
[5846] 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 1.0095522 0.8377255
[5853] 0.7651538 1.3817806 0.8835964 0.8835964 1.3817806 0.8835964 0.8835964
[5860] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 1.3817806 1.0095522
[5867] 1.5787520 0.8835964 0.9571424 1.0095522 1.0095522 0.8835964 1.0095522
[5874] 0.8835964 1.0095522 0.8835964 0.8835964 0.7651538 1.0095522 1.0095522
[5881] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[5888] 1.3817806 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[5895] 0.9571424 1.3817806 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[5902] 1.0095522 0.8377255 0.8377255 1.3817806 1.0095522 0.8835964 0.8835964
[5909] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[5916] 1.3817806 0.8835964 0.9571424 1.0095522 0.8835964 0.8835964 0.8835964
[5923] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.9571424 1.0095522
[5930] 1.0095522 1.0095522 1.0095522 1.0095522 0.6696901 1.0095522 1.0095522
[5937] 0.6696901 0.8835964 0.8377255 1.3817806 1.0095522 0.8377255 0.8835964
[5944] 1.0095522 0.8835964 1.9177880 1.0095522 0.8835964 0.8835964 0.8835964
[5951] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964
[5958] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8377255 1.0095522
[5965] 1.3817806 2.1911668 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[5972] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[5979] 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[5986] 1.3817806 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[5993] 0.8835964 0.8835964 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522
[6000] 0.8835964 0.8835964 0.8835964 1.5787520 0.8835964 1.0095522 2.1911668
[6007] 1.3817806 1.3817806 0.9571424 0.8835964 1.0095522 1.0095522 0.8835964
[6014] 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522 1.3817806 0.8835964
[6021] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 2.1911668
[6028] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[6035] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.6696901 1.0095522
[6042] 0.8835964 1.0095522 1.5787520 1.0095522 0.8377255 0.8835964 0.8835964
[6049] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[6056] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.9571424
[6063] 1.9177880 0.8835964 1.5787520 1.5787520 1.0095522 1.0095522 1.0095522
[6070] 1.0095522 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522 0.8835964
[6077] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.5787520 1.3817806
[6084] 0.8835964 0.8835964 1.3817806 1.0095522 0.6696901 0.8835964 0.6696901
[6091] 1.0095522 1.3817806 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[6098] 0.8835964 0.8835964 0.8835964 0.8835964 1.3817806 0.8835964 1.3817806
[6105] 2.1911668 1.0095522 0.8835964 1.3817806 0.8835964 0.8835964 1.0095522
[6112] 0.8835964 1.3817806 0.8835964 1.0095522 0.8835964 0.8835964 0.6696901
[6119] 1.3817806 0.9571424 1.0095522 1.3817806 0.8835964 0.8377255 1.0095522
[6126] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[6133] 1.5787520 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[6140] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[6147] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[6154] 1.5787520 1.0095522 0.8835964 1.5787520 1.0095522 1.0095522 1.0095522
[6161] 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522
[6168] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[6175] 0.8835964 2.1911668 1.3817806 0.8835964 0.8835964 0.8835964 1.0095522
[6182] 1.3817806 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[6189] 1.0095522 0.6696901 0.6696901 0.8835964 0.8835964 0.8835964 0.8835964
[6196] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[6203] 1.3817806 1.3817806 0.8835964 0.8835964 2.1911668 1.3817806 0.8835964
[6210] 0.8835964 1.0095522 0.8835964 0.8835964 1.3817806 0.8835964 0.8835964
[6217] 0.8835964 0.8835964 0.8835964 1.0095522 1.3817806 0.7651538 0.8835964
[6224] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 1.5787520
[6231] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[6238] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8377255 0.8835964
[6245] 0.8835964 0.8835964 0.6696901 1.3817806 0.8835964 0.8835964 0.8835964
[6252] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[6259] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[6266] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 2.1911668 1.0095522
[6273] 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[6280] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.5787520
[6287] 0.8835964 0.8377255 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[6294] 1.0095522 0.6696901 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[6301] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[6308] 1.0095522 0.8835964 1.0095522 0.8835964 0.7651538 1.0095522 0.8835964
[6315] 1.0095522 0.8377255 0.7651538 0.8377255 0.8835964 0.8835964 1.0095522
[6322] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[6329] 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.5787520
[6336] 1.0095522 2.1911668 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[6343] 0.8835964 1.0095522 1.0095522 0.8835964 0.6696901 0.8835964 0.8835964
[6350] 1.0095522 1.0095522 1.0095522 1.5787520 0.8835964 1.0095522 0.8835964
[6357] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522
[6364] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[6371] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[6378] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[6385] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[6392] 0.7651538 0.8835964 0.7651538 0.8835964 0.8835964 1.0095522 1.0095522
[6399] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.7651538 1.0095522
[6406] 0.8835964 0.8835964 0.8377255 0.8835964 0.8835964 1.0095522 1.0095522
[6413] 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[6420] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[6427] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[6434] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 2.1911668
[6441] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[6448] 1.3817806 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[6455] 0.8835964 0.8835964 1.3817806 1.3817806 0.8835964 1.0095522 0.8835964
[6462] 0.8835964 1.0095522 0.8835964 0.8377255 0.8835964 0.8835964 0.8835964
[6469] 1.0095522 1.3817806 0.8835964 0.8835964 1.0095522 1.0095522 1.3817806
[6476] 0.9571424 0.8835964 0.8835964 1.0095522 1.9177880 0.8835964 1.0095522
[6483] 1.3817806 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[6490] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[6497] 1.0095522 2.1911668 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[6504] 0.8835964 1.3817806 0.8377255 0.8835964 0.8835964 0.8835964 0.8835964
[6511] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[6518] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[6525] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[6532] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[6539] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[6546] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 1.3817806
[6553] 0.8835964 1.0095522 1.0095522 1.0095522 0.8377255 0.8835964 1.3817806
[6560] 0.8835964 1.3817806 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522
[6567] 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 0.7651538 0.7651538
[6574] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.5787520 1.0095522
[6581] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[6588] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.9177880
[6595] 1.0095522 0.8835964 0.8377255 0.8835964 0.8835964 1.0095522 1.0095522
[6602] 0.8835964 0.8835964 0.7651538 0.8835964 0.8835964 0.8835964 1.0095522
[6609] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[6616] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[6623] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[6630] 0.8377255 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522
[6637] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[6644] 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.9571424
[6651] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[6658] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[6665] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[6672] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.6696901 1.0095522
[6679] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[6686] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[6693] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[6700] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[6707] 0.8835964 1.0095522 0.8835964 0.8377255 0.8835964 1.3817806 0.8835964
[6714] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522
[6721] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[6728] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[6735] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.9177880 1.0095522
[6742] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[6749] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[6756] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[6763] 0.8835964 1.0095522 2.1911668 0.6696901 0.8835964 0.8835964 0.8835964
[6770] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[6777] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522
[6784] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[6791] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 2.1911668 0.8835964
[6798] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[6805] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[6812] 1.0095522 1.0095522 1.0095522 1.0095522 1.5787520 0.8835964 0.8835964
[6819] 0.8835964 0.8835964 0.8835964 1.0095522 2.1911668 0.8835964 0.8835964
[6826] 0.8835964 1.0095522 2.1911668 1.0095522 1.0095522 0.8835964 1.0095522
[6833] 1.0095522 0.8835964 0.8835964 0.8835964 1.5787520 0.8835964 0.8835964
[6840] 1.3817806 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[6847] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522
[6854] 1.9177880 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[6861] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522
[6868] 0.8835964 2.1911668 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[6875] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[6882] 1.0095522 0.8835964 1.0095522 1.0095522 0.9571424 0.8835964 1.0095522
[6889] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[6896] 0.8835964 0.8835964 2.1911668 0.8835964 1.0095522 0.8835964 0.8835964
[6903] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522
[6910] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[6917] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8377255 0.8835964
[6924] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[6931] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8377255
[6938] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[6945] 0.6696901 1.9177880 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[6952] 1.9177880 1.0095522 1.9177880 0.8835964 1.0095522 0.8835964 0.8835964
[6959] 1.9177880 1.3817806 0.8835964 0.8835964 0.8377255 1.0095522 1.0095522
[6966] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[6973] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[6980] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[6987] 0.8835964 1.3817806 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[6994] 1.3817806 0.8835964 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964
[7001] 0.8835964 1.0095522 1.0095522 0.6696901 0.8835964 1.0095522 1.0095522
[7008] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[7015] 2.1911668 0.8835964 0.7651538 0.9571424 0.7651538 1.3817806 0.8835964
[7022] 1.0095522 0.8835964 1.9177880 0.9571424 0.8835964 0.8835964 0.8835964
[7029] 2.1911668 1.5787520 0.8377255 1.0095522 0.7651538 2.1911668 1.0095522
[7036] 0.8835964 1.0095522 0.8835964 2.1911668 1.0095522 0.8835964 0.8835964
[7043] 1.3817806 0.6696901 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[7050] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[7057] 0.8835964 0.8835964 1.0095522 0.6696901 0.8835964 1.0095522 0.8835964
[7064] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[7071] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.9177880
[7078] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.7651538
[7085] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.6696901 0.8835964
[7092] 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964 1.5787520 1.0095522
[7099] 0.8835964 1.9177880 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[7106] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[7113] 0.7651538 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[7120] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[7127] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964
[7134] 0.8377255 0.8835964 0.8377255 0.8835964 0.6696901 1.0095522 1.0095522
[7141] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522
[7148] 0.8835964 1.0095522 0.8835964 0.8377255 1.0095522 1.0095522 0.8835964
[7155] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[7162] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[7169] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522
[7176] 0.8835964 0.8835964 0.8835964 0.8835964 1.9177880 0.8835964 0.8835964
[7183] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.6696901 0.8835964
[7190] 1.0095522 1.0095522 1.0095522 0.7651538 1.0095522 0.8835964 1.0095522
[7197] 0.8835964 1.0095522 1.3817806 1.0095522 1.0095522 2.1911668 0.8835964
[7204] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[7211] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[7218] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[7225] 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 2.1911668 0.8835964
[7232] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[7239] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[7246] 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 2.1911668 1.3817806
[7253] 1.0095522 1.0095522 0.8377255 0.6696901 1.5787520 1.3817806 1.0095522
[7260] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[7267] 1.3817806 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[7274] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[7281] 1.0095522 0.8835964 1.3817806 1.0095522 0.8835964 0.8835964 0.8835964
[7288] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[7295] 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.6696901 0.8835964
[7302] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.3817806
[7309] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[7316] 1.9177880 1.3817806 0.8835964 0.8835964 0.8835964 1.0095522 0.8377255
[7323] 0.7651538 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[7330] 0.9571424 0.8377255 0.6696901 1.0095522 0.6696901 0.6696901 1.0095522
[7337] 0.8835964 0.8835964 1.0095522 0.8835964 0.6696901 0.8835964 0.8835964
[7344] 0.8835964 0.7651538 1.0095522 0.9571424 1.3817806 0.6696901 0.8835964
[7351] 0.8377255 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522
[7358] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[7365] 0.8835964 0.8835964 1.0095522 1.0095522 0.6696901 0.8835964 0.8835964
[7372] 0.6696901 1.0095522 0.9571424 0.8835964 1.3817806 1.0095522 1.3817806
[7379] 0.8377255 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[7386] 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 0.9571424 1.0095522
[7393] 1.0095522 0.8377255 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[7400] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[7407] 1.3817806 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522
[7414] 1.0095522 1.0095522 0.6696901 0.8377255 1.0095522 1.0095522 0.8835964
[7421] 0.8835964 1.0095522 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522
[7428] 0.8835964 1.0095522 1.0095522 1.9177880 0.8835964 0.8835964 0.8835964
[7435] 0.8835964 1.9177880 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[7442] 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[7449] 1.0095522 1.3817806 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[7456] 1.0095522 1.3817806 0.8835964 0.8835964 1.5787520 1.0095522 2.1911668
[7463] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[7470] 0.8835964 0.8835964 0.8377255 0.8835964 1.0095522 0.8835964 0.8835964
[7477] 1.3817806 0.8835964 1.5787520 0.8835964 0.8377255 0.8835964 0.8835964
[7484] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[7491] 1.0095522 1.0095522 0.8835964 0.8377255 1.0095522 1.0095522 0.8377255
[7498] 1.0095522 1.0095522 0.8835964 1.0095522 1.5787520 0.8377255 1.3817806
[7505] 0.7651538 0.8377255 0.6696901 1.0095522 0.8835964 1.3817806 1.5787520
[7512] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[7519] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[7526] 1.0095522 1.9177880 0.8835964 0.8835964 0.9571424 0.8835964 1.0095522
[7533] 1.3817806 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522
[7540] 1.0095522 0.8835964 1.0095522 0.8835964 0.9571424 1.0095522 1.0095522
[7547] 1.0095522 0.8835964 1.0095522 0.8835964 0.9571424 0.8835964 1.0095522
[7554] 1.0095522 0.8377255 1.0095522 1.0095522 0.8377255 0.8835964 1.0095522
[7561] 1.0095522 0.8835964 0.8377255 0.8377255 1.0095522 0.8835964 1.9177880
[7568] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[7575] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522
[7582] 1.3817806 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[7589] 0.6696901 0.9571424 0.8835964 1.0095522 0.6696901 0.6696901 1.0095522
[7596] 0.9571424 0.8835964 1.0095522 0.8835964 0.7651538 0.8835964 0.8835964
[7603] 0.8835964 0.8835964 0.9571424 0.8835964 1.0095522 1.0095522 0.8835964
[7610] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522
[7617] 1.5787520 0.8377255 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522
[7624] 0.8835964 0.8835964 0.8377255 0.8835964 1.0095522 0.8835964 0.8835964
[7631] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[7638] 0.6696901 0.6696901 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[7645] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[7652] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8377255
[7659] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[7666] 0.8835964 0.8835964 0.6696901 1.0095522 0.8835964 1.0095522 1.0095522
[7673] 1.0095522 1.0095522 0.8835964 0.9571424 0.8835964 0.8835964 0.8835964
[7680] 1.0095522 1.0095522 1.0095522 0.7651538 1.0095522 1.0095522 0.8835964
[7687] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[7694] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[7701] 1.0095522 1.0095522 1.0095522 0.7651538 0.8835964 1.9177880 0.7651538
[7708] 0.6696901 0.9571424 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[7715] 0.8835964 1.0095522 1.0095522 0.6696901 0.8835964 1.0095522 1.0095522
[7722] 0.8835964 1.0095522 0.6696901 0.8835964 1.0095522 0.8835964 1.0095522
[7729] 0.8835964 1.0095522 1.0095522 1.0095522 2.1911668 1.0095522 1.0095522
[7736] 0.8835964 2.1911668 1.0095522 0.8835964 0.8835964 1.0095522 0.8377255
[7743] 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[7750] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964
[7757] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[7764] 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522
[7771] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[7778] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[7785] 1.9177880 1.0095522 0.8835964 0.9571424 0.8835964 1.0095522 0.8377255
[7792] 0.8377255 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[7799] 1.3817806 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[7806] 0.9571424 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8377255
[7813] 0.8835964 1.0095522 0.7651538 0.8835964 1.0095522 0.8835964 1.0095522
[7820] 1.0095522 0.8835964 1.0095522 0.8835964 1.3817806 0.8835964 1.0095522
[7827] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[7834] 2.1911668 1.0095522 0.8835964 0.8835964 1.0095522 0.8377255 1.0095522
[7841] 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964
[7848] 1.0095522 1.0095522 0.8377255 1.3817806 0.9571424 0.8835964 0.8835964
[7855] 1.0095522 1.9177880 1.9177880 0.6696901 1.0095522 0.8377255 0.9571424
[7862] 1.9177880 1.0095522 0.8835964 0.8835964 1.3817806 1.5787520 1.0095522
[7869] 0.8835964 1.9177880 1.0095522 0.8835964 0.7651538 0.7651538 0.8835964
[7876] 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964
[7883] 0.6696901 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[7890] 0.8835964 1.3817806 1.9177880 1.9177880 2.1911668 1.9177880 1.0095522
[7897] 0.8835964 1.0095522 0.7651538 0.8377255 1.0095522 0.8835964 1.0095522
[7904] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964
[7911] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522
[7918] 0.8835964 0.8835964 0.8835964 1.9177880 0.8377255 1.0095522 2.1911668
[7925] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.9571424
[7932] 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.9571424
[7939] 1.0095522 0.8377255 0.8835964 1.9177880 0.9571424 0.8835964 0.8377255
[7946] 0.6696901 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 2.1911668
[7953] 0.8835964 1.0095522 2.1911668 1.0095522 2.1911668 0.9571424 1.9177880
[7960] 0.8835964 0.8835964 1.9177880 0.8377255 0.8835964 1.0095522 0.8835964
[7967] 0.8835964 0.9571424 0.8835964 0.8377255 0.8835964 0.8835964 1.0095522
[7974] 0.8835964 1.0095522 0.6696901 1.0095522 1.0095522 2.1911668 1.9177880
[7981] 2.1911668 2.1911668 1.0095522 1.9177880 0.6696901 0.7651538 0.8835964
[7988] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964
[7995] 1.5787520 0.9571424 0.8835964 0.8835964 1.5787520 1.0095522 0.8835964
[8002] 1.9177880 1.9177880 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[8009] 0.8835964 1.9177880 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[8016] 0.6696901 2.1911668 0.8835964 1.0095522 0.8377255 1.0095522 1.0095522
[8023] 1.0095522 1.9177880 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[8030] 0.8835964 1.0095522 1.9177880 0.8835964 1.0095522 2.1911668 0.8835964
[8037] 1.9177880 1.0095522 2.1911668 1.0095522 0.8835964 1.0095522 0.8835964
[8044] 0.8835964 0.8835964 0.8835964 2.1911668 1.0095522 0.8835964 0.8835964
[8051] 1.0095522 0.8835964 2.1911668 2.1911668 1.9177880 0.8835964 1.0095522
[8058] 1.0095522 2.1911668 0.8835964 1.0095522 0.8835964 1.9177880 2.1911668
[8065] 0.8835964 0.6696901 0.8835964 0.8835964 0.8835964 0.9571424 0.7651538
[8072] 1.0095522 1.0095522 0.8377255 1.0095522 0.8835964 0.8835964 1.0095522
[8079] 0.8835964 2.1911668 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964
[8086] 0.8835964 0.8835964 1.0095522 0.7651538 2.1911668 0.8835964 1.0095522
[8093] 1.9177880 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[8100] 1.0095522 0.8835964 0.8835964 2.1911668 0.9571424 2.1911668 1.0095522
[8107] 0.8835964 0.8377255 1.0095522 2.1911668 0.8377255 0.8835964 0.8377255
[8114] 1.0095522 1.0095522 1.0095522 1.3817806 1.0095522 1.9177880 0.8835964
[8121] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[8128] 0.8835964 2.1911668 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[8135] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964
[8142] 0.8835964 0.8835964 1.0095522 1.3817806 1.0095522 0.8835964 1.0095522
[8149] 0.8835964 0.9571424 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[8156] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[8163] 0.8835964 1.9177880 1.9177880 0.8377255 2.1911668 1.0095522 1.9177880
[8170] 2.1911668 2.1911668 2.1911668 0.9571424 0.6696901 0.8835964 2.1911668
[8177] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[8184] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522
[8191] 0.7651538 1.0095522 1.0095522 1.0095522 1.0095522 0.9571424 0.8835964
[8198] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[8205] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.0095522 0.8377255
[8212] 1.0095522 0.8835964 0.8377255 0.8835964 1.0095522 1.0095522 0.8835964
[8219] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[8226] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.6696901 0.8835964
[8233] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964
[8240] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[8247] 0.8835964 1.0095522 1.9177880 1.0095522 0.8835964 0.8835964 0.8835964
[8254] 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.7651538 0.6696901
[8261] 0.8835964 1.0095522 0.8835964 1.0095522 2.1911668 0.8835964 2.1911668
[8268] 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[8275] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964
[8282] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 2.1911668 1.0095522
[8289] 0.8835964 0.7651538 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522
[8296] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[8303] 0.8835964 0.8835964 2.1911668 0.8835964 0.8835964 1.0095522 0.8835964
[8310] 0.8835964 0.8835964 0.7651538 1.0095522 1.0095522 0.8377255 0.8835964
[8317] 0.8835964 0.8835964 0.8835964 1.0095522 0.9571424 0.8835964 1.3817806
[8324] 1.0095522 1.0095522 1.0095522 0.8377255 0.8835964 0.8835964 0.8835964
[8331] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[8338] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522
[8345] 0.8835964 0.8835964 0.6696901 1.0095522 0.8835964 0.8835964 1.0095522
[8352] 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 0.6696901 0.8835964
[8359] 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[8366] 1.0095522 0.8835964 0.8835964 1.3817806 1.0095522 1.0095522 0.8835964
[8373] 1.0095522 0.8835964 1.0095522 0.8835964 0.8377255 1.0095522 0.8835964
[8380] 1.0095522 0.8835964 1.3817806 0.8835964 1.0095522 0.8835964 0.8835964
[8387] 0.8835964 1.0095522 0.8835964 0.8835964 0.8377255 0.8835964 0.8835964
[8394] 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964 0.8835964 0.8835964
[8401] 0.8835964 0.9571424 0.8835964 0.8835964 0.8835964 0.9571424 0.8835964
[8408] 0.8835964 0.8835964 1.0095522 0.8835964 0.8377255 0.8835964 0.6696901
[8415] 1.0095522 1.0095522 0.8835964 0.8835964 0.9571424 0.9571424 0.8835964
[8422] 1.0095522 0.8835964 0.8835964 0.8835964 0.8377255 2.1911668 0.8835964
[8429] 1.0095522 0.8835964 0.9571424 0.8835964 0.8835964 1.0095522 0.8835964
[8436] 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964 0.7651538
[8443] 0.8835964 0.8835964 1.0095522 0.8835964 1.3817806 1.3817806 0.8835964
[8450] 0.8835964 0.8835964 1.3817806 1.0095522 0.8835964 0.8835964 0.8835964
[8457] 0.9571424 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[8464] 1.3817806 0.8835964 0.8835964 0.8835964 0.8377255 1.3817806 0.8835964
[8471] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[8478] 1.0095522 1.0095522 1.0095522 0.8835964 1.9177880 0.9571424 1.0095522
[8485] 0.9571424 1.0095522 0.8835964 1.0095522 0.8835964 0.8377255 1.9177880
[8492] 0.6696901 1.0095522 0.8835964 0.7651538 1.3817806 0.8835964 1.0095522
[8499] 0.8835964 1.0095522 1.0095522 0.7651538 1.3817806 2.1911668 1.0095522
[8506] 1.3817806 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[8513] 0.8835964 0.8377255 1.5787520 0.8835964 0.8835964 1.0095522 1.0095522
[8520] 1.3817806 0.8835964 1.0095522 0.8835964 0.9571424 0.8835964 0.8835964
[8527] 0.8835964 1.0095522 0.8835964 1.3817806 0.8835964 0.8835964 2.1911668
[8534] 2.1911668 0.8835964 0.8835964 1.3817806 0.8835964 2.1911668 0.8835964
[8541] 0.8835964 0.8835964 0.8377255 1.0095522 0.9571424 0.8377255 0.8835964
[8548] 0.8835964 1.0095522 1.3817806 0.8377255 0.8835964 0.6696901 1.0095522
[8555] 0.8377255 1.3817806 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[8562] 0.8835964 1.0095522 0.8377255 1.0095522 1.0095522 1.0095522 1.0095522
[8569] 1.0095522 0.9571424 1.3817806 0.8835964 0.8835964 0.8835964 0.8835964
[8576] 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 1.3817806 0.8377255
[8583] 0.8377255 0.6696901 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522
[8590] 0.8835964 0.8835964 1.3817806 0.8835964 0.8835964 0.6696901 0.8835964
[8597] 1.5787520 1.0095522 0.8835964 1.0095522 1.5787520 1.5787520 1.0095522
[8604] 1.0095522 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255
[8611] 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255
[8618] 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.9571424
[8625] 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424
[8632] 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424
[8639] 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[8646] 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424
[8653] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424
[8660] 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424
[8667] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[8674] 0.9571424 0.9571424 0.9571424 0.8377255 0.8835964 0.8377255 0.9571424
[8681] 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255
[8688] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[8695] 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255
[8702] 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255
[8709] 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424
[8716] 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424
[8723] 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255
[8730] 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255
[8737] 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255
[8744] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[8751] 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[8758] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[8765] 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255
[8772] 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424
[8779] 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[8786] 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255
[8793] 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.9571424
[8800] 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255
[8807] 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424
[8814] 0.8377255 0.9571424 0.9571424 1.0095522 1.0095522 0.8835964 0.8835964
[8821] 1.0095522 0.9571424 0.8835964 0.8377255 0.8377255 1.0095522 0.8835964
[8828] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 1.0095522
[8835] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[8842] 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522 0.7651538 1.0095522
[8849] 0.8835964 1.0095522 0.9571424 2.1911668 1.5787520 0.8835964 0.8835964
[8856] 0.8835964 0.8835964 1.3817806 0.8835964 0.8835964 1.5787520 1.3817806
[8863] 1.0095522 1.0095522 1.0095522 0.8377255 0.8835964 0.9571424 0.8835964
[8870] 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 1.5787520 0.8835964
[8877] 1.5787520 1.3817806 0.8377255 0.8835964 1.0095522 1.3817806 0.8835964
[8884] 0.8835964 1.5787520 1.0095522 0.8835964 1.0095522 1.0095522 0.8377255
[8891] 1.0095522 1.3817806 0.8835964 1.0095522 0.8835964 0.8835964 1.3817806
[8898] 1.0095522 1.0095522 1.3817806 1.0095522 0.8835964 1.3817806 1.0095522
[8905] 0.8835964 0.8377255 0.8835964 0.8835964 1.0095522 0.8835964 0.8835964
[8912] 1.0095522 1.9177880 1.0095522 1.0095522 0.8835964 1.0095522 0.9571424
[8919] 0.8835964 1.3817806 0.8835964 1.3817806 1.0095522 0.6696901 1.3817806
[8926] 0.8835964 0.8835964 0.8835964 0.8835964 0.9571424 1.0095522 0.8377255
[8933] 1.0095522 1.0095522 0.8835964 1.5787520 1.5787520 1.3817806 0.8835964
[8940] 0.8835964 0.8835964 0.8835964 0.8835964 0.8377255 1.0095522 0.9571424
[8947] 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964 0.6696901 0.6696901
[8954] 0.8835964 1.3817806 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[8961] 0.8835964 0.8377255 0.8835964 1.3817806 1.0095522 1.0095522 0.9571424
[8968] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522
[8975] 0.8835964 1.0095522 1.0095522 0.8377255 2.1911668 0.8835964 0.8835964
[8982] 1.3817806 1.0095522 1.0095522 1.0095522 0.8835964 0.6696901 2.1911668
[8989] 1.0095522 1.3817806 0.8835964 2.1911668 0.8835964 0.9571424 1.0095522
[8996] 0.8835964 0.8835964 1.0095522 0.8835964 0.8377255 1.0095522 1.0095522
[9003] 0.7651538 0.8835964 1.9177880 1.0095522 0.8835964 0.8377255 0.8835964
[9010] 0.8835964 1.0095522 1.3817806 1.3817806 0.8835964 0.8835964 0.8835964
[9017] 0.8835964 0.8377255 0.8835964 0.8835964 0.6696901 1.0095522 0.8835964
[9024] 1.0095522 1.0095522 1.0095522 1.0095522 0.7651538 0.8835964 0.8835964
[9031] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.6696901
[9038] 1.0095522 1.5787520 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[9045] 1.0095522 0.8377255 1.0095522 1.9177880 0.8835964 1.0095522 0.8835964
[9052] 0.9571424 0.8835964 0.8835964 1.5787520 0.8835964 0.8835964 0.8835964
[9059] 1.0095522 1.0095522 0.8835964 0.8835964 1.5787520 0.8835964 0.8835964
[9066] 2.1911668 0.6696901 1.0095522 1.0095522 0.8835964 1.3817806 1.3817806
[9073] 0.8835964 0.8835964 0.8835964 2.1911668 1.3817806 0.8835964 0.8835964
[9080] 0.8835964 1.0095522 1.0095522 0.8835964 0.9571424 0.8835964 0.8835964
[9087] 1.3817806 1.3817806 1.0095522 0.8835964 1.3817806 0.9571424 0.8835964
[9094] 1.0095522 0.8835964 1.0095522 0.7651538 1.0095522 0.8835964 1.0095522
[9101] 1.0095522 0.8835964 0.8835964 0.8377255 1.0095522 0.8835964 0.8835964
[9108] 1.0095522 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964 0.8835964
[9115] 1.3817806 1.5787520 1.0095522 0.8377255 0.8835964 0.8835964 0.8835964
[9122] 0.8835964 0.8377255 0.8835964 1.0095522 0.8835964 0.8835964 1.5787520
[9129] 0.8835964 1.0095522 0.9571424 1.0095522 0.8835964 1.0095522 1.0095522
[9136] 0.8377255 0.8835964 0.6696901 1.0095522 2.1911668 1.0095522 0.8835964
[9143] 1.0095522 1.3817806 1.0095522 0.9571424 1.0095522 0.8835964 0.8835964
[9150] 1.0095522 0.8835964 0.8835964 1.0095522 0.7651538 0.8835964 1.0095522
[9157] 1.3817806 1.0095522 1.0095522 1.3817806 2.1911668 1.0095522 0.6696901
[9164] 0.8835964 1.0095522 0.8835964 0.8835964 1.5787520 0.8835964 1.0095522
[9171] 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[9178] 0.8377255 1.0095522 1.0095522 0.8835964 1.3817806 1.3817806 0.8835964
[9185] 0.9571424 1.3817806 1.0095522 0.8835964 1.0095522 1.3817806 1.0095522
[9192] 1.0095522 0.8835964 0.9571424 0.8835964 0.8835964 1.3817806 1.0095522
[9199] 1.0095522 0.9571424 1.0095522 1.9177880 1.3817806 1.0095522 1.0095522
[9206] 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[9213] 0.8377255 1.0095522 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964
[9220] 0.8835964 0.8835964 1.0095522 1.0095522 1.3817806 1.9177880 1.0095522
[9227] 0.9571424 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.9571424
[9234] 0.8835964 0.9571424 1.0095522 1.0095522 1.0095522 1.0095522 0.8835964
[9241] 1.0095522 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[9248] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 1.3817806 1.0095522
[9255] 0.8835964 0.8377255 0.8377255 1.0095522 1.0095522 0.8835964 0.8835964
[9262] 1.3817806 0.8377255 0.8377255 1.0095522 0.8835964 1.0095522 1.0095522
[9269] 0.8835964 0.9571424 1.0095522 1.0095522 0.8835964 1.0095522 1.0095522
[9276] 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964
[9283] 1.0095522 0.8377255 0.8835964 1.0095522 0.8835964 0.8835964 1.0095522
[9290] 0.8377255 0.8835964 0.8377255 0.9571424 1.0095522 0.8835964 1.0095522
[9297] 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.3817806
[9304] 0.6696901 1.0095522 1.0095522 0.8835964 1.0095522 0.9571424 1.0095522
[9311] 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964 1.0095522 2.1911668
[9318] 0.8835964 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522
[9325] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 0.8835964
[9332] 0.9571424 0.8835964 0.8377255 1.0095522 0.9571424 0.8835964 1.0095522
[9339] 1.0095522 0.8835964 0.8835964 0.8835964 2.1911668 0.8835964 0.7651538
[9346] 1.5787520 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964 0.8835964
[9353] 0.8835964 1.0095522 0.8835964 1.0095522 1.3817806 2.1911668 1.0095522
[9360] 1.0095522 0.8835964 0.8377255 1.0095522 1.0095522 0.8835964 1.0095522
[9367] 2.1911668 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522 0.8377255
[9374] 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[9381] 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522 1.0095522
[9388] 0.8835964 1.0095522 1.0095522 0.9571424 1.3817806 0.8835964 1.0095522
[9395] 0.8835964 0.9571424 1.0095522 1.0095522 1.3817806 1.3817806 1.0095522
[9402] 0.8377255 0.8377255 1.0095522 1.5787520 1.3817806 1.0095522 0.8835964
[9409] 0.8835964 1.0095522 0.8377255 0.8835964 1.0095522 0.8377255 1.0095522
[9416] 1.0095522 0.8377255 0.8835964 1.0095522 0.8835964 1.0095522 1.0095522
[9423] 1.0095522 0.8835964 0.8835964 0.8377255 0.8835964 1.0095522 1.0095522
[9430] 0.9571424 1.0095522 0.8835964 0.8835964 0.8835964 1.5787520 1.0095522
[9437] 0.8835964 1.0095522 0.8835964 1.0095522 2.1911668 0.8835964 1.0095522
[9444] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522
[9451] 1.0095522 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 0.8835964
[9458] 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964
[9465] 1.3817806 1.0095522 1.0095522 0.8835964 1.3817806 0.8835964 1.0095522
[9472] 1.0095522 0.8835964 1.0095522 0.8835964 0.8835964 0.9571424 1.0095522
[9479] 0.8835964 0.6696901 0.8835964 0.8377255 0.7651538 0.8835964 0.8835964
[9486] 0.8835964 1.0095522 0.8377255 0.7651538 0.8835964 0.9571424 1.0095522
[9493] 1.0095522 2.1911668 0.8835964 0.7651538 1.0095522 0.8835964 1.0095522
[9500] 1.0095522 0.8835964 1.0095522 1.0095522 0.8835964 1.0095522 0.8835964
[9507] 1.0095522 0.8377255 0.8835964 0.8835964 0.9571424 1.0095522 0.8835964
[9514] 1.0095522 0.9571424 0.8835964 1.0095522 0.9571424 0.8835964 0.8835964
[9521] 1.5787520 1.0095522 1.0095522 1.3817806 0.9571424 0.8377255 0.9571424
[9528] 1.0095522 0.8835964 0.8835964 0.8835964 0.8835964 0.8835964 0.6696901
[9535] 0.8835964 0.8835964 1.0095522 1.3817806 0.8835964 1.0095522 0.8835964
[9542] 1.3817806 1.3817806 0.8835964 0.8835964 1.0095522 1.0095522 0.8835964
[9549] 1.0095522 0.8835964 1.0095522 1.0095522 1.3817806 0.8377255 1.0095522
[9556] 1.0095522 1.3817806 0.8835964 1.0095522 0.8835964 1.0095522 0.8835964
[9563] 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 1.0095522 1.0095522
[9570] 0.8835964 1.0095522 1.0095522 0.8835964 2.1911668 1.0095522 0.8835964
[9577] 0.8835964 0.8835964 2.1911668 0.8835964 0.8835964 0.8835964 0.8835964
[9584] 0.8835964 1.3817806 0.8835964 0.8835964 0.8835964 0.8835964 1.0095522
[9591] 0.9571424 1.0095522 0.8835964 0.8835964 1.0095522 0.9571424 0.8377255
[9598] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 0.8835964 1.0095522
[9605] 1.0095522 1.0095522 1.0095522 0.8835964 0.8835964 0.8377255 1.0095522
[9612] 1.0095522 0.8835964 0.8835964 0.8835964 1.0095522 1.0095522 0.8377255
[9619] 0.8835964 0.8377255 1.0095522 1.0095522 0.8835964 0.8835964 0.8835964
[9626] 1.0095522 1.0095522 1.3817806 0.8835964 1.0095522 1.0095522 1.0095522
[9633] 1.5787520 1.0095522 1.0095522 0.8377255 1.0095522 1.0095522 0.8835964
[9640] 0.8835964 1.0095522 0.7651538 1.0095522 0.8835964 1.3817806 1.0095522
[9647] 1.0095522 1.0095522 1.3817806 0.8835964 1.0095522 1.0095522 0.8835964
[9654] 1.0095522 0.8835964 0.8835964 1.0095522 0.8835964 0.8377255 0.8835964
[9661] 0.8835964 1.5787520 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255
[9668] 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[9675] 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424
[9682] 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424
[9689] 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255
[9696] 0.8377255 0.8377255 0.8377255 0.9571424 1.5787520 0.8377255 0.8377255
[9703] 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[9710] 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255
[9717] 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[9724] 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 1.0095522
[9731] 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424
[9738] 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[9745] 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255
[9752] 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255
[9759] 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255
[9766] 0.9571424 0.8377255 0.9571424 0.8377255 0.7651538 0.8377255 0.9571424
[9773] 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[9780] 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255
[9787] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[9794] 0.9571424 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424
[9801] 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255
[9808] 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424
[9815] 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255
[9822] 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255
[9829] 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424
[9836] 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424
[9843] 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[9850] 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255
[9857] 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424
[9864] 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255
[9871] 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424
[9878] 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255
[9885] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[9892] 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424
[9899] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[9906] 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424
[9913] 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255
[9920] 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255
[9927] 0.9571424 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255
[9934] 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 1.3817806
[9941] 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255
[9948] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[9955] 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424
[9962] 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255
[9969] 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424
[9976] 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424
[9983] 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424 0.9571424
[9990] 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255
[9997] 0.9571424 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424
[10004] 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255
[10011] 0.8377255 0.9571424 0.8377255 0.9571424 1.0095522 0.8377255 0.9571424
[10018] 0.9571424 0.8377255 0.8377255 0.9571424 0.8835964 0.9571424 0.9571424
[10025] 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255
[10032] 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255
[10039] 0.9571424 0.9571424 0.9571424 1.0095522 0.9571424 0.9571424 0.8377255
[10046] 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424 0.9571424 0.9571424
[10053] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[10060] 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255
[10067] 1.5787520 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255
[10074] 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[10081] 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255
[10088] 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255
[10095] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[10102] 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255
[10109] 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255
[10116] 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255
[10123] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[10130] 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.8835964
[10137] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424
[10144] 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424
[10151] 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[10158] 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424
[10165] 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424
[10172] 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424
[10179] 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255
[10186] 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255
[10193] 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[10200] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[10207] 0.9571424 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424
[10214] 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424
[10221] 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255
[10228] 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255
[10235] 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[10242] 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424
[10249] 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424
[10256] 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424
[10263] 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255
[10270] 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255
[10277] 0.8377255 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255
[10284] 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255
[10291] 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255
[10298] 1.5787520 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[10305] 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424
[10312] 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255
[10319] 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424
[10326] 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424
[10333] 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424
[10340] 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255
[10347] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[10354] 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255
[10361] 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424
[10368] 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424
[10375] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[10382] 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[10389] 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255
[10396] 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255
[10403] 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[10410] 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[10417] 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[10424] 0.9571424 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255
[10431] 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424
[10438] 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.9571424
[10445] 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255
[10452] 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424
[10459] 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424
[10466] 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255
[10473] 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424
[10480] 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255
[10487] 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255
[10494] 0.8377255 0.9571424 0.9571424 0.9571424 0.9571424 0.8377255 0.9571424
[10501] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255
[10508] 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424
[10515] 0.9571424 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255
[10522] 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255
[10529] 0.9571424 0.9571424 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255
[10536] 0.8377255 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255
[10543] 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424
[10550] 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[10557] 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424
[10564] 0.9571424 0.8377255 0.8377255 0.8377255 1.3817806 0.8377255 0.9571424
[10571] 0.9571424 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.8377255
[10578] 0.8377255 0.8377255 0.9571424 0.9571424 0.8377255 0.9571424 0.9571424
[10585] 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424
[10592] 0.8377255 0.9571424 0.8377255 0.9571424 0.9571424 0.9571424 0.8377255
[10599] 0.8377255 0.9571424 0.8377255 0.8377255 0.8377255 0.9571424 0.9571424
[10606] 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255 0.9571424 0.8377255
[10613] 0.9571424 0.8377255 0.8377255 0.8377255 0.8377255 0.8377255 0.9571424
[10620] 0.9571424 0.9571424 0.9571424 0.8377255 0.8377255 0.9571424 0.8377255
[10627] 1.5787520 1.3817806 1.3817806 0.8835964 0.8835964 1.0095522 1.3817806
[10634] 0.8835964 1.3817806 1.3817806 0.9571424 1.3817806 1.3817806 1.0095522
[10641] 1.0095522 0.8835964 Min. 1st Qu. Median Mean 3rd Qu. Max.
0.6697 0.8836 0.9571 1.0000 1.0096 2.1912 Occasionally, some generated weights may be too small or too large. We can prevent that problem by restricting them to some minimum/maximum values. We use trimWeights() with ‘lower’ set to the minimum and ‘upper’ to the maximum weight that we want to allow in our data.
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.6697 0.8836 0.9571 1.0000 1.0096 2.1912 For a survey that should be nationally representative, we usually base our survey weights on at least the following variables: gender, age category, race, ethnicity, education, and region of the country. Additional variables may be included depending on the topic and nature of the data.