Covid19 and the Software Industry
Belosan W Jekale, Razi Monief, Hirenkumar Desai
2024-02-11
R Markdown
Abstract: The Covid pandemic has affected the global world. One of the major affects were seen in workplaces. Many companies have since moved either partially or fully remote; especially the software industry. Using our survey based dataset we will be conducting analysis on Covid19 and it’s affects on the software industry.
Dataset:
A data frame with 1512 observations on 15 variables
[, IV1] Thinking about your current job, how much of your work time have you
spent working remotely this year? If you work a 5 day week, each day
of remote working equals 20% of your time.
[, A1] Does your employer have a policy that workers must be at the employer’s
workplace some of the time?
[, IV2] How much of your work time does your employer’s policy require you to
be at their workplace? If you work a 5 day week, 1 day equals 20% of
your work time.
[, A2] Imagine that COVID-19 is cured or eradicated. How likely would you
consider the following statements? - I would have more choice about
whether I work remotely
[, IV3] Think about your experience this year. On a day when you attend your
employer's workplace, how many hours would you spend doing the
following activities? For example, a response of ‘1’ means 1 hour. A
response of ‘0.5’ means half an hour. Your responses should add to 24
hours. - Preparing for work and commuting
[, IV4] Think about your experience this year. On a day when you attend your
employer's workplace, how many hours would you spend doing the
following activities? For example, a response of ‘1’ means 1 hour. A
response of ‘0.5’ means half an hour. Your responses should add to 24
hours. - Working
[, IV5] Think about your experience this year. On a day when you attend your
employer's workplace, how many hours would you spend doing the
following activities? For example, a response of ‘1’ means 1 hour. A
response of ‘0.5’ means half an hour. Your responses should add to 24
hours. - Caring and domestic responsibilities
[, IV6] Think about your experience this year. On a day when you attend your
employer's workplace, how many hours would you spend doing the
following activities? For example, a response of ‘1’ means 1 hour. A
response of ‘0.5’ means half an hour. Your responses should add to 24
hours. - Personal and family time
[, IV7] Think about your experience this year. On a day when you attend your
employer's workplace, how many hours would you spend doing the
following activities? For example, a response of ‘1’ means 1 hour. A
response of ‘0.5’ means half an hour. Your responses should add to 24
hours. - Sleep
[, IV8] Think about your experience this year. On a day when you work remotely,
how many hours would you spend doing the following activities? For
example, a response of ‘1’ means 1 hour. A response of ‘0.5’ means
half an hour. Your responses should add to 24 hours. - Preparing
for work and commuting
[, IV9] Think about your experience this year. On a day when you work remotely,
how many hours would you spend doing the following activities? For
example, a response of ‘1’ means 1 hour. A response of ‘0.5’ means
half an hour. Your responses should add to 24 hours. - Working
[, IV10] Think about your experience this year. On a day when you work remotely,
how many hours would you spend doing the following activities? For
example, a response of ‘1’ means 1 hour. A response of ‘0.5’ means
half an hour. Your responses should add to 24 hours. - Caring and
domestic responsibilities
[, IV11] Think about your experience this year. On a day when you work remotely,
how many hours would you spend doing the following activities? For
example, a response of ‘1’ means 1 hour. A response of ‘0.5’ means
half an hour. Your responses should add to 24 hours. - Personal and
family time
[, IV12] Think about your experience this year. On a day when you work remotely,
how many hours would you spend doing the following activities? For
example, a response of ‘1’ means 1 hour. A response of ‘0.5’ means
half an hour. Your responses should add to 24 hours. - Sleep
[, D1] This question is about your productivity. Productivity means what you
produce for each hour that you work. It includes the amount of work you
achieve each hour, and the quality of your work each hour. Please
compare your productivity when you work remotely to when you work at
your employer’s workplace. Roughly how productive are you, each hour,
when you work remotely?
Hypothesis
- Null hypothesis (H0): Remote work HAS NOT affected the software industry
during covid 19 pandemic
- Alternative hypothesis (Ha): Remote work HAS affected the software industry
during covid 19 pandemicIndependent Variables
- The elements (columns/variables) that are used to predict the dependent variable are
known as independent variables. A single dependent
variable has utilized in all cases, and a number of independent variables are
combined with it in order to predict the dependent variable based on changes
in the independent variables. For the developed hypothesis, the IV is the
IV9. However,depending on the hypothesis, the possible independent variables
are IV1, IV2, IV3, IV4, IV5, IV6, IV7, IV8, IV10, IV11 and IV12. Dependent Variables
- The primary factor (Column/variable) predicted by regression is called
the dependent variable. In this case, the dataset's dependent variable is the
D1.Importing Dataset
covidData <- read.csv("C:/Users/billy/OneDrive/Documents/ANLY 500/covidIT_data.csv", check.names = F)
str(covidData)## 'data.frame': 1512 obs. of 15 variables:
## $ IV1 : num 0.5 0.1 0.9 0.4 1 0.5 0.8 1 0.5 0.9 ...
## $ A1 : chr "Yes" "Yes" "Yes" "Yes" ...
## $ IV2 : num 0.4 1 0.2 0.5 0 0 0.2 0 0 0 ...
## $ A2 : chr "Somewhat likely" "Neither unlikely or likely" "Very likely" "Somewhat likely" ...
## $ IV3 : num 2 1.5 3 1 5 3 2 1.5 1 1.5 ...
## $ IV4 : num 6 8 8 8 8 8 8 8.5 8 8 ...
## $ IV5 : num 1 2 1 6 2 2 3 0.5 5 0 ...
## $ IV6 : num 7 6 4 3 3 3 3 5.5 2 7.5 ...
## $ IV7 : num 8 6.5 8 6 6 8 8 8 8 7 ...
## $ IV8 : num 0 0 1 1 NA 2 1 0.5 1 0.5 ...
## $ IV9 : num 8 10 8 8 9 8 8 9 7 8 ...
## $ IV10: num 1 4 2 4 3 4 3 0.5 5 0 ...
## $ IV11: num 7 4 5 4 4 2 4 5 3 8.5 ...
## $ IV12: num 8 6 8 7 8 8 8 9 8 7 ...
## $ D1 : num 0.5 0.5 0 0.5 0.2 0.2 0.1 0.2 -0.5 0.5 ...Librarys and functions
#install.packages("readr")
library(readr)
#install.packages("ggplot2")
library(ggplot2)
#install.packages("psych")
library (psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha#install.packages("mice", repos = "https://cran.us.r-project.org/")
library(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindcleanup <- theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line.x = element_line(color = 'black'),
axis.line.y = element_line(color = 'black'),
legend.key = element_rect(fill = 'white'),
text = element_text(size = 10))The structure and preview of the data
str(covidData)## 'data.frame': 1512 obs. of 15 variables:
## $ IV1 : num 0.5 0.1 0.9 0.4 1 0.5 0.8 1 0.5 0.9 ...
## $ A1 : chr "Yes" "Yes" "Yes" "Yes" ...
## $ IV2 : num 0.4 1 0.2 0.5 0 0 0.2 0 0 0 ...
## $ A2 : chr "Somewhat likely" "Neither unlikely or likely" "Very likely" "Somewhat likely" ...
## $ IV3 : num 2 1.5 3 1 5 3 2 1.5 1 1.5 ...
## $ IV4 : num 6 8 8 8 8 8 8 8.5 8 8 ...
## $ IV5 : num 1 2 1 6 2 2 3 0.5 5 0 ...
## $ IV6 : num 7 6 4 3 3 3 3 5.5 2 7.5 ...
## $ IV7 : num 8 6.5 8 6 6 8 8 8 8 7 ...
## $ IV8 : num 0 0 1 1 NA 2 1 0.5 1 0.5 ...
## $ IV9 : num 8 10 8 8 9 8 8 9 7 8 ...
## $ IV10: num 1 4 2 4 3 4 3 0.5 5 0 ...
## $ IV11: num 7 4 5 4 4 2 4 5 3 8.5 ...
## $ IV12: num 8 6 8 7 8 8 8 9 8 7 ...
## $ D1 : num 0.5 0.5 0 0.5 0.2 0.2 0.1 0.2 -0.5 0.5 ...head(covidData)tail(covidData)dim(covidData)## [1] 1512 15names(covidData)## [1] "IV1" "A1" "IV2" "A2" "IV3" "IV4" "IV5" "IV6" "IV7" "IV8"
## [11] "IV9" "IV10" "IV11" "IV12" "D1"Data Screening/Cleanup
### Data Screening
## Converting character variables and Checking errors
summary(covidData)## IV1 A1 IV2 A2
## Min. :0.0000 Length:1512 Min. :0.000 Length:1512
## 1st Qu.:0.2000 Class :character 1st Qu.:0.000 Class :character
## Median :0.5000 Mode :character Median :0.100 Mode :character
## Mean :0.4969 Mean :0.264
## 3rd Qu.:0.8000 3rd Qu.:0.500
## Max. :1.0000 Max. :1.000
##
## IV3 IV4 IV5 IV6
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 1.000 1st Qu.: 7.000 1st Qu.: 2.000 1st Qu.: 3.000
## Median : 2.000 Median : 8.000 Median : 3.000 Median : 4.000
## Mean : 2.246 Mean : 7.832 Mean : 2.797 Mean : 3.855
## 3rd Qu.: 3.000 3rd Qu.: 8.500 3rd Qu.: 4.000 3rd Qu.: 5.000
## Max. :20.000 Max. :20.000 Max. :19.000 Max. :14.000
## NA's :3 NA's :3 NA's :17 NA's :5
## IV7 IV8 IV9 IV10
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 7.000 1st Qu.: 0.000 1st Qu.: 7.00 1st Qu.: 2.000
## Median : 8.000 Median : 0.500 Median : 8.00 Median : 3.000
## Mean : 7.393 Mean : 1.178 Mean : 8.11 Mean : 3.026
## 3rd Qu.: 8.000 3rd Qu.: 1.000 3rd Qu.: 9.00 3rd Qu.: 4.000
## Max. :20.000 Max. :24.000 Max. :24.00 Max. :17.000
## NA's :12 NA's :24 NA's :3 NA's :18
## IV11 IV12 D1
## Min. : 0.000 Min. : 0.000 Min. :-0.5000
## 1st Qu.: 3.000 1st Qu.: 7.000 1st Qu.: 0.0000
## Median : 4.000 Median : 8.000 Median : 0.2000
## Mean : 4.335 Mean : 7.485 Mean : 0.1737
## 3rd Qu.: 5.000 3rd Qu.: 8.000 3rd Qu.: 0.4000
## Max. :24.000 Max. :14.000 Max. : 0.5000
## NA's :7 NA's :9notypos <- covidData
notypos$A1 <- factor(notypos$A1,
labels = c("Yes", "No"))
notypos$A2 <- factor(notypos$A2,
labels = c("Very unlikely", "Very likely", "Neither unlikely or likely", "Somewhat likely", "Somewhat unlikely"))
summary(notypos)## IV1 A1 IV2 A2
## Min. :0.0000 Yes:552 Min. :0.000 Very unlikely :384
## 1st Qu.:0.2000 No :960 1st Qu.:0.000 Very likely :455
## Median :0.5000 Median :0.100 Neither unlikely or likely:222
## Mean :0.4969 Mean :0.264 Somewhat likely :254
## 3rd Qu.:0.8000 3rd Qu.:0.500 Somewhat unlikely :197
## Max. :1.0000 Max. :1.000
##
## IV3 IV4 IV5 IV6
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 1.000 1st Qu.: 7.000 1st Qu.: 2.000 1st Qu.: 3.000
## Median : 2.000 Median : 8.000 Median : 3.000 Median : 4.000
## Mean : 2.246 Mean : 7.832 Mean : 2.797 Mean : 3.855
## 3rd Qu.: 3.000 3rd Qu.: 8.500 3rd Qu.: 4.000 3rd Qu.: 5.000
## Max. :20.000 Max. :20.000 Max. :19.000 Max. :14.000
## NA's :3 NA's :3 NA's :17 NA's :5
## IV7 IV8 IV9 IV10
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 7.000 1st Qu.: 0.000 1st Qu.: 7.00 1st Qu.: 2.000
## Median : 8.000 Median : 0.500 Median : 8.00 Median : 3.000
## Mean : 7.393 Mean : 1.178 Mean : 8.11 Mean : 3.026
## 3rd Qu.: 8.000 3rd Qu.: 1.000 3rd Qu.: 9.00 3rd Qu.: 4.000
## Max. :20.000 Max. :24.000 Max. :24.00 Max. :17.000
## NA's :12 NA's :24 NA's :3 NA's :18
## IV11 IV12 D1
## Min. : 0.000 Min. : 0.000 Min. :-0.5000
## 1st Qu.: 3.000 1st Qu.: 7.000 1st Qu.: 0.0000
## Median : 4.000 Median : 8.000 Median : 0.2000
## Mean : 4.335 Mean : 7.485 Mean : 0.1737
## 3rd Qu.: 5.000 3rd Qu.: 8.000 3rd Qu.: 0.4000
## Max. :24.000 Max. :14.000 Max. : 0.5000
## NA's :7 NA's :9names(notypos)## [1] "IV1" "A1" "IV2" "A2" "IV3" "IV4" "IV5" "IV6" "IV7" "IV8"
## [11] "IV9" "IV10" "IV11" "IV12" "D1"apply(notypos[ , -c(2, 4)], 2, mean, na.rm = T)## IV1 IV2 IV3 IV4 IV5 IV6 IV7 IV8
## 0.4968915 0.2639550 2.2457720 7.8318754 2.7971237 3.8546715 7.3933667 1.1781922
## IV9 IV10 IV11 IV12 D1
## 8.1104705 3.0261714 4.3354020 7.4849301 0.1736772apply(notypos[ , -c(2, 4)], 2, sd, na.rm = T)## IV1 IV2 IV3 IV4 IV5 IV6 IV7 IV8
## 0.3487668 0.3331513 1.6436332 1.9310000 1.6261774 1.7657156 1.5473905 1.9498599
## IV9 IV10 IV11 IV12 D1
## 2.0983928 1.5731061 1.9087719 1.5674942 0.2409066## Checking missing data
apply(notypos, 2, function(x) { sum(is.na(x))})## IV1 A1 IV2 A2 IV3 IV4 IV5 IV6 IV7 IV8 IV9 IV10 IV11 IV12 D1
## 0 0 0 0 3 3 17 5 12 24 3 18 7 9 0percentmiss <- function(x){ sum(is.na(x))/length(x) * 100}
missing <- apply(notypos, 1, percentmiss)
table(missing)## missing
## 0 6.66666666666667 13.3333333333333 20
## 1458 31 13 1
## 26.6666666666667 40 46.6666666666667
## 7 1 1replace_rows <- subset(notypos, missing <= 25)
noreplace_row <- subset(notypos, missing > 25)
nrow(notypos)## [1] 1512nrow(replace_rows)## [1] 1503nrow(noreplace_row)## [1] 9apply(replace_rows, 2, percentmiss)## IV1 A1 IV2 A2 IV3 IV4 IV5 IV6
## 0.0000000 0.0000000 0.0000000 0.0000000 0.0665336 0.1330672 0.8649368 0.1330672
## IV7 IV8 IV9 IV10 IV11 IV12 D1
## 0.5322688 1.3306720 0.0000000 0.6653360 0.1330672 0.1330672 0.0000000replace_columns <- replace_rows[ , -c(2,4)]
noreplace_columns <- replace_rows[ , c(2,4)]
temp_no_miss <- mice(replace_columns)##
## iter imp variable
## 1 1 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 1 2 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 1 3 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 1 4 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 1 5 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 2 1 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 2 2 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 2 3 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 2 4 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 2 5 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 3 1 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 3 2 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 3 3 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 3 4 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 3 5 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 4 1 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 4 2 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 4 3 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 4 4 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 4 5 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 5 1 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 5 2 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 5 3 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 5 4 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12
## 5 5 IV3 IV4 IV5 IV6 IV7 IV8 IV10 IV11 IV12## Warning: Number of logged events: 143nomiss <- complete(temp_no_miss, 1)
dim(notypos)## [1] 1512 15dim(nomiss)## [1] 1503 13all_colunms <- cbind(noreplace_columns, nomiss)
dim(all_colunms)## [1] 1503 15summary(all_colunms)## A1 A2 IV1 IV2
## Yes:547 Very unlikely :380 Min. :0.0000 Min. :0.0000
## No :956 Very likely :452 1st Qu.:0.2000 1st Qu.:0.0000
## Neither unlikely or likely:221 Median :0.5000 Median :0.1000
## Somewhat likely :254 Mean :0.4983 Mean :0.2647
## Somewhat unlikely :196 3rd Qu.:0.8000 3rd Qu.:0.5000
## Max. :1.0000 Max. :1.0000
## IV3 IV4 IV5 IV6
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 1.000 1st Qu.: 7.000 1st Qu.: 2.00 1st Qu.: 3.00
## Median : 2.000 Median : 8.000 Median : 2.75 Median : 4.00
## Mean : 2.219 Mean : 7.833 Mean : 2.76 Mean : 3.84
## 3rd Qu.: 3.000 3rd Qu.: 8.500 3rd Qu.: 4.00 3rd Qu.: 5.00
## Max. :16.000 Max. :20.000 Max. :15.00 Max. :12.50
## IV7 IV8 IV9 IV10
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 7.000 1st Qu.: 0.000 1st Qu.: 7.000 1st Qu.: 2.000
## Median : 8.000 Median : 0.500 Median : 8.000 Median : 3.000
## Mean : 7.352 Mean : 1.131 Mean : 8.096 Mean : 2.997
## 3rd Qu.: 8.000 3rd Qu.: 1.000 3rd Qu.: 9.000 3rd Qu.: 4.000
## Max. :17.000 Max. :16.000 Max. :23.000 Max. :12.000
## IV11 IV12 D1
## Min. : 0.0 Min. : 0.000 Min. :-0.5000
## 1st Qu.: 3.0 1st Qu.: 7.000 1st Qu.: 0.0000
## Median : 4.0 Median : 8.000 Median : 0.2000
## Mean : 4.3 Mean : 7.477 Mean : 0.1733
## 3rd Qu.: 5.0 3rd Qu.: 8.000 3rd Qu.: 0.4000
## Max. :20.0 Max. :14.000 Max. : 0.5000## Outliers
#leverage
model1 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = all_colunms)
k <- 14 #Number of IV
leverage <- hatvalues(model1)
cutleverage <- (2*k+2) /nrow(all_colunms)
badleverage <- as.numeric(leverage > cutleverage)
table(badleverage)## badleverage
## 0 1
## 1398 105#Cooks
cooks <- cooks.distance(model1)
cutcooks <- 4 / (nrow(all_colunms) - k - 1)
badcooks <- as.numeric(cooks > cutcooks)
table(badcooks)## badcooks
## 0 1
## 1431 72#Mahal
mahal <- mahalanobis(all_colunms[ , -c(1,2)],
colMeans(all_colunms[ , -c(1,2)]),
cov(all_colunms[ , -c(1,2)]))
cutmahal <- qchisq(1-.001, ncol(all_colunms[ , -c(1,2)]))
badmahal <- as.numeric(mahal > cutmahal)
table(badmahal)## badmahal
## 0 1
## 1427 76## Overall Outliers
totalout <- badmahal + badleverage + badcooks
table(totalout)## totalout
## 0 1 2 3
## 1357 69 47 30noout <- subset(all_colunms, totalout < 2)Assumption Checks
## Additivity
str(noout)## 'data.frame': 1426 obs. of 15 variables:
## $ A1 : Factor w/ 2 levels "Yes","No": 2 2 2 2 1 1 2 1 2 2 ...
## $ A2 : Factor w/ 5 levels "Very unlikely",..: 2 1 4 2 2 1 2 1 1 2 ...
## $ IV1 : num 0.5 0.1 0.9 0.4 1 0.5 0.8 1 0.5 0.9 ...
## $ IV2 : num 0.4 1 0.2 0.5 0 0 0.2 0 0 0 ...
## $ IV3 : num 2 1.5 3 1 5 3 2 1.5 1 1.5 ...
## $ IV4 : num 6 8 8 8 8 8 8 8.5 8 8 ...
## $ IV5 : num 1 2 1 6 2 2 3 0.5 5 0 ...
## $ IV6 : num 7 6 4 3 3 3 3 5.5 2 7.5 ...
## $ IV7 : num 8 6.5 8 6 6 8 8 8 8 7 ...
## $ IV8 : num 0 0 1 1 0 2 1 0.5 1 0.5 ...
## $ IV9 : num 8 10 8 8 9 8 8 9 7 8 ...
## $ IV10: num 1 4 2 4 3 4 3 0.5 5 0 ...
## $ IV11: num 7 4 5 4 4 2 4 5 3 8.5 ...
## $ IV12: num 8 6 8 7 8 8 8 9 8 7 ...
## $ D1 : num 0.5 0.5 0 0.5 0.2 0.2 0.1 0.2 -0.5 0.5 ...cor(noout[ , -c(1,2)])## IV1 IV2 IV3 IV4 IV5 IV6
## IV1 1.000000000 -0.32873025 0.04073436 -0.024248356 -0.07666597 -0.009548454
## IV2 -0.328730251 1.00000000 0.09159735 -0.029309894 0.08032254 -0.058916482
## IV3 0.040734355 0.09159735 1.00000000 -0.228108827 -0.02125173 -0.321536824
## IV4 -0.024248356 -0.02930989 -0.22810883 1.000000000 -0.36423075 -0.392977336
## IV5 -0.076665967 0.08032254 -0.02125173 -0.364230748 1.00000000 -0.295318349
## IV6 -0.009548454 -0.05891648 -0.32153682 -0.392977336 -0.29531835 1.000000000
## IV7 0.088697493 -0.07844387 -0.35374330 -0.097167666 -0.28043129 -0.119094084
## IV8 -0.135077423 0.13502179 0.49626394 -0.321775441 0.18329676 -0.052438713
## IV9 0.049145206 -0.05164419 -0.12236020 0.733002948 -0.29388327 -0.318752046
## IV10 -0.093415587 0.06134702 0.02712966 -0.284174359 0.71161348 -0.195875141
## IV11 0.055751109 -0.06206494 -0.13565702 -0.262357074 -0.25811046 0.634042739
## IV12 0.112135969 -0.06570106 -0.23664803 0.002034401 -0.24375595 -0.109084738
## D1 0.163612758 0.01927080 0.10576987 -0.100859612 0.09222619 -0.067007677
## IV7 IV8 IV9 IV10 IV11 IV12
## IV1 0.088697493 -0.13507742 0.049145206 -0.09341559 0.05575111 0.112135969
## IV2 -0.078443870 0.13502179 -0.051644186 0.06134702 -0.06206494 -0.065701057
## IV3 -0.353743297 0.49626394 -0.122360203 0.02712966 -0.13565702 -0.236648034
## IV4 -0.097167666 -0.32177544 0.733002948 -0.28417436 -0.26235707 0.002034401
## IV5 -0.280431290 0.18329676 -0.293883272 0.71161348 -0.25811046 -0.243755946
## IV6 -0.119094084 -0.05243871 -0.318752046 -0.19587514 0.63404274 -0.109084738
## IV7 1.000000000 -0.27386097 -0.048449265 -0.23262579 -0.05077909 0.686708693
## IV8 -0.273860970 1.00000000 -0.333811883 0.07246651 -0.25601441 -0.399535481
## IV9 -0.048449265 -0.33381188 1.000000000 -0.33965751 -0.41750130 -0.089080647
## IV10 -0.232625789 0.07246651 -0.339657515 1.00000000 -0.31644775 -0.297433574
## IV11 -0.050779087 -0.25601441 -0.417501299 -0.31644775 1.00000000 -0.088610121
## IV12 0.686708693 -0.39953548 -0.089080647 -0.29743357 -0.08861012 1.000000000
## D1 -0.004600325 0.06469410 -0.001335388 0.03644695 -0.07442951 -0.013655250
## D1
## IV1 0.163612758
## IV2 0.019270800
## IV3 0.105769873
## IV4 -0.100859612
## IV5 0.092226185
## IV6 -0.067007677
## IV7 -0.004600325
## IV8 0.064694096
## IV9 -0.001335388
## IV10 0.036446949
## IV11 -0.074429506
## IV12 -0.013655250
## D1 1.000000000#install.packages("corrplot")
library(corrplot)## corrplot 0.92 loadedcorrplot(cor(noout[ , -c(1,2)]))
# We have met the assumption of additivity.
## Linearity
random <- rchisq(nrow(noout), 7)
fake <- lm(random ~ .,
data = noout)
standardized <- rstudent(fake)
fitvaules <- scale(fake$fitted.values)
{qqnorm(standardized)
abline(0,1)}
plot(fake, 2)
# We have not met the assumption for linearity as the plot shows above since it's not between -2 and 2 and the tails of the plot are fat.
## Normality
#install.packages("moments")
library(moments)
skewness(noout[ , -c(1,2)])## IV1 IV2 IV3 IV4 IV5 IV6 IV7
## 0.1125337 0.9689997 1.1586409 -0.5227377 0.4864770 0.5846907 -0.4986082
## IV8 IV9 IV10 IV11 IV12 D1
## 2.2056853 -0.2812643 0.4816787 0.4451744 -0.7716670 -0.3732701kurtosis(noout[ , -c(1,2)]) - 3## IV1 IV2 IV3 IV4 IV5 IV6 IV7
## -1.4148330 -0.3945146 2.1224644 2.4926685 0.2015405 0.6450057 2.4610933
## IV8 IV9 IV10 IV11 IV12 D1
## 5.0260046 1.8336889 0.6846064 0.7579440 3.4185970 -0.4188437hist(standardized, breaks=15)
length(standardized)## [1] 1426# We have met the assumption for normality as it is close to a normal distribution with a bit of a right skew.
## Homogeneity/Homoscedasticity
{plot(fitvaules, standardized)
abline(0,0)
abline(v = 0)}
# The assumption for Homogeneity and Homoscedasticity have been met as the plot shown below is equally distributed top to bottom and left to right respectively.Exploratory Data Anaysis: Frequency Distribution of Data
# Frequency table and Histogram for IV3: Time spent preparing for work and commuting - workplace
fIV3 = table (noout$IV3)
fIV3##
## 0 0.2 0.25 0.4 0.5 0.7 1 1.25 1.5 1.75 2 2.12 2.5 3 3.5 4
## 50 2 5 1 54 1 333 2 92 1 397 1 56 230 20 94
## 4.5 5 6 7 8 9
## 1 52 24 3 6 1histIV3 <- ggplot(noout, aes(x = IV3))
histIV3 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Preparing for work and commuting") +
ylab("Frequency") +
ggtitle("Time spent preparing for work and commuting - Workplace") +
cleanup
describe(noout$IV3)# Frequency table and Histogram for IV4: Time spent working - Workplace
fIV4 = table (noout$IV4)
fIV4##
## 1.5 2 3 4 5 6 6.5 6.75 7 7.5 7.6 8 8.5 8.6 9 9.5
## 1 9 12 34 47 85 2 1 172 62 4 610 46 1 169 10
## 9.7 10 10.5 11 11.5 12 13 14
## 1 109 3 17 2 25 2 2histIV4 <- ggplot(noout, aes(x = IV4))
histIV4 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Working") +
ylab("Frequency") +
ggtitle("Time spent Working - Workplace") +
cleanup
describe(noout$IV4)# Frequency table and Histogram for IV5: Time spent Caring and domestic responsibilities - Workplace
fIV5 = table (noout$IV5)
fIV5##
## 0 0.5 1 1.5 2 2.3 2.4 2.5 2.75 2.8999 3
## 59 9 179 30 415 1 1 24 2 1 320
## 3.5 3.6 4 4.5 5 5.5 6 6.5 7 8
## 18 1 205 10 101 2 34 1 11 2histIV5 <- ggplot(covidData, aes(x = IV5))
histIV5 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Caring and domestic responsibilities") +
ylab("Frequency") +
ggtitle("Time spent Caring and domestic responsibilities - Workplace") +
cleanup## Warning: Removed 17 rows containing non-finite values (`stat_bin()`).
describe(noout$IV5)# Frequency table and Histogram for IV6: Time spent Personal and family time - Workplace
fIV6 = table (noout$IV6)
fIV6##
## 0 0.5 1 1.5 2 2.25 2.5 2.75 3 3.5 3.75 4 4.5 5 5.3 5.5
## 6 3 57 12 224 1 22 1 292 39 1 338 22 208 1 15
## 5.75 5.8 6 6.5 7 7.5 8 9 10 11
## 1 1 98 11 31 9 24 5 3 1histIV6 <- ggplot(noout, aes(x = IV6))
histIV6 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Personal and family time - Workplace") +
ylab("Frequency") +
ggtitle("Time spent Personal and family time - Workplace") +
cleanup
describe(noout$IV6)# Frequency table and Histogram for IV7: Time spent Sleep - Workplace
fIV7 = table (noout$IV7)
fIV7##
## 2 3 4 5 5.5 6 6.5 7 7.25 7.5 7.7 7.75 7.8 8 8.5 8.75
## 3 6 25 37 3 167 16 339 1 39 1 1 1 620 12 1
## 8.9 9 9.5 9.9 10 10.5 11 12
## 1 96 3 1 40 1 6 6histIV7 <- ggplot(noout, aes(x = IV7))
histIV7 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Sleep") +
ylab("Frequency") +
ggtitle("Time spent Sleep - Workplace") +
cleanup
describe(noout$IV7)# Frequency table and Histogram for IV8: Time spent preparing for work and commuting - Remote
fIV8 = table (noout$IV8)
fIV8##
## 0 0.1 0.2 0.25 0.3 0.4 0.5 1 1.5 2 2.5 3 3.5 4 5 6
## 544 2 2 6 2 1 296 282 9 112 4 49 1 48 36 22
## 7 8 9
## 5 4 1histIV8 <- ggplot(noout, aes(x = IV8))
histIV8 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Preparing for work and commuting") +
ylab("Frequency") +
ggtitle("Time spent preparing for work and commuting - Remote") +
cleanup
describe(noout$IV8)# Frequency table and Histogram for IV9: Time spent working - Remote
fIV9 = table (noout$IV9)
fIV9##
## 1 2 2.5 3 4 5 6 6.5 7 7.5 7.6 8 8.5 8.75 9 9.5
## 2 6 1 13 31 47 91 2 145 47 1 493 41 2 207 18
## 9.75 10 10.5 11 11.5 12 13 14 15 16
## 1 190 6 20 1 52 4 2 2 1histIV9 <- ggplot(noout, aes(x = IV9))
histIV9 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Working") +
ylab("Frequency") +
ggtitle("Time spent working - Remote") +
cleanup
describe(noout$IV9)# Frequency table and Histogram for IV10: Time spent Caring and domestic responsibilities - Remote
fIV10 = table (noout$IV10)
fIV10##
## 0 0.5 1 1.5 2 2.5 3 3.5 3.7 3.75 4 4.25 4.5 5 5.5 6
## 40 5 134 33 352 35 350 15 1 1 272 1 13 118 4 33
## 6.5 7 8 10
## 1 10 7 1histIV10 <- ggplot(noout, aes(x = IV10))
histIV10 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Caring and domestic responsibilities") +
ylab("Frequency") +
ggtitle("Time spent Caring and domestic responsibilities - Remote") +
cleanup
describe(noout$IV10)# Frequency table and Histogram for IV11: Time spent Personal and family time - Remote
fIV11 = table (noout$IV11)
fIV11##
## 0 1 1.5 2 2.5 3 3.5 3.6 4 4.25 4.5 4.69 4.9 5 5.25 5.39
## 9 27 9 146 11 226 36 1 325 2 54 1 1 246 3 1
## 5.5 5.8 6 6.5 6.75 7 7.5 8 8.5 9 9.5 10 11 12
## 41 2 149 23 1 48 10 34 7 4 2 3 3 1histIV11 <- ggplot(noout, aes(x = IV11))
histIV11 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Personal and family time") +
ylab("Frequency") +
ggtitle("Time spent Personal and family time - Remote") +
cleanup
describe(noout$IV11)# Frequency table and Histogram for IV12: Time spent Sleep - Remote
fIV12 = table (noout$IV12)
fIV12##
## 0 2 3 4 5 5.5 6 6.5 7 7.5 7.75 8 8.5 9 9.4 9.5
## 1 5 8 30 35 1 123 12 297 39 1 651 20 120 1 7
## 10 11 12 13
## 63 3 8 1histIV12 <- ggplot(noout, aes(x = IV12))
histIV12 +
geom_histogram(binwidth = 0.4,
color = "black",
fill = "grey") +
xlab("Sleep") +
ylab("Frequency") +
ggtitle("Time spent Sleep - Remote") +
cleanup
describe(noout$IV12)Comparison of employee’s productivity when working remotely vs when working at employer’s workplace (IV4 vs IV9)
scatter <- ggplot(noout, aes(IV4, IV9))
scatter +
geom_point() +
geom_smooth(method = "lm", color = "blue") +
xlab("IV4") +
ylab("IV9") +
ggtitle("Employer’s workplace vs working remotely") +
cleanup## `geom_smooth()` using formula = 'y ~ x'
scatter <- ggplot(noout, aes(D1, IV4))
scatter +
geom_point() +
geom_smooth(method = "lm", color = "blue") +
xlab("D1") +
ylab("IV4") +
ggtitle("Employee’s productivity when working at Office") +
cleanup## `geom_smooth()` using formula = 'y ~ x'
scatter <- ggplot(noout, aes(D1, IV9))
scatter +
geom_point() +
geom_smooth(method = "lm", color = "blue") +
xlab("D1") +
ylab("IV9") +
ggtitle("Employee’s productivity when working remotely") +
cleanup## `geom_smooth()` using formula = 'y ~ x'
grouped <- ggplot(noout, aes(IV4, IV9, color = D1))
grouped +
geom_point() +
geom_smooth(method = "lm", color = "red") +
xlab("IV4") +
ylab("IV9") +
ggtitle("Employee’s productivity when working remotely Vs when working at
Office") +
cleanup## `geom_smooth()` using formula = 'y ~ x'
Method: Stepwise Regression
modelStep <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep1 <- lm(D1 ~ A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep2 <- lm(D1 ~ A1 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep3 <- lm(D1 ~ A1 + A2 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep4 <- lm(D1 ~ A1 + A2 + IV1 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep5 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep6 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep7 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep8 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep9 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep10 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV9 + IV10 + IV11 + IV12, data = noout)
modelStep11 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep12 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV11 + IV12, data = noout)
modelStep13 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV12, data = noout)
modelStep14 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 + IV11, data = noout)
summary(modelStep7)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV6 + IV7 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88726 -0.15633 0.00522 0.17830 0.45311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.632996 395.165734 0.138 0.890060
## A1No 0.019437 0.016361 1.188 0.235022
## A2Very likely 0.057128 0.016779 3.405 0.000681 ***
## A2Neither unlikely or likely -0.005381 0.020420 -0.264 0.792202
## A2Somewhat likely 0.069245 0.019727 3.510 0.000462 ***
## A2Somewhat unlikely 0.007309 0.022146 0.330 0.741405
## IV1 0.104501 0.020113 5.196 2.34e-07 ***
## IV2 0.040246 0.024176 1.665 0.096192 .
## IV3 -0.003271 0.007669 -0.427 0.669749
## IV4 -0.036482 0.007707 -4.733 2.43e-06 ***
## IV6 -0.017161 0.007204 -2.382 0.017347 *
## IV7 -0.011108 0.008718 -1.274 0.202847
## IV8 -2.258337 16.465293 -0.137 0.890926
## IV9 -2.242814 16.465191 -0.136 0.891670
## IV10 -2.263796 16.465229 -0.137 0.890664
## IV11 -2.262543 16.465295 -0.137 0.890724
## IV12 -2.260375 16.465238 -0.137 0.890828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07967, Adjusted R-squared: 0.06922
## F-statistic: 7.623 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07967, Adjusted R-squared: 0.06922
summary(modelStep6)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV5 + IV6 + IV7 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88724 -0.15634 0.00521 0.17830 0.45307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.744028 395.167050 0.136 0.891838
## A1No 0.019443 0.016361 1.188 0.234884
## A2Very likely 0.057131 0.016779 3.405 0.000680 ***
## A2Neither unlikely or likely -0.005381 0.020420 -0.264 0.792197
## A2Somewhat likely 0.069246 0.019727 3.510 0.000462 ***
## A2Somewhat unlikely 0.007320 0.022146 0.331 0.741053
## IV1 0.104503 0.020113 5.196 2.34e-07 ***
## IV2 0.040241 0.024176 1.664 0.096236 .
## IV3 0.033203 0.007404 4.485 7.90e-06 ***
## IV5 0.036473 0.007707 4.732 2.44e-06 ***
## IV6 0.019317 0.006707 2.880 0.004037 **
## IV7 0.025367 0.008361 3.034 0.002456 **
## IV8 -2.257773 16.465344 -0.137 0.890953
## IV9 -2.242254 16.465242 -0.136 0.891698
## IV10 -2.263231 16.465280 -0.137 0.890691
## IV11 -2.261981 16.465346 -0.137 0.890751
## IV12 -2.259811 16.465288 -0.137 0.890855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07966, Adjusted R-squared: 0.06921
## F-statistic: 7.622 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07966, Adjusted R-squared: 0.06921
summary(modelStep5)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88726 -0.15633 0.00521 0.17830 0.45310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.558832 395.168741 0.138 0.890209
## A1No 0.019437 0.016361 1.188 0.235011
## A2Very likely 0.057129 0.016779 3.405 0.000681 ***
## A2Neither unlikely or likely -0.005381 0.020420 -0.264 0.792197
## A2Somewhat likely 0.069245 0.019727 3.510 0.000462 ***
## A2Somewhat unlikely 0.007311 0.022146 0.330 0.741352
## IV1 0.104500 0.020113 5.196 2.34e-07 ***
## IV2 0.040246 0.024176 1.665 0.096198 .
## IV4 -0.033214 0.007405 -4.485 7.87e-06 ***
## IV5 0.003265 0.007669 0.426 0.670402
## IV6 -0.013892 0.006235 -2.228 0.026044 *
## IV7 -0.007839 0.007845 -0.999 0.317864
## IV8 -2.258517 16.465300 -0.137 0.890917
## IV9 -2.242993 16.465197 -0.136 0.891662
## IV10 -2.263972 16.465235 -0.138 0.890655
## IV11 -2.262722 16.465301 -0.137 0.890716
## IV12 -2.260554 16.465244 -0.137 0.890819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07967, Adjusted R-squared: 0.06921
## F-statistic: 7.623 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07967, Adjusted R-squared: 0.06921
summary(modelStep4)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV3 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86322 -0.15288 0.00332 0.17793 0.45312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 76.323794 402.996274 0.189 0.849814
## A1No 0.034181 0.013732 2.489 0.012920 *
## A2Very likely 0.057048 0.016796 3.397 0.000701 ***
## A2Neither unlikely or likely -0.003527 0.020409 -0.173 0.862830
## A2Somewhat likely 0.069841 0.019742 3.538 0.000417 ***
## A2Somewhat unlikely 0.015585 0.021578 0.722 0.470252
## IV1 0.100669 0.020003 5.033 5.46e-07 ***
## IV3 -1.069649 3.169431 -0.337 0.735798
## IV4 -1.103352 3.169872 -0.348 0.727836
## IV5 -1.066653 3.169702 -0.337 0.736533
## IV6 -1.083914 3.169723 -0.342 0.732433
## IV7 -1.078051 3.169516 -0.340 0.733809
## IV8 -2.095058 16.480532 -0.127 0.898861
## IV9 -2.079762 16.480430 -0.126 0.899595
## IV10 -2.100643 16.480468 -0.127 0.898592
## IV11 -2.099526 16.480534 -0.127 0.898647
## IV12 -2.097243 16.480477 -0.127 0.898756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2319 on 1409 degrees of freedom
## Multiple R-squared: 0.07793, Adjusted R-squared: 0.06746
## F-statistic: 7.443 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07967, Adjusted R-squared: 0.06921
summary(modelStep3)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82707 -0.15586 0.00703 0.17954 0.46098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 166.382042 406.133332 0.410 0.68211
## A1No 0.006553 0.016337 0.401 0.68842
## A2Very likely 0.063216 0.016897 3.741 0.00019 ***
## A2Neither unlikely or likely -0.016499 0.020500 -0.805 0.42104
## A2Somewhat likely 0.083428 0.019722 4.230 2.48e-05 ***
## A2Somewhat unlikely -0.011765 0.022073 -0.533 0.59410
## IV2 0.026193 0.024253 1.080 0.28033
## IV3 -1.221049 3.197073 -0.382 0.70257
## IV4 -1.260511 3.197510 -0.394 0.69348
## IV5 -1.221014 3.197343 -0.382 0.70261
## IV6 -1.239633 3.197361 -0.388 0.69829
## IV7 -1.231963 3.197151 -0.385 0.70005
## IV8 -5.694777 16.608209 -0.343 0.73173
## IV9 -5.673400 16.608150 -0.342 0.73270
## IV10 -5.697845 16.608161 -0.343 0.73159
## IV11 -5.693986 16.608248 -0.343 0.73177
## IV12 -5.691721 16.608192 -0.343 0.73187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2339 on 1409 degrees of freedom
## Multiple R-squared: 0.06213, Adjusted R-squared: 0.05148
## F-statistic: 5.834 on 16 and 1409 DF, p-value: 1.761e-12# Multiple R-squared: 0.06213, Adjusted R-squared: 0.05148
summary(modelStep2)##
## Call:
## lm(formula = D1 ~ A1 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86334 -0.16219 0.00692 0.17751 0.44115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.43224 404.49279 0.159 0.873
## A1No 0.01772 0.01637 1.083 0.279
## IV1 0.12667 0.01934 6.550 8.02e-11 ***
## IV2 0.03246 0.02355 1.378 0.168
## IV3 -1.21847 3.18609 -0.382 0.702
## IV4 -1.25172 3.18653 -0.393 0.695
## IV5 -1.21378 3.18638 -0.381 0.703
## IV6 -1.23210 3.18638 -0.387 0.699
## IV7 -1.22584 3.18617 -0.385 0.700
## IV8 -1.44818 16.54810 -0.088 0.930
## IV9 -1.43622 16.54803 -0.087 0.931
## IV10 -1.45696 16.54804 -0.088 0.930
## IV11 -1.45596 16.54813 -0.088 0.930
## IV12 -1.45165 16.54809 -0.088 0.930
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2333 on 1412 degrees of freedom
## Multiple R-squared: 0.06491, Adjusted R-squared: 0.0563
## F-statistic: 7.54 on 13 and 1412 DF, p-value: 1.449e-14# Multiple R-squared: 0.06491, Adjusted R-squared: 0.0563
summary(modelStep1)##
## Call:
## lm(formula = D1 ~ A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89120 -0.15660 0.00579 0.17867 0.45653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.831847 402.700507 0.178 0.858454
## A2Very likely 0.057052 0.016788 3.398 0.000697 ***
## A2Neither unlikely or likely -0.005173 0.020428 -0.253 0.800143
## A2Somewhat likely 0.067912 0.019704 3.447 0.000584 ***
## A2Somewhat unlikely 0.004646 0.022081 0.210 0.833388
## IV1 0.100941 0.019896 5.073 4.43e-07 ***
## IV2 0.055939 0.020277 2.759 0.005878 **
## IV3 -1.283137 3.167093 -0.405 0.685431
## IV4 -1.316674 3.167526 -0.416 0.677708
## IV5 -1.280007 3.167361 -0.404 0.686183
## IV6 -1.297498 3.167372 -0.410 0.682129
## IV7 -1.291022 3.167171 -0.408 0.683610
## IV8 -1.694109 16.465209 -0.103 0.918065
## IV9 -1.678922 16.465116 -0.102 0.918796
## IV10 -1.699921 16.465154 -0.103 0.917784
## IV11 -1.698571 16.465218 -0.103 0.917850
## IV12 -1.696545 16.465163 -0.103 0.917947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2318 on 1409 degrees of freedom
## Multiple R-squared: 0.07885, Adjusted R-squared: 0.06839
## F-statistic: 7.538 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07885, Adjusted R-squared: 0.06839
summary(modelStep) ##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88763 -0.15605 0.00551 0.17820 0.45371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.166020 402.760961 0.206 0.836438
## A1No 0.019259 0.016373 1.176 0.239674
## A2Very likely 0.057044 0.016785 3.398 0.000697 ***
## A2Neither unlikely or likely -0.005376 0.020426 -0.263 0.792430
## A2Somewhat likely 0.069225 0.019733 3.508 0.000465 ***
## A2Somewhat unlikely 0.006998 0.022168 0.316 0.752309
## IV1 0.104478 0.020120 5.193 2.37e-07 ***
## IV2 0.040422 0.024188 1.671 0.094918 .
## IV3 -1.173692 3.168028 -0.370 0.711080
## IV4 -1.207065 3.168465 -0.381 0.703289
## IV5 -1.170524 3.168298 -0.369 0.711849
## IV6 -1.187689 3.168317 -0.375 0.707818
## IV7 -1.181557 3.168107 -0.373 0.709239
## IV8 -2.276722 16.470416 -0.138 0.890078
## IV9 -2.261145 16.470314 -0.137 0.890824
## IV10 -2.282128 16.470352 -0.139 0.889818
## IV11 -2.280878 16.470417 -0.138 0.889878
## IV12 -2.278793 16.470361 -0.138 0.889978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2318 on 1408 degrees of freedom
## Multiple R-squared: 0.07976, Adjusted R-squared: 0.06864
## F-statistic: 7.178 on 17 and 1408 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07976, Adjusted R-squared: 0.06864
summary(modelStep14)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV9 + IV10 + IV11, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8876 -0.1563 0.0054 0.1782 0.4536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.443161 76.008356 0.374 0.708304
## A1No 0.019191 0.016360 1.173 0.240962
## A2Very likely 0.057086 0.016777 3.403 0.000686 ***
## A2Neither unlikely or likely -0.005353 0.020418 -0.262 0.793240
## A2Somewhat likely 0.069196 0.019725 3.508 0.000466 ***
## A2Somewhat unlikely 0.007159 0.022130 0.323 0.746374
## IV1 0.104589 0.020096 5.204 2.23e-07 ***
## IV2 0.040400 0.024179 1.671 0.094976 .
## IV3 -1.172382 3.166911 -0.370 0.711291
## IV4 -1.205739 3.167348 -0.381 0.703500
## IV5 -1.169197 3.167180 -0.369 0.712064
## IV6 -1.186370 3.167199 -0.375 0.708029
## IV7 -1.180219 3.166989 -0.373 0.709455
## IV8 0.002078 0.007464 0.278 0.780701
## IV9 0.017640 0.007685 2.295 0.021851 *
## IV10 -0.003337 0.008374 -0.399 0.690270
## IV11 -0.002078 0.007877 -0.264 0.791976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
## F-statistic: 7.631 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
summary(modelStep13)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV9 + IV10 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8876 -0.1563 0.0054 0.1782 0.4536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.393411 76.013659 0.374 0.708810
## A1No 0.019191 0.016360 1.173 0.240963
## A2Very likely 0.057086 0.016777 3.403 0.000686 ***
## A2Neither unlikely or likely -0.005353 0.020418 -0.262 0.793242
## A2Somewhat likely 0.069196 0.019725 3.508 0.000466 ***
## A2Somewhat unlikely 0.007159 0.022130 0.323 0.746367
## IV1 0.104589 0.020096 5.204 2.23e-07 ***
## IV2 0.040400 0.024179 1.671 0.094976 .
## IV3 -1.172387 3.166912 -0.370 0.711290
## IV4 -1.205743 3.167348 -0.381 0.703499
## IV5 -1.169202 3.167180 -0.369 0.712063
## IV6 -1.186375 3.167200 -0.375 0.708028
## IV7 -1.180224 3.166990 -0.373 0.709454
## IV8 0.004156 0.006240 0.666 0.505498
## IV9 0.019718 0.005959 3.309 0.000959 ***
## IV10 -0.001260 0.007071 -0.178 0.858629
## IV12 0.002077 0.007877 0.264 0.792027
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
## F-statistic: 7.631 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
summary(modelStep12)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV9 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8876 -0.1563 0.0054 0.1782 0.4536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.363164 76.013695 0.373 0.709106
## A1No 0.019191 0.016360 1.173 0.240964
## A2Very likely 0.057086 0.016777 3.403 0.000686 ***
## A2Neither unlikely or likely -0.005353 0.020418 -0.262 0.793243
## A2Somewhat likely 0.069196 0.019725 3.508 0.000466 ***
## A2Somewhat unlikely 0.007159 0.022130 0.324 0.746360
## IV1 0.104589 0.020096 5.204 2.23e-07 ***
## IV2 0.040400 0.024179 1.671 0.094976 .
## IV3 -1.172386 3.166912 -0.370 0.711290
## IV4 -1.205743 3.167348 -0.381 0.703499
## IV5 -1.169201 3.167180 -0.369 0.712063
## IV6 -1.186374 3.167200 -0.375 0.708028
## IV7 -1.180223 3.166990 -0.373 0.709455
## IV8 0.005416 0.007624 0.710 0.477585
## IV9 0.020978 0.007278 2.882 0.004007 **
## IV11 0.001259 0.007071 0.178 0.858676
## IV12 0.003337 0.008374 0.399 0.690322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
## F-statistic: 7.631 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
summary(modelStep11)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8876 -0.1563 0.0054 0.1782 0.4536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.866932 76.013878 0.380 0.704182
## A1No 0.019192 0.016360 1.173 0.240949
## A2Very likely 0.057086 0.016777 3.403 0.000686 ***
## A2Neither unlikely or likely -0.005353 0.020418 -0.262 0.793235
## A2Somewhat likely 0.069196 0.019725 3.508 0.000466 ***
## A2Somewhat unlikely 0.007158 0.022130 0.323 0.746414
## IV1 0.104588 0.020097 5.204 2.24e-07 ***
## IV2 0.040400 0.024179 1.671 0.094974 .
## IV3 -1.172399 3.166911 -0.370 0.711287
## IV4 -1.205755 3.167347 -0.381 0.703496
## IV5 -1.169214 3.167180 -0.369 0.712060
## IV6 -1.186387 3.167199 -0.375 0.708025
## IV7 -1.180236 3.166989 -0.373 0.709452
## IV8 -0.015562 0.006825 -2.280 0.022751 *
## IV10 -0.020978 0.007278 -2.882 0.004006 **
## IV11 -0.019719 0.005959 -3.309 0.000959 ***
## IV12 -0.017641 0.007685 -2.296 0.021847 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
## F-statistic: 7.631 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
summary(modelStep10)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8876 -0.1563 0.0054 0.1782 0.4536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.493133 76.010441 0.375 0.707822
## A1No 0.019191 0.016360 1.173 0.240961
## A2Very likely 0.057086 0.016777 3.403 0.000686 ***
## A2Neither unlikely or likely -0.005353 0.020418 -0.262 0.793242
## A2Somewhat likely 0.069196 0.019725 3.508 0.000466 ***
## A2Somewhat unlikely 0.007159 0.022130 0.323 0.746374
## IV1 0.104589 0.020096 5.204 2.23e-07 ***
## IV2 0.040400 0.024179 1.671 0.094976 .
## IV3 -1.172386 3.166911 -0.370 0.711290
## IV4 -1.205742 3.167348 -0.381 0.703499
## IV5 -1.169201 3.167180 -0.369 0.712063
## IV6 -1.186374 3.167199 -0.375 0.708028
## IV7 -1.180223 3.166989 -0.373 0.709455
## IV9 0.015562 0.006825 2.280 0.022755 *
## IV10 -0.005416 0.007623 -0.710 0.477546
## IV11 -0.004156 0.006240 -0.666 0.505465
## IV12 -0.002079 0.007464 -0.279 0.780653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
## F-statistic: 7.631 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
summary(modelStep9)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88725 -0.15633 0.00521 0.17830 0.45310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.358535 395.164118 0.138 0.890608
## A1No 0.019439 0.016361 1.188 0.234982
## A2Very likely 0.057129 0.016779 3.405 0.000680 ***
## A2Neither unlikely or likely -0.005380 0.020420 -0.263 0.792210
## A2Somewhat likely 0.069245 0.019727 3.510 0.000462 ***
## A2Somewhat unlikely 0.007313 0.022146 0.330 0.741262
## IV1 0.104501 0.020113 5.196 2.34e-07 ***
## IV2 0.040245 0.024176 1.665 0.096201 .
## IV3 0.007832 0.007845 0.998 0.318311
## IV4 -0.025378 0.008361 -3.035 0.002448 **
## IV5 0.011100 0.008719 1.273 0.203210
## IV6 -0.006057 0.008241 -0.735 0.462463
## IV8 -2.258005 16.465307 -0.137 0.890942
## IV9 -2.242483 16.465205 -0.136 0.891686
## IV10 -2.263462 16.465243 -0.137 0.890680
## IV11 -2.262212 16.465309 -0.137 0.890740
## IV12 -2.260046 16.465252 -0.137 0.890844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07966, Adjusted R-squared: 0.06921
## F-statistic: 7.623 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07966, Adjusted R-squared: 0.06921
summary(modelStep8)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV7 +
## IV8 + IV9 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88725 -0.15634 0.00521 0.17830 0.45309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.217655 395.167806 0.137 0.890891
## A1No 0.019440 0.016361 1.188 0.234945
## A2Very likely 0.057130 0.016779 3.405 0.000680 ***
## A2Neither unlikely or likely -0.005380 0.020420 -0.263 0.792226
## A2Somewhat likely 0.069246 0.019727 3.510 0.000462 ***
## A2Somewhat unlikely 0.007315 0.022146 0.330 0.741203
## IV1 0.104502 0.020113 5.196 2.34e-07 ***
## IV2 0.040244 0.024176 1.665 0.096212 .
## IV3 0.013886 0.006235 2.227 0.026093 *
## IV4 -0.019323 0.006708 -2.881 0.004027 **
## IV5 0.017154 0.007204 2.381 0.017387 *
## IV7 0.006049 0.008241 0.734 0.463056
## IV8 -2.258189 16.465318 -0.137 0.890933
## IV9 -2.242667 16.465216 -0.136 0.891678
## IV10 -2.263646 16.465254 -0.137 0.890671
## IV11 -2.262397 16.465319 -0.137 0.890731
## IV12 -2.260227 16.465262 -0.137 0.890835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07966, Adjusted R-squared: 0.06921
## F-statistic: 7.623 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07966, Adjusted R-squared: 0.06921 modelStep11 <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11a <- lm(D1 ~ A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11b <- lm(D1 ~ A1 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11c <- lm(D1 ~ A1 + A2 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11d <- lm(D1 ~ A1 + A2 + IV1 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11e <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11f <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11g <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV6 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11h <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11i <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11j <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV10 + IV11 + IV12, data = noout)
modelStep11k <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV11 + IV12, data = noout)
modelStep11l <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV12, data = noout)
modelStep11m <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11, data = noout)
summary(modelStep11)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8876 -0.1563 0.0054 0.1782 0.4536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.866932 76.013878 0.380 0.704182
## A1No 0.019192 0.016360 1.173 0.240949
## A2Very likely 0.057086 0.016777 3.403 0.000686 ***
## A2Neither unlikely or likely -0.005353 0.020418 -0.262 0.793235
## A2Somewhat likely 0.069196 0.019725 3.508 0.000466 ***
## A2Somewhat unlikely 0.007158 0.022130 0.323 0.746414
## IV1 0.104588 0.020097 5.204 2.24e-07 ***
## IV2 0.040400 0.024179 1.671 0.094974 .
## IV3 -1.172399 3.166911 -0.370 0.711287
## IV4 -1.205755 3.167347 -0.381 0.703496
## IV5 -1.169214 3.167180 -0.369 0.712060
## IV6 -1.186387 3.167199 -0.375 0.708025
## IV7 -1.180236 3.166989 -0.373 0.709452
## IV8 -0.015562 0.006825 -2.280 0.022751 *
## IV10 -0.020978 0.007278 -2.882 0.004006 **
## IV11 -0.019719 0.005959 -3.309 0.000959 ***
## IV12 -0.017641 0.007685 -2.296 0.021847 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1409 degrees of freedom
## Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
## F-statistic: 7.631 on 16 and 1409 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07974, Adjusted R-squared: 0.06929
summary(modelStep11a)##
## Call:
## lm(formula = D1 ~ A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89116 -0.15659 0.00576 0.17867 0.45645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.507354 75.990683 0.415 0.678482
## A2Very likely 0.057083 0.016779 3.402 0.000688 ***
## A2Neither unlikely or likely -0.005156 0.020420 -0.252 0.800705
## A2Somewhat likely 0.067894 0.019696 3.447 0.000583 ***
## A2Somewhat unlikely 0.004771 0.022039 0.216 0.828652
## IV1 0.101032 0.019869 5.085 4.17e-07 ***
## IV2 0.055882 0.020262 2.758 0.005892 **
## IV3 -1.281890 3.165958 -0.405 0.685614
## IV4 -1.315415 3.166390 -0.415 0.677890
## IV5 -1.278747 3.166226 -0.404 0.686369
## IV6 -1.296245 3.166237 -0.409 0.682311
## IV7 -1.289754 3.166035 -0.407 0.683797
## IV8 -0.015178 0.006818 -2.226 0.026168 *
## IV10 -0.020996 0.007279 -2.884 0.003980 **
## IV11 -0.019639 0.005959 -3.296 0.001006 **
## IV12 -0.017618 0.007686 -2.292 0.022036 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1410 degrees of freedom
## Multiple R-squared: 0.07884, Adjusted R-squared: 0.06904
## F-statistic: 8.046 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07884, Adjusted R-squared: 0.06904
summary(modelStep11b)##
## Call:
## lm(formula = D1 ~ A1 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86334 -0.16223 0.00689 0.17746 0.44116
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.959075 76.447235 0.392 0.69520
## A1No 0.017669 0.016350 1.081 0.28002
## IV1 0.126720 0.019323 6.558 7.64e-11 ***
## IV2 0.032477 0.023545 1.379 0.16800
## IV3 -1.218318 3.184975 -0.383 0.70213
## IV4 -1.251563 3.185414 -0.393 0.69445
## IV5 -1.213614 3.185257 -0.381 0.70325
## IV6 -1.231950 3.185260 -0.387 0.69899
## IV7 -1.225674 3.185047 -0.385 0.70043
## IV8 -0.011961 0.006812 -1.756 0.07933 .
## IV10 -0.020739 0.007313 -2.836 0.00463 **
## IV11 -0.019737 0.005988 -3.296 0.00101 **
## IV12 -0.015424 0.007711 -2.000 0.04566 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2332 on 1413 degrees of freedom
## Multiple R-squared: 0.0649, Adjusted R-squared: 0.05696
## F-statistic: 8.173 on 12 and 1413 DF, p-value: 4.924e-15# Multiple R-squared: 0.0649, Adjusted R-squared: 0.05696
summary(modelStep11c)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82680 -0.15607 0.00721 0.17956 0.46077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.144530 76.713378 0.393 0.694416
## A1No 0.006349 0.016321 0.389 0.697325
## A2Very likely 0.063337 0.016888 3.750 0.000184 ***
## A2Neither unlikely or likely -0.016470 0.020493 -0.804 0.421714
## A2Somewhat likely 0.083392 0.019715 4.230 2.49e-05 ***
## A2Somewhat unlikely -0.011413 0.022042 -0.518 0.604690
## IV2 0.026100 0.024244 1.077 0.281857
## IV3 -1.217923 3.196058 -0.381 0.703209
## IV4 -1.257362 3.196495 -0.393 0.694116
## IV5 -1.217855 3.196328 -0.381 0.703248
## IV6 -1.236499 3.196346 -0.387 0.698927
## IV7 -1.228776 3.196136 -0.384 0.700698
## IV8 -0.021358 0.006796 -3.143 0.001708 **
## IV10 -0.024442 0.007314 -3.342 0.000854 ***
## IV11 -0.020553 0.006011 -3.419 0.000646 ***
## IV12 -0.018307 0.007755 -2.361 0.018368 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2338 on 1410 degrees of freedom
## Multiple R-squared: 0.06205, Adjusted R-squared: 0.05208
## F-statistic: 6.219 on 15 and 1410 DF, p-value: 7.211e-13# Multiple R-squared: 0.06205, Adjusted R-squared: 0.05208
summary(modelStep11d)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV3 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86320 -0.15286 0.00335 0.17797 0.45303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.381675 76.047596 0.347 0.728711
## A1No 0.034112 0.013716 2.487 0.013000 *
## A2Very likely 0.057086 0.016787 3.401 0.000691 ***
## A2Neither unlikely or likely -0.003506 0.020401 -0.172 0.863571
## A2Somewhat likely 0.069814 0.019734 3.538 0.000417 ***
## A2Somewhat unlikely 0.015728 0.021541 0.730 0.465422
## IV1 0.100773 0.019979 5.044 5.15e-07 ***
## IV3 -1.068511 3.168312 -0.337 0.735979
## IV4 -1.102199 3.168752 -0.348 0.728017
## IV5 -1.065499 3.168583 -0.336 0.736717
## IV6 -1.082768 3.168603 -0.342 0.732614
## IV7 -1.076887 3.168396 -0.340 0.733995
## IV8 -0.015284 0.006828 -2.238 0.025345 *
## IV10 -0.020876 0.007282 -2.867 0.004209 **
## IV11 -0.019750 0.005962 -3.313 0.000948 ***
## IV12 -0.017476 0.007689 -2.273 0.023189 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2319 on 1410 degrees of freedom
## Multiple R-squared: 0.07792, Adjusted R-squared: 0.06811
## F-statistic: 7.943 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07792, Adjusted R-squared: 0.06811
summary(modelStep11e)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88721 -0.15644 0.00511 0.17826 0.45301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.726463 0.160492 4.526 6.50e-06 ***
## A1No 0.019370 0.016348 1.185 0.236256
## A2Very likely 0.057170 0.016770 3.409 0.000670 ***
## A2Neither unlikely or likely -0.005358 0.020412 -0.262 0.792995
## A2Somewhat likely 0.069216 0.019719 3.510 0.000462 ***
## A2Somewhat unlikely 0.007469 0.022107 0.338 0.735517
## IV1 0.104609 0.020090 5.207 2.20e-07 ***
## IV2 0.040224 0.024167 1.664 0.096253 .
## IV4 -0.033198 0.007401 -4.485 7.87e-06 ***
## IV5 0.003281 0.007666 0.428 0.668712
## IV6 -0.013884 0.006233 -2.228 0.026069 *
## IV7 -0.007812 0.007840 -0.996 0.319227
## IV8 -0.015510 0.006822 -2.274 0.023137 *
## IV10 -0.020975 0.007276 -2.883 0.004001 **
## IV11 -0.019715 0.005957 -3.310 0.000957 ***
## IV12 -0.017555 0.007679 -2.286 0.022395 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1410 degrees of freedom
## Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
## F-statistic: 8.135 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
summary(modelStep11f)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV5 + IV6 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8872 -0.1564 0.0051 0.1783 0.4530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.070240 0.076465 -0.919 0.358465
## A1No 0.019376 0.016348 1.185 0.236129
## A2Very likely 0.057172 0.016770 3.409 0.000670 ***
## A2Neither unlikely or likely -0.005358 0.020412 -0.262 0.792995
## A2Somewhat likely 0.069217 0.019719 3.510 0.000462 ***
## A2Somewhat unlikely 0.007478 0.022107 0.338 0.735220
## IV1 0.104612 0.020090 5.207 2.20e-07 ***
## IV2 0.040220 0.024167 1.664 0.096290 .
## IV3 0.033187 0.007400 4.485 7.90e-06 ***
## IV5 0.036474 0.007704 4.734 2.42e-06 ***
## IV6 0.019309 0.006705 2.880 0.004038 **
## IV7 0.025379 0.008357 3.037 0.002435 **
## IV8 -0.015505 0.006822 -2.273 0.023180 *
## IV10 -0.020972 0.007276 -2.883 0.004005 **
## IV11 -0.019713 0.005957 -3.309 0.000959 ***
## IV12 -0.017551 0.007679 -2.286 0.022427 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1410 degrees of freedom
## Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
## F-statistic: 8.135 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
summary(modelStep11g)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV6 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88722 -0.15645 0.00511 0.17827 0.45302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.805305 0.178334 4.516 6.84e-06 ***
## A1No 0.019370 0.016348 1.185 0.236267
## A2Very likely 0.057169 0.016770 3.409 0.000670 ***
## A2Neither unlikely or likely -0.005357 0.020412 -0.262 0.793001
## A2Somewhat likely 0.069216 0.019719 3.510 0.000462 ***
## A2Somewhat unlikely 0.007468 0.022107 0.338 0.735570
## IV1 0.104610 0.020090 5.207 2.20e-07 ***
## IV2 0.040225 0.024167 1.664 0.096246 .
## IV3 -0.003288 0.007665 -0.429 0.668060
## IV4 -0.036483 0.007705 -4.735 2.41e-06 ***
## IV6 -0.017169 0.007201 -2.384 0.017249 *
## IV7 -0.011097 0.008715 -1.273 0.203121
## IV8 -0.015510 0.006822 -2.274 0.023143 *
## IV10 -0.020977 0.007276 -2.883 0.003996 **
## IV11 -0.019715 0.005957 -3.310 0.000957 ***
## IV12 -0.017555 0.007679 -2.286 0.022394 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1410 degrees of freedom
## Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
## F-statistic: 8.136 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
summary(modelStep11h)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8872 -0.1564 0.0051 0.1783 0.4530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.393294 0.150759 2.609 0.009183 **
## A1No 0.019373 0.016348 1.185 0.236190
## A2Very likely 0.057171 0.016770 3.409 0.000670 ***
## A2Neither unlikely or likely -0.005357 0.020412 -0.262 0.793024
## A2Somewhat likely 0.069217 0.019719 3.510 0.000462 ***
## A2Somewhat unlikely 0.007474 0.022107 0.338 0.735369
## IV1 0.104611 0.020090 5.207 2.2e-07 ***
## IV2 0.040222 0.024167 1.664 0.096267 .
## IV3 0.013878 0.006232 2.227 0.026118 *
## IV4 -0.019315 0.006705 -2.881 0.004028 **
## IV5 0.017163 0.007201 2.383 0.017289 *
## IV7 0.006068 0.008236 0.737 0.461387
## IV8 -0.015509 0.006822 -2.273 0.023151 *
## IV10 -0.020975 0.007276 -2.883 0.004001 **
## IV11 -0.019717 0.005957 -3.310 0.000957 ***
## IV12 -0.017554 0.007679 -2.286 0.022406 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1410 degrees of freedom
## Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
## F-statistic: 8.135 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
summary(modelStep11i)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88721 -0.15645 0.00511 0.17826 0.45302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.539065 0.178796 3.015 0.002616 **
## A1No 0.019371 0.016348 1.185 0.236227
## A2Very likely 0.057170 0.016770 3.409 0.000670 ***
## A2Neither unlikely or likely -0.005357 0.020412 -0.262 0.793008
## A2Somewhat likely 0.069216 0.019719 3.510 0.000462 ***
## A2Somewhat unlikely 0.007472 0.022107 0.338 0.735428
## IV1 0.104610 0.020090 5.207 2.2e-07 ***
## IV2 0.040224 0.024167 1.664 0.096256 .
## IV3 0.007804 0.007840 0.995 0.319674
## IV4 -0.025390 0.008358 -3.038 0.002427 **
## IV5 0.011089 0.008716 1.272 0.203484
## IV6 -0.006077 0.008237 -0.738 0.460796
## IV8 -0.015509 0.006822 -2.273 0.023150 *
## IV10 -0.020974 0.007276 -2.883 0.004001 **
## IV11 -0.019715 0.005957 -3.310 0.000958 ***
## IV12 -0.017557 0.007679 -2.286 0.022381 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1410 degrees of freedom
## Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
## F-statistic: 8.135 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
summary(modelStep11j)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89855 -0.15454 0.00808 0.17718 0.42667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.038055 76.108395 0.329 0.742221
## A1No 0.017401 0.016365 1.063 0.287819
## A2Very likely 0.055586 0.016789 3.311 0.000954 ***
## A2Neither unlikely or likely -0.004107 0.020441 -0.201 0.840808
## A2Somewhat likely 0.065116 0.019673 3.310 0.000957 ***
## A2Somewhat unlikely 0.009415 0.022141 0.425 0.670733
## IV1 0.112064 0.019857 5.644 2.01e-08 ***
## IV2 0.039052 0.024208 1.613 0.106927
## IV3 -1.023803 3.170951 -0.323 0.746842
## IV4 -1.046508 3.171289 -0.330 0.741453
## IV5 -1.017898 3.171196 -0.321 0.748271
## IV6 -1.034786 3.171212 -0.326 0.744241
## IV7 -1.027876 3.170995 -0.324 0.745873
## IV10 -0.014057 0.006625 -2.122 0.034014 *
## IV11 -0.012511 0.005058 -2.473 0.013505 *
## IV12 -0.009301 0.006769 -1.374 0.169617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.232 on 1410 degrees of freedom
## Multiple R-squared: 0.07635, Adjusted R-squared: 0.06652
## F-statistic: 7.77 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07635, Adjusted R-squared: 0.06652
summary(modelStep11k)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.87824 -0.15761 0.00958 0.18069 0.42754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.373709 76.210431 0.372 0.709720
## A1No 0.019288 0.016402 1.176 0.239803
## A2Very likely 0.057766 0.016819 3.435 0.000611 ***
## A2Neither unlikely or likely -0.005681 0.020471 -0.277 0.781440
## A2Somewhat likely 0.068403 0.019774 3.459 0.000558 ***
## A2Somewhat unlikely 0.010133 0.022163 0.457 0.647598
## IV1 0.109886 0.020064 5.477 5.12e-08 ***
## IV2 0.039816 0.024241 1.643 0.100704
## IV3 -1.160248 3.175105 -0.365 0.714853
## IV4 -1.186701 3.175538 -0.374 0.708683
## IV5 -1.166323 3.175377 -0.367 0.713449
## IV6 -1.174216 3.175394 -0.370 0.711598
## IV7 -1.167624 3.175183 -0.368 0.713126
## IV8 -0.007357 0.006219 -1.183 0.237042
## IV11 -0.012101 0.005354 -2.260 0.023970 *
## IV12 -0.009342 0.007144 -1.308 0.191156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2323 on 1410 degrees of freedom
## Multiple R-squared: 0.07432, Adjusted R-squared: 0.06447
## F-statistic: 7.547 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07432, Adjusted R-squared: 0.06447
summary(modelStep11l)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV10 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88757 -0.15571 0.00796 0.17971 0.44284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.229324 76.281401 0.370 0.711387
## A1No 0.018574 0.016416 1.131 0.258066
## A2Very likely 0.057580 0.016835 3.420 0.000644 ***
## A2Neither unlikely or likely -0.007231 0.020482 -0.353 0.724126
## A2Somewhat likely 0.066752 0.019780 3.375 0.000759 ***
## A2Somewhat unlikely 0.005757 0.022204 0.259 0.795466
## IV1 0.106378 0.020160 5.277 1.52e-07 ***
## IV2 0.040656 0.024264 1.676 0.094052 .
## IV3 -1.157239 3.178064 -0.364 0.715813
## IV4 -1.180538 3.178495 -0.371 0.710385
## IV5 -1.153063 3.178333 -0.363 0.716817
## IV6 -1.176743 3.178355 -0.370 0.711262
## IV7 -1.162919 3.178141 -0.366 0.714486
## IV8 -0.003580 0.005806 -0.617 0.537638
## IV10 -0.010296 0.006546 -1.573 0.115966
## IV12 -0.008612 0.007209 -1.194 0.232491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2325 on 1410 degrees of freedom
## Multiple R-squared: 0.07259, Adjusted R-squared: 0.06272
## F-statistic: 7.358 on 15 and 1410 DF, p-value: 6.782e-16# Multiple R-squared: 0.07259, Adjusted R-squared: 0.06272
summary(modelStep11m)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV10 + IV11, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88173 -0.15359 0.00545 0.17498 0.42740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.426758 76.091872 0.308 0.758223
## A1No 0.019096 0.016384 1.166 0.244007
## A2Very likely 0.054476 0.016764 3.250 0.001183 **
## A2Neither unlikely or likely -0.006901 0.020438 -0.338 0.735686
## A2Somewhat likely 0.066360 0.019716 3.366 0.000784 ***
## A2Somewhat unlikely 0.004552 0.022134 0.206 0.837106
## IV1 0.105357 0.020124 5.235 1.9e-07 ***
## IV2 0.039685 0.024214 1.639 0.101451
## IV3 -0.952601 3.170252 -0.300 0.763854
## IV4 -0.980964 3.170623 -0.309 0.757069
## IV5 -0.949256 3.170520 -0.299 0.764678
## IV6 -0.965593 3.170528 -0.305 0.760752
## IV7 -0.968142 3.170430 -0.305 0.760132
## IV8 -0.008105 0.006012 -1.348 0.177817
## IV10 -0.014719 0.006758 -2.178 0.029572 *
## IV11 -0.014862 0.005579 -2.664 0.007809 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2321 on 1410 degrees of freedom
## Multiple R-squared: 0.0763, Adjusted R-squared: 0.06647
## F-statistic: 7.765 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.0763, Adjusted R-squared: 0.06647 modelStep11i <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ia <- lm(D1 ~ A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ib <- lm(D1 ~ A1 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ic <- lm(D1 ~ A1 + A2 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11id <- lm(D1 ~ A1 + A2 + IV1 + IV3 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ie <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11if <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ig <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ih <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV7 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ii <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV10 + IV11 + IV12, data = noout)
modelStep11ij <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV11 + IV12, data = noout)
modelStep11ik <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV12, data = noout)
modelStep11il <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV10 + IV11, data = noout)
summary(modelStep11i)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88721 -0.15645 0.00511 0.17826 0.45302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.539065 0.178796 3.015 0.002616 **
## A1No 0.019371 0.016348 1.185 0.236227
## A2Very likely 0.057170 0.016770 3.409 0.000670 ***
## A2Neither unlikely or likely -0.005357 0.020412 -0.262 0.793008
## A2Somewhat likely 0.069216 0.019719 3.510 0.000462 ***
## A2Somewhat unlikely 0.007472 0.022107 0.338 0.735428
## IV1 0.104610 0.020090 5.207 2.2e-07 ***
## IV2 0.040224 0.024167 1.664 0.096256 .
## IV3 0.007804 0.007840 0.995 0.319674
## IV4 -0.025390 0.008358 -3.038 0.002427 **
## IV5 0.011089 0.008716 1.272 0.203484
## IV6 -0.006077 0.008237 -0.738 0.460796
## IV8 -0.015509 0.006822 -2.273 0.023150 *
## IV10 -0.020974 0.007276 -2.883 0.004001 **
## IV11 -0.019715 0.005957 -3.310 0.000958 ***
## IV12 -0.017557 0.007679 -2.286 0.022381 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1410 degrees of freedom
## Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
## F-statistic: 8.135 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
summary(modelStep11ia)##
## Call:
## lm(formula = D1 ~ A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 +
## IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89078 -0.15656 0.00571 0.17881 0.45582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.550956 0.178540 3.086 0.002069 **
## A2Very likely 0.057176 0.016772 3.409 0.000671 ***
## A2Neither unlikely or likely -0.005159 0.020414 -0.253 0.800543
## A2Somewhat likely 0.067902 0.019690 3.449 0.000580 ***
## A2Somewhat unlikely 0.005090 0.022019 0.231 0.817218
## IV1 0.101020 0.019863 5.086 4.15e-07 ***
## IV2 0.055848 0.020256 2.757 0.005907 **
## IV3 0.007829 0.007841 0.998 0.318224
## IV4 -0.025521 0.008359 -3.053 0.002306 **
## IV5 0.011080 0.008717 1.271 0.203917
## IV6 -0.006413 0.008233 -0.779 0.436191
## IV8 -0.015115 0.006815 -2.218 0.026708 *
## IV10 -0.020992 0.007277 -2.885 0.003976 **
## IV11 -0.019634 0.005957 -3.296 0.001006 **
## IV12 -0.017526 0.007680 -2.282 0.022637 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1411 degrees of freedom
## Multiple R-squared: 0.07874, Adjusted R-squared: 0.0696
## F-statistic: 8.614 on 14 and 1411 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07874, Adjusted R-squared: 0.0696
summary(modelStep11ib)##
## Call:
## lm(formula = D1 ~ A1 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 +
## IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8630 -0.1622 0.0071 0.1775 0.4407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.540625 0.179854 3.006 0.00269 **
## A1No 0.017832 0.016339 1.091 0.27530
## IV1 0.126697 0.019318 6.559 7.59e-11 ***
## IV2 0.032380 0.023537 1.376 0.16912
## IV3 0.007325 0.007888 0.929 0.35322
## IV4 -0.025752 0.008407 -3.063 0.00223 **
## IV5 0.012136 0.008759 1.386 0.16608
## IV6 -0.006198 0.008289 -0.748 0.45476
## IV8 -0.011910 0.006809 -1.749 0.08046 .
## IV10 -0.020742 0.007311 -2.837 0.00462 **
## IV11 -0.019733 0.005987 -3.296 0.00100 **
## IV12 -0.015333 0.007705 -1.990 0.04679 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2332 on 1414 degrees of freedom
## Multiple R-squared: 0.06481, Adjusted R-squared: 0.05753
## F-statistic: 8.908 on 11 and 1414 DF, p-value: 1.705e-15# Multiple R-squared: 0.06481, Adjusted R-squared: 0.05753
summary(modelStep11ic)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV2 + IV3 + IV4 + IV5 + IV6 + IV8 +
## IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82639 -0.15631 0.00702 0.17980 0.46014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.651637 0.179119 3.638 0.000285 ***
## A1No 0.006534 0.016309 0.401 0.688777
## A2Very likely 0.063427 0.016881 3.757 0.000179 ***
## A2Neither unlikely or likely -0.016477 0.020487 -0.804 0.421376
## A2Somewhat likely 0.083416 0.019709 4.232 2.46e-05 ***
## A2Somewhat unlikely -0.011090 0.022019 -0.504 0.614588
## IV2 0.025914 0.024232 1.069 0.285064
## IV3 0.010820 0.007891 1.371 0.170516
## IV4 -0.028452 0.008414 -3.381 0.000741 ***
## IV5 0.010990 0.008796 1.249 0.211694
## IV6 -0.007646 0.008307 -0.920 0.357497
## IV8 -0.021303 0.006792 -3.136 0.001746 **
## IV10 -0.024439 0.007312 -3.342 0.000853 ***
## IV11 -0.020550 0.006009 -3.420 0.000645 ***
## IV12 -0.018221 0.007749 -2.351 0.018841 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2338 on 1411 degrees of freedom
## Multiple R-squared: 0.06196, Adjusted R-squared: 0.05265
## F-statistic: 6.657 on 14 and 1411 DF, p-value: 2.891e-13# Multiple R-squared: 0.06196, Adjusted R-squared: 0.05265
summary(modelStep11id)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV3 + IV4 + IV5 + IV6 + IV8 +
## IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86294 -0.15301 0.00321 0.17786 0.45248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.534390 0.178886 2.987 0.002863 **
## A1No 0.034217 0.013709 2.496 0.012674 *
## A2Very likely 0.057164 0.016781 3.407 0.000676 ***
## A2Neither unlikely or likely -0.003518 0.020395 -0.172 0.863092
## A2Somewhat likely 0.069830 0.019728 3.540 0.000414 ***
## A2Somewhat unlikely 0.015981 0.021522 0.743 0.457876
## IV1 0.100808 0.019972 5.047 5.06e-07 ***
## IV3 0.008344 0.007838 1.065 0.287269
## IV4 -0.025195 0.008363 -3.013 0.002635 **
## IV5 0.011447 0.008718 1.313 0.189399
## IV6 -0.005815 0.008241 -0.706 0.480552
## IV8 -0.015236 0.006824 -2.233 0.025728 *
## IV10 -0.020873 0.007280 -2.867 0.004203 **
## IV11 -0.019747 0.005960 -3.313 0.000946 ***
## IV12 -0.017400 0.007683 -2.265 0.023688 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2318 on 1411 degrees of freedom
## Multiple R-squared: 0.07784, Adjusted R-squared: 0.06869
## F-statistic: 8.508 on 14 and 1411 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07784, Adjusted R-squared: 0.06869
summary(modelStep11ie)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 +
## IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88149 -0.15667 0.00614 0.17812 0.46227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.649155 0.140491 4.621 4.18e-06 ***
## A1No 0.019414 0.016347 1.188 0.235187
## A2Very likely 0.057236 0.016770 3.413 0.000661 ***
## A2Neither unlikely or likely -0.004947 0.020408 -0.242 0.808516
## A2Somewhat likely 0.069227 0.019719 3.511 0.000461 ***
## A2Somewhat unlikely 0.008026 0.022100 0.363 0.716520
## IV1 0.106088 0.020035 5.295 1.38e-07 ***
## IV2 0.041218 0.024147 1.707 0.088039 .
## IV4 -0.030250 0.006784 -4.459 8.89e-06 ***
## IV5 0.006094 0.007127 0.855 0.392624
## IV6 -0.011820 0.005879 -2.011 0.044547 *
## IV8 -0.014081 0.006669 -2.111 0.034923 *
## IV10 -0.020827 0.007274 -2.863 0.004257 **
## IV11 -0.019222 0.005936 -3.238 0.001231 **
## IV12 -0.020833 0.006939 -3.002 0.002726 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1411 degrees of freedom
## Multiple R-squared: 0.07901, Adjusted R-squared: 0.06987
## F-statistic: 8.646 on 14 and 1411 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07901, Adjusted R-squared: 0.06987
summary(modelStep11if)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV5 + IV6 + IV8 +
## IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88558 -0.15266 0.00393 0.17515 0.43769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.036600 0.068088 0.538 0.590976
## A1No 0.020029 0.016394 1.222 0.222017
## A2Very likely 0.057607 0.016818 3.425 0.000632 ***
## A2Neither unlikely or likely -0.006148 0.020470 -0.300 0.763964
## A2Somewhat likely 0.068312 0.019774 3.455 0.000567 ***
## A2Somewhat unlikely 0.005635 0.022163 0.254 0.799339
## IV1 0.108904 0.020099 5.418 7.06e-08 ***
## IV2 0.039193 0.024235 1.617 0.106064
## IV3 0.021716 0.006382 3.403 0.000686 ***
## IV5 0.026801 0.007035 3.809 0.000145 ***
## IV6 0.010778 0.006106 1.765 0.077726 .
## IV8 -0.008038 0.006382 -1.260 0.208028
## IV10 -0.015067 0.007031 -2.143 0.032300 *
## IV11 -0.014549 0.005725 -2.541 0.011157 *
## IV12 -0.002146 0.005782 -0.371 0.710523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2323 on 1411 degrees of freedom
## Multiple R-squared: 0.07363, Adjusted R-squared: 0.06444
## F-statistic: 8.011 on 14 and 1411 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07363, Adjusted R-squared: 0.06444
summary(modelStep11ig)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88721 -0.15645 0.00511 0.17826 0.45302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.539065 0.178796 3.015 0.002616 **
## A1No 0.019371 0.016348 1.185 0.236227
## A2Very likely 0.057170 0.016770 3.409 0.000670 ***
## A2Neither unlikely or likely -0.005357 0.020412 -0.262 0.793008
## A2Somewhat likely 0.069216 0.019719 3.510 0.000462 ***
## A2Somewhat unlikely 0.007472 0.022107 0.338 0.735428
## IV1 0.104610 0.020090 5.207 2.2e-07 ***
## IV2 0.040224 0.024167 1.664 0.096256 .
## IV3 0.007804 0.007840 0.995 0.319674
## IV4 -0.025390 0.008358 -3.038 0.002427 **
## IV5 0.011089 0.008716 1.272 0.203484
## IV6 -0.006077 0.008237 -0.738 0.460796
## IV8 -0.015509 0.006822 -2.273 0.023150 *
## IV10 -0.020974 0.007276 -2.883 0.004001 **
## IV11 -0.019715 0.005957 -3.310 0.000958 ***
## IV12 -0.017557 0.007679 -2.286 0.022381 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1410 degrees of freedom
## Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
## F-statistic: 8.135 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
summary(modelStep11ih)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV7 +
## IV8 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8872 -0.1564 0.0051 0.1783 0.4530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.393294 0.150759 2.609 0.009183 **
## A1No 0.019373 0.016348 1.185 0.236190
## A2Very likely 0.057171 0.016770 3.409 0.000670 ***
## A2Neither unlikely or likely -0.005357 0.020412 -0.262 0.793024
## A2Somewhat likely 0.069217 0.019719 3.510 0.000462 ***
## A2Somewhat unlikely 0.007474 0.022107 0.338 0.735369
## IV1 0.104611 0.020090 5.207 2.2e-07 ***
## IV2 0.040222 0.024167 1.664 0.096267 .
## IV3 0.013878 0.006232 2.227 0.026118 *
## IV4 -0.019315 0.006705 -2.881 0.004028 **
## IV5 0.017163 0.007201 2.383 0.017289 *
## IV7 0.006068 0.008236 0.737 0.461387
## IV8 -0.015509 0.006822 -2.273 0.023151 *
## IV10 -0.020975 0.007276 -2.883 0.004001 **
## IV11 -0.019717 0.005957 -3.310 0.000957 ***
## IV12 -0.017554 0.007679 -2.286 0.022406 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1410 degrees of freedom
## Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
## F-statistic: 8.135 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07965, Adjusted R-squared: 0.06986
summary(modelStep11ii)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89855 -0.15454 0.00808 0.17718 0.42667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.038055 76.108395 0.329 0.742221
## A1No 0.017401 0.016365 1.063 0.287819
## A2Very likely 0.055586 0.016789 3.311 0.000954 ***
## A2Neither unlikely or likely -0.004107 0.020441 -0.201 0.840808
## A2Somewhat likely 0.065116 0.019673 3.310 0.000957 ***
## A2Somewhat unlikely 0.009415 0.022141 0.425 0.670733
## IV1 0.112064 0.019857 5.644 2.01e-08 ***
## IV2 0.039052 0.024208 1.613 0.106927
## IV3 -1.023803 3.170951 -0.323 0.746842
## IV4 -1.046508 3.171289 -0.330 0.741453
## IV5 -1.017898 3.171196 -0.321 0.748271
## IV6 -1.034786 3.171212 -0.326 0.744241
## IV7 -1.027876 3.170995 -0.324 0.745873
## IV10 -0.014057 0.006625 -2.122 0.034014 *
## IV11 -0.012511 0.005058 -2.473 0.013505 *
## IV12 -0.009301 0.006769 -1.374 0.169617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.232 on 1410 degrees of freedom
## Multiple R-squared: 0.07635, Adjusted R-squared: 0.06652
## F-statistic: 7.77 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07635, Adjusted R-squared: 0.06652
summary(modelStep11ij)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.87824 -0.15761 0.00958 0.18069 0.42754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.373709 76.210431 0.372 0.709720
## A1No 0.019288 0.016402 1.176 0.239803
## A2Very likely 0.057766 0.016819 3.435 0.000611 ***
## A2Neither unlikely or likely -0.005681 0.020471 -0.277 0.781440
## A2Somewhat likely 0.068403 0.019774 3.459 0.000558 ***
## A2Somewhat unlikely 0.010133 0.022163 0.457 0.647598
## IV1 0.109886 0.020064 5.477 5.12e-08 ***
## IV2 0.039816 0.024241 1.643 0.100704
## IV3 -1.160248 3.175105 -0.365 0.714853
## IV4 -1.186701 3.175538 -0.374 0.708683
## IV5 -1.166323 3.175377 -0.367 0.713449
## IV6 -1.174216 3.175394 -0.370 0.711598
## IV7 -1.167624 3.175183 -0.368 0.713126
## IV8 -0.007357 0.006219 -1.183 0.237042
## IV11 -0.012101 0.005354 -2.260 0.023970 *
## IV12 -0.009342 0.007144 -1.308 0.191156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2323 on 1410 degrees of freedom
## Multiple R-squared: 0.07432, Adjusted R-squared: 0.06447
## F-statistic: 7.547 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07432, Adjusted R-squared: 0.06447
summary(modelStep11ik)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV10 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88757 -0.15571 0.00796 0.17971 0.44284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.229324 76.281401 0.370 0.711387
## A1No 0.018574 0.016416 1.131 0.258066
## A2Very likely 0.057580 0.016835 3.420 0.000644 ***
## A2Neither unlikely or likely -0.007231 0.020482 -0.353 0.724126
## A2Somewhat likely 0.066752 0.019780 3.375 0.000759 ***
## A2Somewhat unlikely 0.005757 0.022204 0.259 0.795466
## IV1 0.106378 0.020160 5.277 1.52e-07 ***
## IV2 0.040656 0.024264 1.676 0.094052 .
## IV3 -1.157239 3.178064 -0.364 0.715813
## IV4 -1.180538 3.178495 -0.371 0.710385
## IV5 -1.153063 3.178333 -0.363 0.716817
## IV6 -1.176743 3.178355 -0.370 0.711262
## IV7 -1.162919 3.178141 -0.366 0.714486
## IV8 -0.003580 0.005806 -0.617 0.537638
## IV10 -0.010296 0.006546 -1.573 0.115966
## IV12 -0.008612 0.007209 -1.194 0.232491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2325 on 1410 degrees of freedom
## Multiple R-squared: 0.07259, Adjusted R-squared: 0.06272
## F-statistic: 7.358 on 15 and 1410 DF, p-value: 6.782e-16# Multiple R-squared: 0.07259, Adjusted R-squared: 0.06272
summary(modelStep11il)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 +
## IV7 + IV8 + IV10 + IV11, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88173 -0.15359 0.00545 0.17498 0.42740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.426758 76.091872 0.308 0.758223
## A1No 0.019096 0.016384 1.166 0.244007
## A2Very likely 0.054476 0.016764 3.250 0.001183 **
## A2Neither unlikely or likely -0.006901 0.020438 -0.338 0.735686
## A2Somewhat likely 0.066360 0.019716 3.366 0.000784 ***
## A2Somewhat unlikely 0.004552 0.022134 0.206 0.837106
## IV1 0.105357 0.020124 5.235 1.9e-07 ***
## IV2 0.039685 0.024214 1.639 0.101451
## IV3 -0.952601 3.170252 -0.300 0.763854
## IV4 -0.980964 3.170623 -0.309 0.757069
## IV5 -0.949256 3.170520 -0.299 0.764678
## IV6 -0.965593 3.170528 -0.305 0.760752
## IV7 -0.968142 3.170430 -0.305 0.760132
## IV8 -0.008105 0.006012 -1.348 0.177817
## IV10 -0.014719 0.006758 -2.178 0.029572 *
## IV11 -0.014862 0.005579 -2.664 0.007809 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2321 on 1410 degrees of freedom
## Multiple R-squared: 0.0763, Adjusted R-squared: 0.06647
## F-statistic: 7.765 on 15 and 1410 DF, p-value: < 2.2e-16# Multiple R-squared: 0.0763, Adjusted R-squared: 0.06647modelStep11ie <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11iea <- lm(D1 ~ A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ieb <- lm(D1 ~ A1 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ieac <- lm(D1 ~ A1 + A2 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ied <- lm(D1 ~ A1 + A2 + IV1 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11iee <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV5 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ief <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ieg <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ieh <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV10 + IV11 + IV12, data = noout)
modelStep11iei <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV11 + IV12, data = noout)
modelStep11iej <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 + IV12, data = noout)
modelStep11iek <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 + IV11, data = noout)
summary(modelStep11ie)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 +
## IV10 + IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88149 -0.15667 0.00614 0.17812 0.46227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.649155 0.140491 4.621 4.18e-06 ***
## A1No 0.019414 0.016347 1.188 0.235187
## A2Very likely 0.057236 0.016770 3.413 0.000661 ***
## A2Neither unlikely or likely -0.004947 0.020408 -0.242 0.808516
## A2Somewhat likely 0.069227 0.019719 3.511 0.000461 ***
## A2Somewhat unlikely 0.008026 0.022100 0.363 0.716520
## IV1 0.106088 0.020035 5.295 1.38e-07 ***
## IV2 0.041218 0.024147 1.707 0.088039 .
## IV4 -0.030250 0.006784 -4.459 8.89e-06 ***
## IV5 0.006094 0.007127 0.855 0.392624
## IV6 -0.011820 0.005879 -2.011 0.044547 *
## IV8 -0.014081 0.006669 -2.111 0.034923 *
## IV10 -0.020827 0.007274 -2.863 0.004257 **
## IV11 -0.019222 0.005936 -3.238 0.001231 **
## IV12 -0.020833 0.006939 -3.002 0.002726 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1411 degrees of freedom
## Multiple R-squared: 0.07901, Adjusted R-squared: 0.06987
## F-statistic: 8.646 on 14 and 1411 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07901, Adjusted R-squared: 0.06987
summary(modelStep11iea)##
## Call:
## lm(formula = D1 ~ A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88505 -0.15898 0.00758 0.17847 0.46511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.661419 0.140132 4.720 2.59e-06 ***
## A2Very likely 0.057242 0.016772 3.413 0.000661 ***
## A2Neither unlikely or likely -0.004746 0.020410 -0.233 0.816155
## A2Somewhat likely 0.067910 0.019690 3.449 0.000579 ***
## A2Somewhat unlikely 0.005641 0.022012 0.256 0.797775
## IV1 0.102494 0.019808 5.174 2.62e-07 ***
## IV2 0.056881 0.020230 2.812 0.004995 **
## IV4 -0.030397 0.006784 -4.481 8.04e-06 ***
## IV5 0.006069 0.007128 0.852 0.394619
## IV6 -0.012175 0.005872 -2.073 0.038315 *
## IV8 -0.013682 0.006662 -2.054 0.040177 *
## IV10 -0.020844 0.007275 -2.865 0.004231 **
## IV11 -0.019140 0.005937 -3.224 0.001293 **
## IV12 -0.020812 0.006940 -2.999 0.002757 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2317 on 1412 degrees of freedom
## Multiple R-squared: 0.07809, Adjusted R-squared: 0.0696
## F-statistic: 9.2 on 13 and 1412 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07809, Adjusted R-squared: 0.0696
summary(modelStep11ieb)##
## Call:
## lm(formula = D1 ~ A1 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85771 -0.16266 0.00751 0.18064 0.44973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.644157 0.141127 4.564 5.44e-06 ***
## A1No 0.017848 0.016339 1.092 0.27485
## IV1 0.127936 0.019270 6.639 4.49e-11 ***
## IV2 0.033464 0.023507 1.424 0.15479
## IV4 -0.030311 0.006824 -4.442 9.62e-06 ***
## IV5 0.007452 0.007160 1.041 0.29817
## IV6 -0.011597 0.005909 -1.963 0.04988 *
## IV8 -0.010586 0.006657 -1.590 0.11202
## IV10 -0.020611 0.007309 -2.820 0.00487 **
## IV11 -0.019267 0.005965 -3.230 0.00127 **
## IV12 -0.018405 0.006959 -2.645 0.00826 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2332 on 1415 degrees of freedom
## Multiple R-squared: 0.06424, Adjusted R-squared: 0.05762
## F-statistic: 9.713 on 10 and 1415 DF, p-value: 7.809e-16# Multiple R-squared: 0.06424, Adjusted R-squared: 0.05762
summary(modelStep11ieac)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV2 + IV4 + IV5 + IV6 + IV8 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.81722 -0.15598 0.01046 0.17917 0.47319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.807316 0.138587 5.825 7.05e-09 ***
## A1No 0.006341 0.016314 0.389 0.697581
## A2Very likely 0.063642 0.016886 3.769 0.000171 ***
## A2Neither unlikely or likely -0.016123 0.020492 -0.787 0.431515
## A2Somewhat likely 0.083710 0.019714 4.246 2.32e-05 ***
## A2Somewhat unlikely -0.010682 0.022024 -0.485 0.627736
## IV2 0.027019 0.024226 1.115 0.264920
## IV4 -0.035287 0.006781 -5.204 2.24e-07 ***
## IV5 0.004026 0.007184 0.560 0.575246
## IV6 -0.015684 0.005889 -2.663 0.007826 **
## IV8 -0.019426 0.006655 -2.919 0.003567 **
## IV10 -0.024302 0.007314 -3.323 0.000914 ***
## IV11 -0.019879 0.005991 -3.318 0.000930 ***
## IV12 -0.022799 0.006995 -3.259 0.001143 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2338 on 1412 degrees of freedom
## Multiple R-squared: 0.06071, Adjusted R-squared: 0.05206
## F-statistic: 7.02 on 13 and 1412 DF, p-value: 2.401e-13# Multiple R-squared: 0.06071, Adjusted R-squared: 0.05206
summary(modelStep11ied)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV4 + IV5 + IV6 + IV8 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85618 -0.15596 0.00355 0.17914 0.46239
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.652168 0.140575 4.639 3.82e-06 ***
## A1No 0.034656 0.013703 2.529 0.011546 *
## A2Very likely 0.057234 0.016781 3.411 0.000666 ***
## A2Neither unlikely or likely -0.003029 0.020391 -0.149 0.881933
## A2Somewhat likely 0.069857 0.019728 3.541 0.000412 ***
## A2Somewhat unlikely 0.016800 0.021509 0.781 0.434883
## IV1 0.102289 0.019925 5.134 3.24e-07 ***
## IV4 -0.030394 0.006788 -4.478 8.16e-06 ***
## IV5 0.006108 0.007132 0.856 0.391886
## IV6 -0.011959 0.005882 -2.033 0.042232 *
## IV8 -0.013699 0.006670 -2.054 0.040175 *
## IV10 -0.020713 0.007279 -2.846 0.004496 **
## IV11 -0.019220 0.005940 -3.236 0.001242 **
## IV12 -0.020903 0.006943 -3.011 0.002654 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2318 on 1412 degrees of freedom
## Multiple R-squared: 0.0771, Adjusted R-squared: 0.06861
## F-statistic: 9.074 on 13 and 1412 DF, p-value: < 2.2e-16# Multiple R-squared: 0.0771, Adjusted R-squared: 0.06861
summary(modelStep11iee)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV5 + IV6 + IV8 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86011 -0.15957 0.00987 0.17902 0.45144
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0953570 0.0661079 1.442 0.149399
## A1No 0.0207411 0.0164537 1.261 0.207669
## A2Very likely 0.0582394 0.0168801 3.450 0.000577 ***
## A2Neither unlikely or likely -0.0050524 0.0205437 -0.246 0.805769
## A2Somewhat likely 0.0676255 0.0198467 3.407 0.000674 ***
## A2Somewhat unlikely 0.0064926 0.0222448 0.292 0.770427
## IV1 0.1186146 0.0199696 5.940 3.59e-09 ***
## IV2 0.0425597 0.0243055 1.751 0.080158 .
## IV5 0.0184090 0.0066136 2.783 0.005449 **
## IV6 0.0001469 0.0052652 0.028 0.977753
## IV8 0.0040330 0.0053244 0.757 0.448905
## IV10 -0.0096687 0.0068757 -1.406 0.159881
## IV11 -0.0082922 0.0054424 -1.524 0.127829
## IV12 -0.0035198 0.0057891 -0.608 0.543282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2332 on 1412 degrees of freedom
## Multiple R-squared: 0.06603, Adjusted R-squared: 0.05743
## F-statistic: 7.679 on 13 and 1412 DF, p-value: 6.821e-15# Multiple R-squared: 0.06603, Adjusted R-squared: 0.05743
summary(modelStep11ief)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.87760 -0.15578 0.00675 0.17776 0.46141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.692370 0.131075 5.282 1.48e-07 ***
## A1No 0.019373 0.016346 1.185 0.236129
## A2Very likely 0.057750 0.016758 3.446 0.000585 ***
## A2Neither unlikely or likely -0.004952 0.020406 -0.243 0.808304
## A2Somewhat likely 0.069865 0.019703 3.546 0.000404 ***
## A2Somewhat unlikely 0.008898 0.022075 0.403 0.686945
## IV1 0.105149 0.020003 5.257 1.69e-07 ***
## IV2 0.041242 0.024144 1.708 0.087831 .
## IV4 -0.032498 0.006253 -5.197 2.32e-07 ***
## IV6 -0.013864 0.005371 -2.581 0.009941 **
## IV8 -0.014566 0.006644 -2.192 0.028520 *
## IV10 -0.017973 0.006462 -2.781 0.005490 **
## IV11 -0.019268 0.005935 -3.246 0.001196 **
## IV12 -0.022004 0.006802 -3.235 0.001244 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1412 degrees of freedom
## Multiple R-squared: 0.07853, Adjusted R-squared: 0.07004
## F-statistic: 9.256 on 13 and 1412 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07853, Adjusted R-squared: 0.07004
summary(modelStep11ieg)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV8 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88486 -0.15921 0.00493 0.17613 0.45373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.523076 0.125857 4.156 3.43e-05 ***
## A1No 0.021084 0.016344 1.290 0.197248
## A2Very likely 0.057967 0.016784 3.454 0.000569 ***
## A2Neither unlikely or likely -0.002828 0.020402 -0.139 0.889788
## A2Somewhat likely 0.069832 0.019738 3.538 0.000416 ***
## A2Somewhat unlikely 0.009476 0.022112 0.429 0.668324
## IV1 0.111088 0.019902 5.582 2.85e-08 ***
## IV2 0.041888 0.024170 1.733 0.083304 .
## IV4 -0.024023 0.006042 -3.976 7.37e-05 ***
## IV5 0.011919 0.006518 1.828 0.067689 .
## IV8 -0.012036 0.006598 -1.824 0.068354 .
## IV10 -0.020916 0.007282 -2.872 0.004136 **
## IV11 -0.022969 0.005642 -4.071 4.94e-05 ***
## IV12 -0.017385 0.006731 -2.583 0.009897 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2319 on 1412 degrees of freedom
## Multiple R-squared: 0.07637, Adjusted R-squared: 0.06786
## F-statistic: 8.98 on 13 and 1412 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07637, Adjusted R-squared: 0.06786
summary(modelStep11ieh)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89452 -0.15509 0.00823 0.17804 0.43260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.436063 0.097849 4.457 8.99e-06 ***
## A1No 0.017677 0.016347 1.081 0.279707
## A2Very likely 0.055776 0.016776 3.325 0.000908 ***
## A2Neither unlikely or likely -0.003953 0.020427 -0.194 0.846580
## A2Somewhat likely 0.065356 0.019657 3.325 0.000908 ***
## A2Somewhat unlikely 0.009873 0.022110 0.447 0.655279
## IV1 0.112491 0.019829 5.673 1.70e-08 ***
## IV2 0.039510 0.024162 1.635 0.102236
## IV4 -0.021526 0.005387 -3.996 6.78e-05 ***
## IV5 0.007376 0.007110 1.037 0.299686
## IV6 -0.009928 0.005817 -1.707 0.088109 .
## IV10 -0.014340 0.006601 -2.172 0.030003 *
## IV11 -0.012621 0.005052 -2.498 0.012597 *
## IV12 -0.011450 0.005335 -2.146 0.032040 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2319 on 1412 degrees of freedom
## Multiple R-squared: 0.0761, Adjusted R-squared: 0.06759
## F-statistic: 8.946 on 13 and 1412 DF, p-value: < 2.2e-16# Multiple R-squared: 0.0761, Adjusted R-squared: 0.06759
summary(modelStep11iei)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8725 -0.1588 0.0094 0.1806 0.4310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.453478 0.123060 3.685 0.000237 ***
## A1No 0.019506 0.016389 1.190 0.234176
## A2Very likely 0.057907 0.016811 3.445 0.000589 ***
## A2Neither unlikely or likely -0.005296 0.020459 -0.259 0.795785
## A2Somewhat likely 0.068438 0.019767 3.462 0.000552 ***
## A2Somewhat unlikely 0.010946 0.022133 0.495 0.620993
## IV1 0.111262 0.020004 5.562 3.19e-08 ***
## IV2 0.040583 0.024207 1.676 0.093864 .
## IV4 -0.023568 0.006386 -3.690 0.000232 ***
## IV5 -0.003269 0.006348 -0.515 0.606661
## IV6 -0.011923 0.005894 -2.023 0.043264 *
## IV8 -0.006015 0.006060 -0.993 0.321113
## IV11 -0.011685 0.005334 -2.191 0.028638 *
## IV12 -0.012399 0.006299 -1.969 0.049193 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2322 on 1412 degrees of freedom
## Multiple R-squared: 0.07365, Adjusted R-squared: 0.06513
## F-statistic: 8.636 on 13 and 1412 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07365, Adjusted R-squared: 0.06513
summary(modelStep11iej)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 +
## IV10 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88302 -0.15650 0.00883 0.17886 0.44183
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.401402 0.118225 3.395 0.000705 ***
## A1No 0.018794 0.016401 1.146 0.252042
## A2Very likely 0.057703 0.016826 3.430 0.000622 ***
## A2Neither unlikely or likely -0.006901 0.020467 -0.337 0.736033
## A2Somewhat likely 0.066825 0.019771 3.380 0.000745 ***
## A2Somewhat unlikely 0.006496 0.022169 0.293 0.769536
## IV1 0.107444 0.020098 5.346 1.05e-07 ***
## IV2 0.041202 0.024227 1.701 0.089230 .
## IV4 -0.021179 0.006199 -3.416 0.000653 ***
## IV5 0.006304 0.007150 0.882 0.378140
## IV6 -0.017796 0.005600 -3.178 0.001517 **
## IV8 -0.002706 0.005688 -0.476 0.634310
## IV10 -0.010381 0.006542 -1.587 0.112743
## IV12 -0.011082 0.006272 -1.767 0.077471 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2324 on 1412 degrees of freedom
## Multiple R-squared: 0.07216, Adjusted R-squared: 0.06362
## F-statistic: 8.447 on 13 and 1412 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07216, Adjusted R-squared: 0.06362
summary(modelStep11iek)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV5 + IV6 + IV8 +
## IV10 + IV11, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8649 -0.1597 0.0095 0.1789 0.4317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.300605 0.079347 3.789 0.000158 ***
## A1No 0.019291 0.016394 1.177 0.239506
## A2Very likely 0.053531 0.016772 3.192 0.001445 **
## A2Neither unlikely or likely -0.006599 0.020458 -0.323 0.747086
## A2Somewhat likely 0.065130 0.019727 3.302 0.000985 ***
## A2Somewhat unlikely 0.004965 0.022139 0.224 0.822575
## IV1 0.109307 0.020063 5.448 6e-08 ***
## IV2 0.041651 0.024214 1.720 0.085633 .
## IV4 -0.018853 0.005638 -3.344 0.000849 ***
## IV5 0.010319 0.007006 1.473 0.141036
## IV6 -0.007459 0.005713 -1.306 0.191859
## IV8 -0.001256 0.005136 -0.245 0.806798
## IV10 -0.011556 0.006605 -1.750 0.080393 .
## IV11 -0.011488 0.005363 -2.142 0.032362 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2323 on 1412 degrees of freedom
## Multiple R-squared: 0.07312, Adjusted R-squared: 0.06459
## F-statistic: 8.569 on 13 and 1412 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07312, Adjusted R-squared: 0.06459 modelStep11iefa <- lm(D1 ~ A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11iefb <- lm(D1 ~ A1 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11iefc <- lm(D1 ~ A1 + A2 + IV2 + IV4 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11iefd <- lm(D1 ~ A1 + A2 + IV1 + IV4 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11iefe <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11ieff <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV8 + IV10 + IV11 + IV12, data = noout)
modelStep11iefg <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6+ IV10 + IV11 + IV12, data = noout)
modelStep11iefh <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6+ IV8 + IV11 + IV12, data = noout)
modelStep11iefi <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6+ IV8 + IV10 + IV12, data = noout)
modelStep11iefj <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6+ IV8 + IV10+ IV11, data = noout)
summary(modelStep11iefa)##
## Call:
## lm(formula = D1 ~ A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 + IV11 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88117 -0.15935 0.00693 0.17661 0.46425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.704432 0.130698 5.390 8.26e-08 ***
## A2Very likely 0.057754 0.016760 3.446 0.000586 ***
## A2Neither unlikely or likely -0.004752 0.020408 -0.233 0.815926
## A2Somewhat likely 0.068548 0.019674 3.484 0.000509 ***
## A2Somewhat unlikely 0.006514 0.021986 0.296 0.767056
## IV1 0.101566 0.019776 5.136 3.20e-07 ***
## IV2 0.056871 0.020228 2.812 0.004999 **
## IV4 -0.032635 0.006253 -5.219 2.07e-07 ***
## IV6 -0.014209 0.005364 -2.649 0.008157 **
## IV8 -0.014167 0.006637 -2.135 0.032966 *
## IV10 -0.018001 0.006463 -2.785 0.005423 **
## IV11 -0.019186 0.005936 -3.232 0.001257 **
## IV12 -0.021979 0.006802 -3.231 0.001262 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1413 degrees of freedom
## Multiple R-squared: 0.07761, Adjusted R-squared: 0.06978
## F-statistic: 9.908 on 12 and 1413 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07761, Adjusted R-squared: 0.06978
summary(modelStep11iefb)##
## Call:
## lm(formula = D1 ~ A1 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 + IV11 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85277 -0.16385 0.00839 0.18022 0.44924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.697297 0.131570 5.300 1.34e-07 ***
## A1No 0.017701 0.016338 1.083 0.27881
## IV1 0.126827 0.019242 6.591 6.13e-11 ***
## IV2 0.033706 0.023506 1.434 0.15182
## IV4 -0.033064 0.006291 -5.256 1.70e-07 ***
## IV6 -0.014107 0.005394 -2.615 0.00901 **
## IV8 -0.011165 0.006634 -1.683 0.09260 .
## IV10 -0.017124 0.006496 -2.636 0.00848 **
## IV11 -0.019319 0.005965 -3.239 0.00123 **
## IV12 -0.019810 0.006827 -2.902 0.00377 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2332 on 1416 degrees of freedom
## Multiple R-squared: 0.06352, Adjusted R-squared: 0.05757
## F-statistic: 10.67 on 9 and 1416 DF, p-value: 3.781e-16# Multiple R-squared: 0.06352, Adjusted R-squared: 0.05757
summary(modelStep11iefc)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV2 + IV4 + IV6 + IV8 + IV10 + IV11 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.81502 -0.15678 0.01106 0.17903 0.47255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.835025 0.129438 6.451 1.52e-10 ***
## A1No 0.006390 0.016310 0.392 0.695263
## A2Very likely 0.063945 0.016873 3.790 0.000157 ***
## A2Neither unlikely or likely -0.016061 0.020486 -0.784 0.433173
## A2Somewhat likely 0.084048 0.019700 4.266 2.12e-05 ***
## A2Somewhat unlikely -0.009995 0.021984 -0.455 0.649443
## IV2 0.027117 0.024219 1.120 0.263052
## IV4 -0.036747 0.006259 -5.871 5.39e-09 ***
## IV6 -0.017015 0.005387 -3.158 0.001620 **
## IV8 -0.019717 0.006633 -2.972 0.003004 **
## IV10 -0.022390 0.006468 -3.462 0.000553 ***
## IV11 -0.019906 0.005990 -3.323 0.000912 ***
## IV12 -0.023564 0.006859 -3.436 0.000608 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2338 on 1413 degrees of freedom
## Multiple R-squared: 0.0605, Adjusted R-squared: 0.05252
## F-statistic: 7.582 on 12 and 1413 DF, p-value: 9.689e-14# Multiple R-squared: 0.0605, Adjusted R-squared: 0.05252
summary(modelStep11iefd)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV4 + IV6 + IV8 + IV10 + IV11 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85227 -0.15558 0.00366 0.17884 0.46152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.695481 0.131152 5.303 1.32e-07 ***
## A1No 0.034623 0.013702 2.527 0.01162 *
## A2Very likely 0.057749 0.016769 3.444 0.00059 ***
## A2Neither unlikely or likely -0.003033 0.020389 -0.149 0.88176
## A2Somewhat likely 0.070497 0.019712 3.576 0.00036 ***
## A2Somewhat unlikely 0.017679 0.021482 0.823 0.41068
## IV1 0.101346 0.019893 5.095 3.97e-07 ***
## IV4 -0.032647 0.006257 -5.218 2.08e-07 ***
## IV6 -0.014007 0.005374 -2.607 0.00924 **
## IV8 -0.014186 0.006645 -2.135 0.03295 *
## IV10 -0.017852 0.006466 -2.761 0.00584 **
## IV11 -0.019266 0.005939 -3.244 0.00121 **
## IV12 -0.022077 0.006806 -3.244 0.00121 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2318 on 1413 degrees of freedom
## Multiple R-squared: 0.07662, Adjusted R-squared: 0.06878
## F-statistic: 9.771 on 12 and 1413 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07662, Adjusted R-squared: 0.06878
summary(modelStep11iefe)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV6 + IV8 + IV10 + IV11 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.84065 -0.15959 0.01216 0.17839 0.44567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.102668 0.066213 1.551 0.121228
## A1No 0.020946 0.016493 1.270 0.204289
## A2Very likely 0.060331 0.016904 3.569 0.000370 ***
## A2Neither unlikely or likely -0.005099 0.020593 -0.248 0.804485
## A2Somewhat likely 0.069471 0.019883 3.494 0.000491 ***
## A2Somewhat unlikely 0.009185 0.022277 0.412 0.680167
## IV1 0.118587 0.020017 5.924 3.94e-09 ***
## IV2 0.042996 0.024363 1.765 0.077810 .
## IV6 -0.003955 0.005067 -0.781 0.435201
## IV8 0.007091 0.005222 1.358 0.174724
## IV10 0.003425 0.005027 0.681 0.495769
## IV11 -0.005569 0.005367 -1.038 0.299607
## IV12 -0.003110 0.005801 -0.536 0.591935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2337 on 1413 degrees of freedom
## Multiple R-squared: 0.0609, Adjusted R-squared: 0.05293
## F-statistic: 7.636 on 12 and 1413 DF, p-value: 7.372e-14# Multiple R-squared: 0.0609, Adjusted R-squared: 0.05293
summary(modelStep11ieff)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV8 + IV10 + IV11 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.87713 -0.15869 0.00767 0.17656 0.45378
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.573257 0.122930 4.663 3.41e-06 ***
## A1No 0.021665 0.016354 1.325 0.185485
## A2Very likely 0.059467 0.016778 3.544 0.000406 ***
## A2Neither unlikely or likely -0.001982 0.020414 -0.097 0.922683
## A2Somewhat likely 0.071572 0.019731 3.627 0.000296 ***
## A2Somewhat unlikely 0.012105 0.022084 0.548 0.583691
## IV1 0.110913 0.019918 5.568 3.07e-08 ***
## IV2 0.042214 0.024190 1.745 0.081180 .
## IV4 -0.026767 0.005858 -4.570 5.31e-06 ***
## IV8 -0.012346 0.006602 -1.870 0.061675 .
## IV10 -0.014265 0.006313 -2.259 0.024011 *
## IV11 -0.024594 0.005576 -4.410 1.11e-05 ***
## IV12 -0.018733 0.006696 -2.798 0.005216 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2321 on 1413 degrees of freedom
## Multiple R-squared: 0.07418, Adjusted R-squared: 0.06632
## F-statistic: 9.435 on 12 and 1413 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07418, Adjusted R-squared: 0.06632
summary(modelStep11iefg)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6 + IV10 + IV11 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89033 -0.15590 0.00845 0.17675 0.43030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.479785 0.088311 5.433 6.52e-08 ***
## A1No 0.017554 0.016347 1.074 0.283066
## A2Very likely 0.056341 0.016768 3.360 0.000800 ***
## A2Neither unlikely or likely -0.003918 0.020428 -0.192 0.847946
## A2Somewhat likely 0.065971 0.019649 3.358 0.000807 ***
## A2Somewhat unlikely 0.011013 0.022083 0.499 0.618064
## IV1 0.111616 0.019811 5.634 2.12e-08 ***
## IV2 0.039466 0.024163 1.633 0.102624
## IV4 -0.023899 0.004877 -4.900 1.07e-06 ***
## IV6 -0.012339 0.005333 -2.314 0.020816 *
## IV10 -0.010587 0.005522 -1.917 0.055422 .
## IV11 -0.012400 0.005048 -2.456 0.014151 *
## IV12 -0.012483 0.005242 -2.382 0.017371 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2319 on 1413 degrees of freedom
## Multiple R-squared: 0.07539, Adjusted R-squared: 0.06754
## F-statistic: 9.601 on 12 and 1413 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07539, Adjusted R-squared: 0.06754
summary(modelStep11iefh)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV11 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.87434 -0.16021 0.00884 0.17897 0.43319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.405891 0.081246 4.996 6.59e-07 ***
## A1No 0.019542 0.016385 1.193 0.233179
## A2Very likely 0.057620 0.016797 3.430 0.000620 ***
## A2Neither unlikely or likely -0.005325 0.020454 -0.260 0.794637
## A2Somewhat likely 0.067931 0.019737 3.442 0.000595 ***
## A2Somewhat unlikely 0.010626 0.022118 0.480 0.631021
## IV1 0.112382 0.019881 5.653 1.91e-08 ***
## IV2 0.040508 0.024200 1.674 0.094380 .
## IV4 -0.021418 0.004831 -4.433 1.00e-05 ***
## IV6 -0.010544 0.005249 -2.009 0.044751 *
## IV8 -0.004934 0.005684 -0.868 0.385494
## IV11 -0.010952 0.005139 -2.131 0.033255 *
## IV12 -0.010818 0.005498 -1.968 0.049299 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2322 on 1413 degrees of freedom
## Multiple R-squared: 0.07348, Adjusted R-squared: 0.06561
## F-statistic: 9.339 on 12 and 1413 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07348, Adjusted R-squared: 0.06561
summary(modelStep11iefi)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 +
## IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.87901 -0.15549 0.00891 0.17896 0.43773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.445491 0.107119 4.159 3.39e-05 ***
## A1No 0.018750 0.016400 1.143 0.253111
## A2Very likely 0.058236 0.016813 3.464 0.000549 ***
## A2Neither unlikely or likely -0.006911 0.020466 -0.338 0.735639
## A2Somewhat likely 0.067479 0.019755 3.416 0.000654 ***
## A2Somewhat unlikely 0.007394 0.022144 0.334 0.738496
## IV1 0.106476 0.020067 5.306 1.30e-07 ***
## IV2 0.041226 0.024226 1.702 0.089019 .
## IV4 -0.023482 0.005622 -4.177 3.13e-05 ***
## IV6 -0.019924 0.005053 -3.943 8.43e-05 ***
## IV8 -0.003181 0.005662 -0.562 0.574392
## IV10 -0.007403 0.005601 -1.322 0.186497
## IV12 -0.012270 0.006125 -2.003 0.045360 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2324 on 1413 degrees of freedom
## Multiple R-squared: 0.07165, Adjusted R-squared: 0.06377
## F-statistic: 9.088 on 12 and 1413 DF, p-value: < 2.2e-16# Multiple R-squared: 0.07165, Adjusted R-squared: 0.06377
summary(modelStep11iefj)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 +
## IV11, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.85644 -0.15995 0.01215 0.17693 0.43119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3422107 0.0741781 4.613 4.32e-06 ***
## A1No 0.0192064 0.0164004 1.171 0.241760
## A2Very likely 0.0540692 0.0167748 3.223 0.001296 **
## A2Neither unlikely or likely -0.0067714 0.0204662 -0.331 0.740801
## A2Somewhat likely 0.0658488 0.0197292 3.338 0.000867 ***
## A2Somewhat unlikely 0.0061974 0.0221326 0.280 0.779507
## IV1 0.1079720 0.0200511 5.385 8.48e-08 ***
## IV2 0.0417352 0.0242244 1.723 0.085134 .
## IV4 -0.0216844 0.0053027 -4.089 4.57e-05 ***
## IV6 -0.0106270 0.0052943 -2.007 0.044914 *
## IV8 -0.0008418 0.0051307 -0.164 0.869699
## IV10 -0.0056091 0.0052288 -1.073 0.283580
## IV11 -0.0108032 0.0053452 -2.021 0.043459 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2324 on 1413 degrees of freedom
## Multiple R-squared: 0.0717, Adjusted R-squared: 0.06381
## F-statistic: 9.095 on 12 and 1413 DF, p-value: < 2.2e-16# Multiple R-squared: 0.0717, Adjusted R-squared: 0.06381
## No Adjusted R-squared value is greater than in this step thus we stop the regression.Prediction using Stepwise Regression
## Highest Adjusted R square model:
modelStep11ief <- lm(D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 + IV11 + IV12, data = noout)
summary(modelStep11ief)##
## Call:
## lm(formula = D1 ~ A1 + A2 + IV1 + IV2 + IV4 + IV6 + IV8 + IV10 +
## IV11 + IV12, data = noout)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.87760 -0.15578 0.00675 0.17776 0.46141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.692370 0.131075 5.282 1.48e-07 ***
## A1No 0.019373 0.016346 1.185 0.236129
## A2Very likely 0.057750 0.016758 3.446 0.000585 ***
## A2Neither unlikely or likely -0.004952 0.020406 -0.243 0.808304
## A2Somewhat likely 0.069865 0.019703 3.546 0.000404 ***
## A2Somewhat unlikely 0.008898 0.022075 0.403 0.686945
## IV1 0.105149 0.020003 5.257 1.69e-07 ***
## IV2 0.041242 0.024144 1.708 0.087831 .
## IV4 -0.032498 0.006253 -5.197 2.32e-07 ***
## IV6 -0.013864 0.005371 -2.581 0.009941 **
## IV8 -0.014566 0.006644 -2.192 0.028520 *
## IV10 -0.017973 0.006462 -2.781 0.005490 **
## IV11 -0.019268 0.005935 -3.246 0.001196 **
## IV12 -0.022004 0.006802 -3.235 0.001244 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2316 on 1412 degrees of freedom
## Multiple R-squared: 0.07853, Adjusted R-squared: 0.07004
## F-statistic: 9.256 on 13 and 1412 DF, p-value: < 2.2e-16### Our final model results show that A2 - Very likely, A2 - Somewhat likely, IV1, IV4, IV6, IV8, IV10, IV11 and IV12 are all significant as their p-values are all less than 0.05. We can conclude that these variables are good predictors of our dependent variable D1 which has to do with productivity. Our overall model also has a p-value less than 0.05 with an F-value of 9.306 - With these results, it is safe to say that productivity levels have been affected by the covid19 pandemic and therefore we reject our null hypothesis. This study provides some clarity that Remote work HAS affected the software industry during covid 19 pandemic. Prediction using Stepwise Regression - using the the estimate points above
Coefficients: Estimate
(Intercept) 0.69237
A1No 0.019373
A2Very likely 0.05775
A2Neither unlikely or likely 0.004952
A2Somewhat likely 0.069865
A2Somewhat unlikely 0.008898
IV1 0.105149 IV2 0.041242
IV4 -0.032498 IV6 -0.013864
IV8 -0.014566
IV10 -0.017973
IV11 -0.019268
IV12 -0.022004
Random column from dataset
IV1 A1 IV2 A2 IV3 IV4 IV5 IV6 IV7 IV8 IV9 IV10 IV11 IV12 D1
1 Yes 0 Somewhat unlikely 2 6 4 5 7 7 5 5 7 0.5
Calculation results:
0.105149 0.019373 0 0.3465 -0.194988 -0.06932 0 -0.089865 -0.09634 -0.154028
-0.133519
0.558851