This document discusses using SAS programming to analyze social media recruitment data. It includes importing data files, merging files, conducting frequency analysis, means analysis, ANOVA, correlation, regression, and creating graphs and charts like bar charts, pie charts, and scatterplots. SAS code is provided for merging data, conducting statistical tests, and creating various graphs and visualizations to analyze the social media recruitment data.
2. SAS programming Module
• Importing Data from the file
• Merging the Files for Analysis
• Frequency metrics
• Means , Factor Analysis
• Anova
• Correlation
• Regression
• Logistic Regression
• Chi square
• Graphs
• Plots
• Piecharts
• Block Charts
3. Social Media Recruitment Analysis
• Proc import datafile="D:sassasmain.xls"
• out=work.social1;
• run;
• proc import datafile="D:sasexp.xls"
• out=work.social2;
• run;
• Proc sql;
• create table work.media as
• select *
• from work.social1, work.social2
• where social1.exp=social2.exp;
• order by social1.sno;
• quit;
• ods pdf file= 'D:sasassign.pdf';
• startpage = 1;
• ods pdf text = "SAS ASSIGNMENT-13MBA1021";
• ods html file= 'D:sasassign.html';
6. Vertical and horizontal graphs charts
with discrete options
• TITLE 'Bar Chart with Discrete Option';
• PROC GCHART DATA=media;
• VBAR roi/ DISCRETE;
• RUN;
• TITLE 'Horizontal Bar Chart with Discrete';
• PROC GCHART DATA=media;
• HBAR reg/ DISCRETE;
• RUN;
7.
8. Pie chart coding
TITLE 'Pie Chart with Discrete';
PROC GCHART DATA=media;
PIE roi/ DISCRETE VALUE=INSIDE
PERCENT=INSIDE SLICE=OUTSIDE;
RUN;
9. Frequency analysis for brand
recognition and competitive
advantage
Variable Label N Mean Std Dev Minimum
Maximu
m
ROI
brandreco
gnition
competitiv
eadvanta
ge
ROI
brandreco
gnition
competitiv
eadvanta
ge
26
26
26
3.423076
9
2.923076
9
3.269230
8
1.238485
4
1.354195
8
1.343359
7
1.000000
0
1.000000
0
1.000000
0
5.000000
0
5.000000
0
5.000000
0
The MEANS Procedure
The SAS System
10. Means and chisq analysis
proc means data =work.media;
var emp totemp;
run;
proc freq data =work.media;
tables exp * roi/chisq;
run;
proc freq data =work.media;
tables reg * roi/chisq;
run;
11. Correlation , regression and anova
proc corr data =work.media;
var exp roi reg compadv;
run;
proc reg data =work.media;
model roi reg compadv = exp;
run;
proc anova data =work.media;
class exp;
model imp emp totemp = exp;
run;
ods html close;
run;
ods pdf close;
quit;
Pearson Correlation Coefficients, N = 26
Prob > |r| under H0: Rho=0
ROI
brandrec
ognition
competiti
veadvant
age
ROI
ROI
1.00000 -0.00367
0.9858
0.00092
0.9964
brandrec
ognition
brandrec
ognition
-0.00367
0.9858
1.00000 -0.05412
0.7929
competiti
veadvant
age
competiti
veadvant
age
0.00092
0.9964
-0.05412
0.7929
1.00000
12. The CORR Procedure3 Variables:
ROI brandrecognition
competitiveadvantage
Simple Statistics
Variable N Mean Std Dev Sum
Minimu
m
Maximu
m Label
ROI 26 3.42308 1.23849
89.0000
0
1.00000 5.00000 ROI
brandre
cognitio
n
26 2.92308 1.35420
76.0000
0
1.00000 5.00000
brandre
cognitio
n
competi
tiveadv
antage
26 3.26923 1.34336
85.0000
0
1.00000 5.00000
competit
iveadva
ntage
13.
14. Scatterplot for two different symbols
• TITLE 'Scatterplot - Two Variables';
• PROC GPLOT DATA=media;
• PLOT imp*emp ;
• RUN;
• SYMBOL1 V=circle C=black I=none;
• SYMBOL2 V=star C=red I=none;
• TITLE 'Scatterplot - Different Symbols';
• PROC GPLOT DATA=media;
• PLOT exp*emp=roi;
• RUN;
• QUIT;
• SYMBOL1 V=circle C=blue I=r;
15.
16. TITLE 'Scatterplot - With Regression Line ';
PROC GPLOT DATA=media;
PLOT exp*roi ;
RUN;
QUIT;