1) The document discusses exploring cross-sectional data using various SAS procedures like MEANS, SGPLOT, FREQ, CORR, REG, and TABULATE to analyze rental data involving gender, rooms, number of persons, and distance from campus. 2) The analysis finds that female students pay higher rents than male students, and this effect is primarily due to females living further away from campus, perhaps in safer neighborhoods, while males prefer higher rents to live on campus. 3) Various regression models show that females pay rent premiums compared to males both on campus and farther away, indicating they pay more to live in preferred locations.

1. Basic Cross-Section
Exploratory Data Analysis
Procs MEANS, SGPLOT, FREQ, CORR, REG, and TABULATE
Dr. Steven C. Myers
Department of Economics
College of Business Administration
The University of Akron
econdatascience.com
May 2, 2019

2. Cross sectional (and panel and longitudinal) data
Previously we focused on exploration of Time Series Data and on
reporting our results.
Now we will focus on
Cross Sectional data
Many observations interviewed during one time period
Panel data
Different observations interviewed each year
&
Longitudinal Data
Same observations re-interviewed each year

3. Why are female students paying higher rents?
Rental data on the campus of Ann Arbor involving gender, rooms,
number of persons and distance from campus. N=32.
https://amzn.to/2q49OwA
Variable Explanation
RENT Monthly apartment rent (dollars)
NO Number of persons living in the
apartment
RM Number of rooms in the apartment
SEX Sex
DIST Distance in blocks from campus
RPP Rent per person (RENT/NO)
Source: Pindyck and Rubinfeld, Econometric Models and Economic Forecasts, 4th edition, McGraw-Hill.

4. Why are female students paying higher rents?
proc means data=rentdata.rent2 maxdec=2;
class sex;
var rent rpp no rm dist;
run;
Learn how to add labels to your dataset
Page 101, LSB (Little SAS Book)
PROC MEANS, pp. 118-119 LSB

5. SGPLOT is still helpful
Male Female Male Female
MonthlyRent
RentperPerson,RPP
Title2 'RENT distribution box plots';
proc sgplot data=rentdata.rent2;
vbox RENT / category = sex;
run;
Title2 'RENT distribution box plots';
proc sgplot data=rentdata.rent2;
vbox RPP / category = sex;
run;
Creating Box Plots, pp. 234-235, LSB

6. Does distance matter?
proc freq data=rentdata.rent2;
tables dist*sex /norow nocol nocum noprecent;
run;
proc means data=rentdata.rent2 maxdec=2;
class sex dist;
var rent rpp no rm ;
run;
NO!
Too much output.
Not easy to read or use.
Hard to read and doesn’t deal with RENT
PROC FREQ,
pp.122-124
LSB

7. Creating your own formats, PROC FORMAT, pp. 114-115 LSB
proc format ;
value distance 0-6 ='close 0-6 blocks'
7-12 ='further 7-12 blocks'
13-60 ='far out 16-60 blocks' ;
value gender 1 ='Female'
0 ='Male';
run;
proc freq data=rentdata.rent2;
tables dist*sex /norow nocol nocum noprecent;
format dist distance. sex gender.;
run;
Easier to read.
Still doesn’t deal with RENT

8. PROC MEANS using your defined FORMAT statements
proc means data=rentdata.rent2 maxdec=2;
class dist sex;
var rent rpp;
format dist distance. sex gender.;
run;
Now it is easier to understand.

9. Using scatter plot to look at relationship between RENT and DIST
title2 'Using Scatter plot option
of SGPLOT.';
title3 'Markers are colored by
group, red=female,
blue=male.';
proc sgplot;
data=rentdata.rent2;
scatter y=RENT x=dist /
group=sex;
run;
PROC SGPLOT, SCATTER
plots, pp. 236-237, LSB

10. Using scatter plot to look at relationship between RENT and DIST
title2 'Using Scatter plot option
of SGPLOT.';
title3 'Markers are colored by
group, red=female,
blue=male.';
proc sgplot;
data=rentdata.rent2;
scatter y=RPP x=dist /
group=sex;
run;

11. PROC CORR gives means output and pairwise relationships
Title2 'PROC CORRELATION';
proc corr data=rentdata.rent2;
var rent rpp no rm dist sex;
run;
PROC CORR,
pp. 268-271, LSB

12. You can use the WHERE command with every PROC
Title2 'PROC CORRELATION with WHERE restrictions';
proc corr data=rentdata.rent2;
var rent rpp no rm dist sex;
where rent<600 and dist <42;
run;
No restrictions
N=32
Where RENT<600
And DIST<42
N=27
Where statement
102-103, 328-330,
LSB

13. PROC TABULATE, pp. 124-133, LSB (Little SAS Book)
Combines results of procedures like FREQ and MEANS and produces
highly customized tables.
PROC TABULATE;
CLASS classification-variable-list;
VAR analysis-variable-list;
TABLE page dimension,row-dimension, column-dimension;
1. You must have a CLASS
and/or a VAR statement,
2. All variables must appear in
CLASS or VAR statement.
Commas are critically important.
One dimensional table (no comma)
Two dimensional table (one commas)
Three dimensional table (two commas)
See how to articles in Brightspace

14. Pretty basic PROC TABULATE
Proc tabulate data=rentdata.rent2;
class dist no rm sex ;
var rent rpp ;
Table dist ,sex*rpp*mean;
format dist distance. sex gender.;
run;

15. PROC TABULATE with multiple statistics requested
Proc tabulate data=rentdata.rent2;
class dist no rm sex ;
var rent rpp ;
Table dist , sex*rpp*(n mean stddev);
format dist distance. sex gender.;
run;

16. PROC TABULATE with headings cleaned up.
Proc tabulate data=rentdata.rent2;
class dist no rm sex ;
var rent rpp ;
Table
(all=Total dist) ,
(all='Total' sex='')*rpp=''*(n mean stddev);
format dist distance. sex gender.;
run;

17. PROC TTEST – Are the Means of RPP Different by Sex?
proc ttest
data=rentdata.rent2 ;
class sex;
var rpp;
run;
MALES
FEMALES
MALES
FEMALES

18. Creating fitted curves, linear regression and loess regression
proc SGPLOT
data=rentdata.rent2;
reg x=dist y=rpp /
clm clmtransparency=0 ;
loess x=dist y=rpp /
clm clmtransparency=.7
nomarkers ;
run;
Creating fitted curves,
pp. 240-241, LSB

19. Judging by model I below, females do not pay higher rents.

20. But according to equation III, females do pay higher rents and the
effect comes primarily through the variable distance.

21. And models IV and V show us that
females pay higher rents due to
being further away from campus,
perhaps in a better / safer
neighborhood while males prefer
higher rents to be on campus.

22. Creating fitted curves, classical linear regression vs loess regression
proc SGPLOT
data=rentdata.rent2;
reg x=dist y=rpp /
group=sex
clm clmtransparency=.6 ;
loess x=dist y=rpp /
group=sex
clm clmtransparency=.2
nomarkers ;
run;
Females pay higher rents
on-campus or farther away
from campus. They apparently
pay premiums that their male
counterparts do not.