Giovanni Minchio giovanni.minchio@unitn.it
Yuxin Zhang yuxin.zhang@unitn.it
Quantitative Methods Lab, Lesson 2.1
08
Oct. 2024
In scientific research, a variable refers to anything that can take on different values across a data set.
Measurement scales, also called levels of measurement, indicate how variables are recorded.
Nominal: categorical, no order
Ordinal: categorical, ordered
Interval: numerical, equal distances between adjacent values, no true zero
Ratio: numerical, true zero and ratios
Choose appropriate measurement scales to record your data:
- Competition ranking (first/second/third...)
- Temperature (in Fahrenheit/Celsius)
- Age
- Human height (in centimeters)
- Human weight (in kilograms)
- Preferred political party
- Sex (male/female)
- Number of siblings
- Occupations
- Year of birth
- Number of pages in the last book you read
- Household income
- Place of residence (countryside/town/city/metropolis)
- Mortality rate (from 0% to 100%)
- Favorite ice cream flavor
- Number of cigarettes smoked
Now let’s explore how to present different types of variables in Stata.
Create a new do file and code there.
Set the working directory.
P.s., cd stands for “change directory”
You can also open a log file, which saves your commands and outputs all together.
You can click on the Stata interface:
Or:
“output” is one of my subfolder in the folder “STATALAB2024-25”,
where we have set our working directory. We do not need to repeat that
path again once we set up directory using cd "".
“lesson2” is the name I assigned to this log file, but you can give any name you prefer for your own log file.
“datafile” is one of my subfolder in the folder “STATALAB2024-25”, and “ESS10.dta” is the name of the data set in this subfolder.
When you log off, no input or output is recorded in your log text file. Try it, and you will not see anything saved in the log file.
https://ess.sikt.no/en/?tab=overview
↓
We can present variables by their distributions to illustrate how the values of the variables are spread across different categories or ranges.
Let’s try to present some variables in the ESS 10 data set we downloaded. The variables we will use in this section are:
cntry: country
yrbrn: year of birth
gndr: gender
edulvlb: highest level of education
completed
hhmmb: number of people living in household
happy: how happy are you
netustm: internet use on typical day, in
minutes
netusoft: internet use, how often
polintr: how interested in politics
prtcleit: which party feel closer to, Italy
vote: voted last national election
When we explore a variable:
What is its scale?
Are there strange values?
Do I need to recode/modify it?
summarizehhmmb Number of people living regularly as member of household
----------------------------------------------------------------------------------
Type: Numeric (byte)
Label: hhmmb, but 13 nonmissing values are not labeled
Range: [1,13] Units: 1
Unique values: 13 Missing .: 0/37,611
Unique mv codes: 2 Missing .*: 144/37,611
Examples: 1
2
3
4 Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
hhmmb | 37,467 2.546534 1.341311 1 13happy How happy are you
----------------------------------------------------------------------------------
Type: Numeric (byte)
Label: happy, but 9 nonmissing values are not labeled
Range: [0,10] Units: 1
Unique values: 11 Missing .: 0/37,611
Unique mv codes: 3 Missing .*: 90/37,611
Examples: 6
7
8
9 Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
happy | 37,521 7.241918 1.933552 0 10 How happy are you
-------------------------------------------------------------
Percentiles Smallest
1% 1 0
5% 4 0
10% 5 0 Obs 37,521
25% 6 0 Sum of wgt. 37,521
50% 8 Mean 7.241918
Largest Std. dev. 1.933552
75% 8 10
90% 10 10 Variance 3.738622
95% 10 10 Skewness -.932757
99% 10 10 Kurtosis 4.086627(p.s., sum is an abbreviation for
summarize, it can also be su
summ, summari, etc. Try them out by yourself
and see when and what does not work.)
yrbrn Year of birth
----------------------------------------------------------------------------------
Type: Numeric (int)
Label: yrbrn, but 77 nonmissing values are not labeled
Range: [1931,2007] Units: 1
Unique values: 77 Missing .: 0/37,611
Unique mv codes: 3 Missing .*: 292/37,611
Examples: 1953
1964
1976
1989 Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
yrbrn | 37,319 1970.412 18.41365 1931 2007 Year of birth
-------------------------------------------------------------
Percentiles Smallest
1% 1934 1931
5% 1941 1931
10% 1946 1931 Obs 37,319
25% 1956 1931 Sum of wgt. 37,319
50% 1970 Mean 1970.412
Largest Std. dev. 18.41365
75% 1985 2006
90% 1997 2007 Variance 339.0624
95% 2001 2007 Skewness .0467735
99% 2005 2007 Kurtosis 2.066355polintr How interested in politics
----------------------------------------------------------------------------------
Type: Numeric (byte)
Label: polintr
Range: [1,4] Units: 1
Unique values: 4 Missing .: 0/37,611
Unique mv codes: 3 Missing .*: 88/37,611
Tabulation: Freq. Numeric Label
3,430 1 Very interested
11,836 2 Quite interested
13,412 3 Hardly interested
8,845 4 Not at all interested
30 .a Refusal
45 .b Don't know
13 .c No answer Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
polintr | 37,523 2.737468 .9208133 1 4tabulatecntry Country
----------------------------------------------------------------------------------
Type: String (str2)
Unique values: 22 Missing "": 0/37,611
Examples: "CZ"
"GR"
"IS"
"MK" Country | Freq. Percent Cum.
------------+-----------------------------------
BE | 1,341 3.57 3.57
BG | 2,718 7.23 10.79
CH | 1,523 4.05 14.84
CZ | 2,476 6.58 21.42
EE | 1,542 4.10 25.52
FI | 1,577 4.19 29.72
FR | 1,977 5.26 34.97
GB | 1,149 3.05 38.03
GR | 2,799 7.44 45.47
HR | 1,592 4.23 49.70
HU | 1,849 4.92 54.62
IE | 1,770 4.71 59.33
IS | 903 2.40 61.73
IT | 2,640 7.02 68.75
LT | 1,659 4.41 73.16
ME | 1,278 3.40 76.55
MK | 1,429 3.80 80.35
NL | 1,470 3.91 84.26
NO | 1,411 3.75 88.01
PT | 1,838 4.89 92.90
SI | 1,252 3.33 96.23
SK | 1,418 3.77 100.00
------------+-----------------------------------
Total | 37,611 100.00if Country | Freq. Percent Cum.
------------+-----------------------------------
IT | 2,640 100.00 100.00
------------+-----------------------------------
Total | 2,640 100.00 Country | Freq. Percent Cum.
------------+-----------------------------------
FR | 1,977 32.48 32.48
IT | 2,640 43.37 75.85
NL | 1,470 24.15 100.00
------------+-----------------------------------
Total | 6,087 100.00Variable Storage Display Value
name type format label Variable label
----------------------------------------------------------------------------------
prtcleit byte %4.0g prtcleit Which party feel closer to, Italyprtcleit Which party feel closer to, Italy
----------------------------------------------------------------------------------
Type: Numeric (byte)
Label: prtcleit
Range: [1,40] Units: 1
Unique values: 19 Missing .: 34,971/37,611
Unique mv codes: 5 Missing .*: 2,054/37,611
Examples: .
.
.
. Value label prtcleit
----------------------------------------------------------------------------------
Values Labels
Range: [1,40] String length: [4,36]
N: 27 Unique at full length: yes
Gaps: yes Unique at length 12: yes
Missing .*: 4 Null string: no
Leading/trailing blanks: no
Numeric -> numeric: no
Definition
1 Movimento 5 Stelle
2 Partido Democratico
3 Lega
4 Forza Italia
5 Fratelli d'Italia con Giorgia Meloni
6 Liberi e Uguali (LEU)
7 + Europa
8 Noi con l'Italia - UDC
9 Potere al popolo
10 Casapound Italia
11 Italia Europa Insieme
12 Il popolo della famiglia
13 Civica Popolare Lorenzin
14 SVP-PATT
31 Altro
33 Italia Viva
34 Unione Valdotaine
35 Partito Comunista
36 Vox Italia
37 Partito Socialista
38 Verdi/ Europa Verde
39 Italexit
40 Azione di Calenda
.a Not applicable
.b Refusal
.c Don't know
.d No answer
Variables: prtcleitprtcleit:
1 Movimento 5 Stelle
2 Partido Democratico
3 Lega
4 Forza Italia
5 Fratelli d'Italia con Giorgia Meloni
6 Liberi e Uguali (LEU)
7 + Europa
8 Noi con l'Italia - UDC
9 Potere al popolo
10 Casapound Italia
11 Italia Europa Insieme
12 Il popolo della famiglia
13 Civica Popolare Lorenzin
14 SVP-PATT
31 Altro
33 Italia Viva
34 Unione Valdotaine
35 Partito Comunista
36 Vox Italia
37 Partito Socialista
38 Verdi/ Europa Verde
39 Italexit
40 Azione di Calenda
.a Not applicable
.b Refusal
.c Don't know
.d No answer Which party feel closer to, Italy | Freq. Percent Cum.
-------------------------------------+-----------------------------------
Movimento 5 Stelle | 101 17.24 17.24
Partido Democratico | 188 32.08 49.32
Lega | 78 13.31 62.63
Forza Italia | 57 9.73 72.35
Fratelli d'Italia con Giorgia Meloni | 108 18.43 90.78
Liberi e Uguali (LEU) | 9 1.54 92.32
+ Europa | 2 0.34 92.66
Noi con l'Italia - UDC | 4 0.68 93.34
Potere al popolo | 6 1.02 94.37
SVP-PATT | 8 1.37 95.73
Altro | 3 0.51 96.25
Italia Viva | 5 0.85 97.10
Unione Valdotaine | 1 0.17 97.27
Partito Comunista | 5 0.85 98.12
Vox Italia | 1 0.17 98.29
Partito Socialista | 2 0.34 98.63
Verdi/ Europa Verde | 2 0.34 98.98
Italexit | 3 0.51 99.49
Azione di Calenda | 3 0.51 100.00
-------------------------------------+-----------------------------------
Total | 586 100.00vote Voted last national election
----------------------------------------------------------------------------------
Type: Numeric (byte)
Label: vote
Range: [1,3] Units: 1
Unique values: 3 Missing .: 0/37,611
Unique mv codes: 3 Missing .*: 459/37,611
Tabulation: Freq. Numeric Label
26,794 1 Yes
7,764 2 No
2,594 3 Not eligible to vote
193 .a Refusal
221 .b Don't know
45 .c No answer Voted last national |
election | Freq. Percent Cum.
---------------------+-----------------------------------
Yes | 26,794 72.12 72.12
No | 7,764 20.90 93.02
Not eligible to vote | 2,594 6.98 100.00
---------------------+-----------------------------------
Total | 37,152 100.00 Voted last national |
election | Freq. Percent Cum.
---------------------+-----------------------------------
Yes | 26,794 71.24 71.24
No | 7,764 20.64 91.88
Not eligible to vote | 2,594 6.90 98.78
Refusal | 193 0.51 99.29
Don't know | 221 0.59 99.88
No answer | 45 0.12 100.00
---------------------+-----------------------------------
Total | 37,611 100.00tab1-> tabulation of cntry
Country | Freq. Percent Cum.
------------+-----------------------------------
BE | 1,341 3.57 3.57
BG | 2,718 7.23 10.79
CH | 1,523 4.05 14.84
CZ | 2,476 6.58 21.42
EE | 1,542 4.10 25.52
FI | 1,577 4.19 29.72
FR | 1,977 5.26 34.97
GB | 1,149 3.05 38.03
GR | 2,799 7.44 45.47
HR | 1,592 4.23 49.70
HU | 1,849 4.92 54.62
IE | 1,770 4.71 59.33
IS | 903 2.40 61.73
IT | 2,640 7.02 68.75
LT | 1,659 4.41 73.16
ME | 1,278 3.40 76.55
MK | 1,429 3.80 80.35
NL | 1,470 3.91 84.26
NO | 1,411 3.75 88.01
PT | 1,838 4.89 92.90
SI | 1,252 3.33 96.23
SK | 1,418 3.77 100.00
------------+-----------------------------------
Total | 37,611 100.00
-> tabulation of vote
Voted last national |
election | Freq. Percent Cum.
---------------------+-----------------------------------
Yes | 26,794 71.24 71.24
No | 7,764 20.64 91.88
Not eligible to vote | 2,594 6.90 98.78
Refusal | 193 0.51 99.29
Don't know | 221 0.59 99.88
No answer | 45 0.12 100.00
---------------------+-----------------------------------
Total | 37,611 100.00Variable Storage Display Value
name type format label Variable label
----------------------------------------------------------------------------------
edulvlb int %6.0g edulvlb Highest level of educationValue label edulvlb
----------------------------------------------------------------------------------
Values Labels
Range: [0,5555] String length: [5,69]
N: 31 Unique at full length: yes
Gaps: yes Unique at length 12: no
Missing .*: 3 Null string: no
Leading/trailing blanks: no
Numeric -> numeric: no
Definition
0 Not completed ISCED level 1
113 ISCED 1, completed primary education
129 Vocational ISCED 2C < 2 years, no access ISCED 3
212 General/pre-vocational ISCED 2A/2B, access ISCED 3 vocational
213 General ISCED 2A, access ISCED 3A general/all 3
221 Vocational ISCED 2C >= 2 years, no access ISCED 3
222 Vocational ISCED 2A/2B, access ISCED 3 vocational
223 Vocational ISCED 2, access ISCED 3 general/all
229 Vocational ISCED 3C < 2 years, no access ISCED 5
311 General ISCED 3 >=2 years, no access ISCED 5
312 General ISCED 3A/3B, access ISCED 5B/lower tier 5A
313 General ISCED 3A, access upper tier ISCED 5A/all 5
321 Vocational ISCED 3C >= 2 years, no access ISCED 5
322 Vocational ISCED 3A, access ISCED 5B/ lower tier 5A
323 Vocational ISCED 3A, access upper tier ISCED 5A/all 5
412 General ISCED 4A/4B, access ISCED 5B/lower tier 5A
413 General ISCED 4A, access upper tier ISCED 5A/all 5
421 ISCED 4 programmes without access ISCED 5
422 Vocational ISCED 4A/4B, access ISCED 5B/lower tier 5A
423 Vocational ISCED 4A, access upper tier ISCED 5A/all 5
510 ISCED 5A short, intermediate/academic/general tertiary below
bachelor
520 ISCED 5B short, advanced vocational qualifications
610 ISCED 5A medium, bachelor/equivalent from lower tier tertiary
620 ISCED 5A medium, bachelor/equivalent from upper/single tier
tertiary
710 ISCED 5A long, master/equivalent from lower tier tertiary
720 ISCED 5A long, master/equivalent from upper/single tier tertiary
800 ISCED 6, doctoral degree
5555 Other
.a Refusal
.b Don't know
.c No answer
Variables: edulvlb Highest level of education | Freq. Percent Cum.
----------------------------------------+-----------------------------------
Not completed ISCED level 1 | 317 0.85 0.85
ISCED 1, completed primary education | 2,252 6.01 6.86
Vocational ISCED 2C < 2 years, no acces | 8 0.02 6.88
General/pre-vocational ISCED 2A/2B, acc | 223 0.60 7.48
General ISCED 2A, access ISCED 3A gener | 4,358 11.64 19.11
Vocational ISCED 2C >= 2 years, no acce | 44 0.12 19.23
Vocational ISCED 2A/2B, access ISCED 3 | 341 0.91 20.14
Vocational ISCED 2, access ISCED 3 gene | 44 0.12 20.26
Vocational ISCED 3C < 2 years, no acces | 508 1.36 21.62
General ISCED 3A/3B, access ISCED 5B/lo | 93 0.25 21.86
General ISCED 3A, access upper tier ISC | 5,304 14.16 36.03
Vocational ISCED 3C >= 2 years, no acce | 4,078 10.89 46.92
Vocational ISCED 3A, access ISCED 5B/ l | 666 1.78 48.69
Vocational ISCED 3A, access upper tier | 5,386 14.38 63.08
General ISCED 4A/4B, access ISCED 5B/lo | 17 0.05 63.12
General ISCED 4A, access upper tier ISC | 19 0.05 63.17
ISCED 4 programmes without access ISCED | 836 2.23 65.40
Vocational ISCED 4A/4B, access ISCED 5B | 91 0.24 65.65
Vocational ISCED 4A, access upper tier | 996 2.66 68.31
ISCED 5A short, intermediate/academic/g | 203 0.54 68.85
ISCED 5B short, advanced vocational qua | 1,626 4.34 73.19
ISCED 5A medium, bachelor/equivalent fr | 1,665 4.45 77.64
ISCED 5A medium, bachelor/equivalent fr | 3,133 8.37 86.00
ISCED 5A long, master/equivalent from l | 792 2.11 88.12
ISCED 5A long, master/equivalent from u | 3,961 10.58 98.69
ISCED 6, doctoral degree | 408 1.09 99.78
Other | 81 0.22 100.00
----------------------------------------+-----------------------------------
Total | 37,450 100.00 Highest |
level of |
education | Freq. Percent Cum.
------------+-----------------------------------
0 | 317 0.85 0.85
113 | 2,252 6.01 6.86
129 | 8 0.02 6.88
212 | 223 0.60 7.48
213 | 4,358 11.64 19.11
221 | 44 0.12 19.23
222 | 341 0.91 20.14
223 | 44 0.12 20.26
229 | 508 1.36 21.62
312 | 93 0.25 21.86
313 | 5,304 14.16 36.03
321 | 4,078 10.89 46.92
322 | 666 1.78 48.69
323 | 5,386 14.38 63.08
412 | 17 0.05 63.12
413 | 19 0.05 63.17
421 | 836 2.23 65.40
422 | 91 0.24 65.65
423 | 996 2.66 68.31
510 | 203 0.54 68.85
520 | 1,626 4.34 73.19
610 | 1,665 4.45 77.64
620 | 3,133 8.37 86.00
710 | 792 2.11 88.12
720 | 3,961 10.58 98.69
800 | 408 1.09 99.78
5555 | 81 0.22 100.00
------------+-----------------------------------
Total | 37,450 100.00How can we recode this variable to simplify it without losing too much information? Note it is a measure of education across countries.
Try them out by yourselves using the commands we have learned so far;
e.g., recode, drop, gen,
egen, rename, etc.
There is no single correct solution, and it depends on how you justify your choice. You may also discuss it with your peers.
It’s NOT a mandatory task, but if you’d like to see how your peers think about it and want some feedback, you can upload your idea or solution by the end of this week, to our Moodle section “Lab exercises”, here:
tabulate.The first variable is in rows, and the second variable is in columns.
Voted last national | Gender
election | Male Female | Total
---------------------+----------------------+----------
Yes | 12,503 14,291 | 26,794
No | 3,465 4,299 | 7,764
Not eligible to vote | 1,298 1,296 | 2,594
---------------------+----------------------+----------
Total | 17,266 19,886 | 37,152 table
(table command cannot be shortened!) | Gender
| Male Female Total
---------------------------------+--------------------------
Country |
BE |
Voted last national election |
Yes | 517 526 1,043
No | 75 73 148
Not eligible to vote | 76 68 144
Total | 668 667 1,335
BG |
Voted last national election |
Yes | 900 988 1,888
No | 341 415 756
Not eligible to vote | 39 24 63
Total | 1,280 1,427 2,707
CH |
Voted last national election |
Yes | 440 404 844
No | 157 162 319
Not eligible to vote | 174 150 324
Total | 771 716 1,487
CZ |
Voted last national election |
Yes | 641 846 1,487
No | 345 445 790
Not eligible to vote | 87 95 182
Total | 1,073 1,386 2,459
EE |
Voted last national election |
Yes | 437 596 1,033
No | 158 153 311
Not eligible to vote | 92 97 189
Total | 687 846 1,533
FI |
Voted last national election |
Yes | 609 656 1,265
No | 128 90 218
Not eligible to vote | 41 48 89
Total | 778 794 1,572
FR |
Voted last national election |
Yes | 508 517 1,025
No | 284 306 590
Not eligible to vote | 157 150 307
Total | 949 973 1,922
GB |
Voted last national election |
Yes | 393 489 882
No | 89 123 212
Not eligible to vote | 23 30 53
Total | 505 642 1,147
GR |
Voted last national election |
Yes | 1,124 1,210 2,334
No | 157 200 357
Not eligible to vote | 33 32 65
Total | 1,314 1,442 2,756
HR |
Voted last national election |
Yes | 509 556 1,065
No | 180 281 461
Not eligible to vote | 15 18 33
Total | 704 855 1,559
HU |
Voted last national election |
Yes | 484 811 1,295
No | 157 265 422
Not eligible to vote | 55 71 126
Total | 696 1,147 1,843
IE |
Voted last national election |
Yes | 624 716 1,340
No | 128 144 272
Not eligible to vote | 85 66 151
Total | 837 926 1,763
IS |
Voted last national election |
Yes | 363 380 743
No | 28 34 62
Not eligible to vote | 40 51 91
Total | 431 465 896
IT |
Voted last national election |
Yes | 867 932 1,799
No | 244 323 567
Not eligible to vote | 113 96 209
Total | 1,224 1,351 2,575
LT |
Voted last national election |
Yes | 398 709 1,107
No | 215 287 502
Not eligible to vote | 23 16 39
Total | 636 1,012 1,648
ME |
Voted last national election |
Yes | 564 513 1,077
No | 55 73 128
Not eligible to vote | 10 20 30
Total | 629 606 1,235
MK |
Voted last national election |
Yes | 502 582 1,084
No | 112 167 279
Not eligible to vote | 16 15 31
Total | 630 764 1,394
NL |
Voted last national election |
Yes | 641 614 1,255
No | 73 67 140
Not eligible to vote | 36 37 73
Total | 750 718 1,468
NO |
Voted last national election |
Yes | 576 543 1,119
No | 64 47 111
Not eligible to vote | 78 95 173
Total | 718 685 1,403
PT |
Voted last national election |
Yes | 540 697 1,237
No | 190 295 485
Not eligible to vote | 37 58 95
Total | 767 1,050 1,817
SI |
Voted last national election |
Yes | 384 441 825
No | 149 160 309
Not eligible to vote | 49 49 98
Total | 582 650 1,232
SK |
Voted last national election |
Yes | 482 565 1,047
No | 136 189 325
Not eligible to vote | 19 10 29
Total | 637 764 1,401
Total |
Voted last national election |
Yes | 12,503 14,291 26,794
No | 3,465 4,299 7,764
Not eligible to vote | 1,298 1,296 2,594
Total | 17,266 19,886 37,152
------------------------------------------------------------table | Mean
---------+----------
Gender |
Male | 7.245336
Female | 7.238955
Total | 7.241918
--------------------table | Mean Standard deviation Number of nonmissing values
---------+-------------------------------------------------------------
Gender |
Male | 7.245336 1.89884 17,421
Female | 7.238955 1.963183 20,100
Total | 7.241918 1.933552 37,521
-----------------------------------------------------------------------tabstatSummary for variables: happy
Group variable: gndr (Gender)
gndr | Mean SD N
----------+------------------------------
Male | 7.245336 1.89884 17421
Female | 7.238955 1.963183 20100
----------+------------------------------
Total | 7.241918 1.933552 37521
-----------------------------------------P.s., what about median and mode?
Summary for variables: happy
Group variable: gndr (Gender)
gndr | p50
----------+----------
Male | 8
Female | 8
----------+----------
Total | 8
---------------------Summary for variables: happy
Group variable: gndr (Gender)
gndr | p50
----------+----------
Male | 8
Female | 8
----------+----------
Total | 8
--------------------- | Gender
| Male Female Total
--------------------+--------------------------
How happy are you |
Extremely unhappy | 106 139 245
1 | 84 109 193
2 | 201 261 462
3 | 447 504 951
4 | 541 686 1,227
5 | 1,658 1,989 3,647
6 | 1,725 2,086 3,811
7 | 3,610 3,824 7,434
8 | 4,823 5,359 10,182
9 | 2,524 2,915 5,439
Extremely happy | 1,702 2,228 3,930
Total | 17,421 20,100 37,521
-----------------------------------------------
Internet use in mins by gender by country
| Variable | Central tendency measure | Plot type |
|---|---|---|
| Nominal | mode | barplot |
| Ordinal | mode, median | barplot (correct order) |
| Interval | mode, median, mean | barplot/histogram |
| Ratio | mode, median, mean | histogram |
There are various methods and commands in Stata for achieving similar outputs, and if you’ want to’d like to explore further, Google is always your best friend! Some useful resources:
Image source: https://ru.pinterest.com/pin/680958406132830397/
Image source: https://devcamp.com/site_blogs/top-5-programming-memes
Due date: by 9.Oct.2024 23:59
Work individually (though you can discuss with your peers)
Select 3 variables from today’s data set based on your own interests
Explore these variables using what we’ve learned
Describe each variable and organize the outputs neatly
Convert it to a PDF file and name it as: surname_quanlab_1
Upload to Moodle under “Lab Materials” assignment section:
