Transformations Using SAS
Transformations Using SAS
/*Use the permanent SAS baseball data set*/
libname sasdata2 "d:\sasdata2";
title "Baseball Data Set";
proc means data=sasdata2.baseball;
run;
Baseball Data Set
The MEANS Procedure
Variable Label N Mean Std Dev Minimum
---------------------------------------------------------------------------------------------
no_atbat Times at Bat in 1986 322 390.0745342 143.5958352 127.0000000
no_hits Hits in 1986 322 103.3975155 44.1795091 31.0000000
no_home Home Runs in 1986 322 11.1024845 8.6987696 0
no_runs Runs in 1986 322 52.2173913 25.0573661 12.0000000
no_rbi RBIs in 1986 322 49.3726708 25.5011624 8.0000000
no_bb Walks in 1986 322 39.8571429 21.0959408 3.0000000
yr_major Years in the Major Leagues 322 7.6801242 4.9697066 1.0000000
cr_atbat Career times at bat 322 2763.08 2328.48 166.0000000
cr_hits Career Hits 322 747.6863354 654.7876194 34.0000000
cr_home Career Home Runs 322 74.0900621 90.0651268 0
cr_runs Career Runs 322 374.2857143 336.4250377 18.0000000
cr_rbi Career RBIs 322 347.6149068 338.7903452 9.0000000
cr_bb Career Walks 322 273.3944099 273.6253716 8.0000000
no_outs Put Outs in 1986 322 288.9937888 280.6566732 0
no_assts Assists in 1986 322 106.9161491 136.8524541 0
no_error Errors in 1986 322 8.0403727 6.3683591 0
salary 1987 Salary in $ Thousands 263 535.9258821 451.1186807 67.5000000
---------------------------------------------------------------------------------------------
Variable Label Maximum
------------------------------------------------------
no_atbat Times at Bat in 1986 687.0000000
no_hits Hits in 1986 238.0000000
no_home Home Runs in 1986 40.0000000
no_runs Runs in 1986 130.0000000
no_rbi RBIs in 1986 121.0000000
no_bb Walks in 1986 105.0000000
yr_major Years in the Major Leagues 24.0000000
cr_atbat Career times at bat 14053.00
cr_hits Career Hits 4256.00
cr_home Career Home Runs 548.0000000
cr_runs Career Runs 2165.00
cr_rbi Career RBIs 1659.00
cr_bb Career Walks 1566.00
no_outs Put Outs in 1986 1378.00
no_assts Assists in 1986 492.0000000
no_error Errors in 1986 32.0000000
salary 1987 Salary in $ Thousands 2460.00
------------------------------------------------------
proc freq data=sasdata2.baseball;
tables team league division;
run;
Baseball Data Set
The FREQ Procedure
Team at the end of 1986
Cumulative Cumulative
team Frequency Percent Frequency Percent
-----------------------------------------------------------------
Atlanta 11 3.42 11 3.42
Baltimore 15 4.66 26 8.07
Boston 10 3.11 36 11.18
California 13 4.04 49 15.22
Chicago 24 7.45 73 22.67
Cincinnati 12 3.73 85 26.40
Cleveland 12 3.73 97 30.12
Detroit 12 3.73 109 33.85
Houston 11 3.42 120 37.27
KansasCity 14 4.35 134 41.61
LosAngeles 14 4.35 148 45.96
Milwaukee 14 4.35 162 50.31
Minneapolis 13 4.04 175 54.35
Montreal 14 4.35 189 58.70
NewYork 24 7.45 213 66.15
Oakland 12 3.73 225 69.88
Philadelphia 12 3.73 237 73.60
Pittsburgh 11 3.42 248 77.02
SanDiego 13 4.04 261 81.06
SanFrancisco 14 4.35 275 85.40
Seattle 12 3.73 287 89.13
StLouis 11 3.42 298 92.55
Texas 13 4.04 311 96.58
Toronto 11 3.42 322 100.00
League at the end of 1986
Cumulative Cumulative
league Frequency Percent Frequency Percent
-------------------------------------------------------------
American 175 54.35 175 54.35
National 147 45.65 322 100.00
Division at the end of 1986
Cumulative Cumulative
division Frequency Percent Frequency Percent
-------------------------------------------------------------
East 157 48.76 157 48.76
West 165 51.24 322 100.00
proc univariate data=sasdata2.baseball;
var salary;
histogram;
qqplot / normal(mu=est sigma=est);
run;
[pic] [pic]
goptions reset=all;
goptions device=win target=winprtm;
symbol1 color=black value=dot height=.5 interpol=rl;
title "Salary vs. Number of Hits in Previous Year";
proc gplot data=sasdata2.baseball;
plot salary * no_hits;
run; quit;
[pic]
proc reg data=sasdata2.baseball;
model salary = no_hits;
plot rstudent.*predicted.;
output out=regdata1 p=predict r=resid rstudent=rstudent;
run; quit;
Salary vs. Number of Hits in Previous Year
The REG Procedure
Model: MODEL1
Dependent Variable: salary 1987 Salary in $ Thousands
Number of Observations Read 322
Number of Observations Used 263
Number of Observations with Missing Values 59
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Pr > F
Model 1 13402507 13402507 87.63 |t|
Intercept Intercept 1 -25.27489 64.61726 -0.39 0.6960
no_hits Hits in 1986 1 5.14110 0.54919 9.36 F
Model 1 49.86811 49.86811 83.57 |t|
Intercept Intercept 1 4.84862 0.12764 37.99 ................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related download
- ancova examples using sas university of michigan
- multiple regression ii
- transformations using sas
- steps for conducting multiple linear regression
- start day 4
- working with sas formats catalogs
- defining data in spss
- ancova examples using sas
- structural equation modeling purdue university
- analysis of augmented designs using sas
Related searches
- describe the transformations from
- transformations of exponential functions worksheet
- using sas for data analysis
- describing transformations calculator
- parent graphs transformations answer key
- algebra transformations calculator
- parent functions and transformations worksheet
- geometry transformations calculator
- transformations of functions graphs worksheet
- desmos transformations calculator
- sas convert character date to sas date
- transformations quiz pdf