Exploring the Baseball data with SAS and SAS/INSIGHT

Locating the Baseball files

In SAS programming statements, refer to the data in a PROC step or DATA step by the following names:

SAS name
------------
psy6140.baseball
psy6140.pitcher
psy6140.team

proc univariate data=psy6140.baseball;
   var ....

Starting SAS/INSIGHT

Viewing the data

Some things to try:

1. Double-click on the NAME variable to see its properties. Assign NAME the LABEL role. Ctrl-Click to deselect the NAME variable.
2. Double-click on the "4" next to Andre Dawson. The Examine Observations panel pops up, showing his statistics.
3. From the Edit menu, select Observations, then Find... Highlight all the players on the Atlanta team by selecting TEAM, and ATL from the panels. When you click Apply or OK, all players are highlighted in the spreadsheet. What happens if you then click a color or symbol in the tool bar?

Overview of the data

The scatterplot matrix is linked to the data table (and to all active views of the dataset:

• Click on any point in the scatterplot matrix. The NAME label apears, and the corresponding point is highlighted in all other pairwise plots. Ctrl-click on another point to extend the selection.
• Click on any observation number in the data table. The corresponding point is highlighted in the scatterplot matrix.
• Double-click on any observation number in the data table. A popup window shows all values for this observation.

Preliminary analyses

• Choose Kernel Density... from the Curves menu, press OK to select the default. A smoothed, non-parametric curve is fit to the distribution and displayed on the histogram.
• Choose Parametric Density... from the Curves menu, press OK to select the default. A smoothed,parametric (Normal) curve is fit to the distribution and displayed on the histogram.

Subgrouping the data

For some purposes, a simpler way to do the same sort of thing is to click the "rainbow" button on the Tools panel. In the Color Observations panel that appears, select, e.g., POSITION. Then, try scatterplot(s) of some variables (e.g., PUTOUTS vs. ASSISTS) to see them color coded by the variable you selected. (You can also use a continuous variable for color coding by rainbow colors.)

Similarly, the button containing all point markers in the Tools panel brings up a Mark Observations panel. You can assign the same or a different variable to set the point markers for observations.

Try some simple regression models

• Select the SALARY variable, then click on Y to assign it as the dependent variable.
• Select YEARS, then click on X to assign it an a predictor. Do the same with RUNS.
• Click on the Output button to see what options are available. Select Nonparametric Curves and click on loess to fit a smooth curve to the data
• When you have completed your OUTPUT selections, click on the Apply button in the Fit (Y X) panel.

Some other things

1. How can you make a boxplot of the SALARY variable? What players have extremely high/low salaries?
2. How can you make a set of parallel boxplots of SALARY, stratified by number of YEARS in the major leagues? (Hint: assign YEARS as the X variable in the Boxplot dialog box.)
3. How can you transform a variable? (Hint: select a variable, then look in the Edit menu)
4. How can you assign a format to a variable? (Hint: same hint as above)
5. Examine the data table for any newly-created variables from the Fit analysis. See the VARS menu for some other variables that can be added to the dataset.
6. Some analyses add additional variables to the data table (e.g., residuals, Cook's D, etc.). If you want to save the modified data table, choose Save:Data... from the File menu. You can only save it to the HOME Library, which is located on your F: drive. To access the modified data later, Open it from the HOME library.
7. To exit SAS/INSIGHT, click in the Menu square (upper left corner) of the data table window, and choose Close.

SAS programming statements and macro programs

Transforming variables

data baseball;
   set psy6140.baseball;
   logsal = log10(salary);
   label logsal = 'log Salary';

Creating dummy variables

%dummy(data=baseball, var=position, format=$pos, base=first);

Checking for outliers

%outlier(data=baseball, id=name,
   var=logsal atbat hits homer runs rbi walks years 
       putouts assists errors);

Sample regression

symbol value=dot;
proc reg data=baseball;
   model logsal = years runs hits rbi walks;
   plot r. * p.;
   run; quit;

Influence plots

%inflplot(data=baseball, id=name,
   y=logsal, 
   x=years runs hits rbi walks);

Box-Cox transformations

1. The statement below calls the BOXCOX macro to transform SALARY in a model using YEARS, RUNS, and HITS as predictors:

   %boxcox(data=psy6140.baseball,
      resp=salary,
   	model=years runs hits,
   	id=name,
   	gplot=RMSE EFFECT INFL);
   

2. Use %webhelp(boxcox); to get information on the use of the boxcox macro. (%webhelp(?); gives some basic information on its use.)

Merging files

title2 'Merge team at-home attendance with individual data';
proc sort data=psy6140.team out=team;
   by team;
proc sort data=baseball;
   by team;
data basenew;
   merge baseball team(keep=team atthome);
   by team;

To merge the other way--- incorporating variables from the individual file into the TEAM file, first use PROC SUMMARY to get the mean (or median) of the variables you want, then merge as above:

title2 'Average stats by team, merge with the team data'   ;
proc summary data=baseball nway;
   class team;
   var runs homer;
   output out=means mean=;

proc sort data=team;
   by team;
data teamnew;
   merge team means(keep=team runs homer);
   by team;