/*st103d01.sas*/
/* Set graphics off initially */
ods graphics off;
/* Part A: Descriptive Analysis */
/* Use PROC MEANS to calculate selected descriptive statistics */
proc means data=STAT1.ameshousing3
mean var std nway;
/* Group data by Season_Sold and Heating_QC variables */
class Season_Sold Heating_QC;
/* Calculate statistics for SalePrice variable */
var SalePrice;
/* Format Season_Sold variable */
format Season_Sold Season.;
/* Specify title for output */
title 'Selected Descriptive Statistics';
run;
/* Part B: Graphical Analysis */
/* Use PROC SGPLOT to create a line plot of SalePrice by Season_Sold and Heating_QC */
proc sgplot data=STAT1.ameshousing3;
/* Group data by Season_Sold and Heating_QC variables */
vline Season_Sold / group=Heating_QC
/* Calculate mean SalePrice for each group */
stat=mean
/* Use SalePrice as the response variable */
response=SalePrice
/* Add markers to the plot */
markers;
/* Format Season_Sold variable */
format Season_Sold season.;
run;
/* Part C: Inferential Analysis */
/* Set graphics on for this part */
ods graphics on;
/* Use PROC GLM to create a linear regression model */
proc glm data=STAT1.ameshousing3 order=internal;
/* Group data by Season_Sold and Heating_QC variables */
class Season_Sold Heating_QC;
/* Use Heating_QC and Season_Sold variables to predict SalePrice */
model SalePrice = Heating_QC Season_Sold;
/* Calculate least squares means for Season_Sold variable */
lsmeans Season_Sold / diff adjust=tukey;
/* Format Season_Sold variable */
format Season_Sold season.;
/* Specify title for output */
title "Model with Heating Quality and Season as Predictors";
run;
/* Quit PROC GLM */
quit;
/* Remove title from output */
title;
Example 2:
/*st103d02.sas*/
/* Part A: Creating an Interaction Model */
/* Turn graphics on for this part */
ods graphics on;
/* Use PROC GLM to create an interaction model */
proc glm data=STAT1.ameshousing3
order=internal
/* Only plot the interaction plot */
plots(only)=intplot;
/* Group data by Season_Sold and Heating_QC variables */
class Season_Sold Heating_QC;
/* Use Heating_QC, Season_Sold, and their interaction to predict SalePrice */
model SalePrice = Heating_QC Season_Sold Heating_QC*Season_Sold;
/* Calculate least squares means for Heating_QC and Season_Sold variables */
lsmeans Heating_QC*Season_Sold / diff slice=Heating_QC;
/* Format Season_Sold variable */
format Season_Sold Season.;
/* Store the interaction model */
store out=interact;
/* Specify title for output */
title "Model with Heating Quality and Season as Interacting Predictors";
run;
/* Quit PROC GLM */
quit;
/* Part B: Creating an Interaction Plot */
/* Use PROC PLM to create an interaction plot */
proc plm restore=interact
/* Show all available plots */
plots=all;
/* Slice the data by Heating_QC variable and adjust for multiple comparisons using Tukey's method */
slice Heating_QC*Season_Sold / sliceby=Heating_QC adjust=tukey;
/* Create an effect plot with interaction and display confidence limits for the means */
effectplot interaction(sliceby=Heating_QC) / clm;
run;
/* Remove title from output */
title;
Example 3:
/*st103s01.sas*/
/* Part A: Creating a Grouped Means Plot */
/* Use PROC SGPLOT to create a grouped means plot */
proc sgplot data=STAT1.drug;
/* Group the data by Disease variable and plot mean BloodP against DrugDose */
vline DrugDose / group=Disease
stat=mean
response=BloodP
markers;
/* Format the DrugDose variable */
format DrugDose dosefmt.;
run;
/* Part B: Creating an Interaction Model */
/* Turn graphics on for this part */
ods graphics on;
/* Use PROC GLM to create an interaction model */
proc glm data=STAT1.drug plots(only)=intplot;
/* Group data by DrugDose and Disease variables */
class DrugDose Disease;
/* Use DrugDose, Disease, and their interaction to predict BloodP */
model BloodP = DrugDose|Disease;
/* Calculate least squares means for DrugDose and Disease variables */
lsmeans DrugDose*Disease / slice=Disease;
run;
/* Quit PROC GLM */
quit;
/* Remove title from output */
title;
/*st103d03.sas*/
/* Part A: Creating a Simple Linear Regression Model */
/* Turn graphics on for this part */
ods graphics on;
/* Use PROC REG to create a simple linear regression model */
proc reg data=STAT1.ameshousing3 ;
/* Use Basement_Area and Lot_Area to predict SalePrice */
model SalePrice=Basement_Area Lot_Area;
/* Add a title to the output */
title "Model with Basement Area and Lot Area";
run;
/* Quit PROC REG */
quit;
/* Part B: Creating a General Linear Model with Contour Plot */
/* Use PROC GLM to create a general linear model with a contour plot */
proc glm data=STAT1.ameshousing3
plots(only)=(contourfit);
/* Use Basement_Area and Lot_Area to predict SalePrice */
model SalePrice=Basement_Area Lot_Area;
/* Store the results of the model */
store out=multiple;
/* Add a title to the output */
title "Model with Basement Area and Gross Living Area";
run;
/* Quit PROC GLM */
quit;
/* Part C: Creating an Effect Plot with Sliced Fit */
/* Use PROC PLM to create an effect plot with a sliced fit */
proc plm restore=multiple plots=all;
/* Plot the contour of the fitted surface */
effectplot contour (y=Basement_Area x=Lot_Area);
/* Plot the sliced fit with slices of Basement_Area at 250 to 1000 by 250 */
effectplot slicefit(x=Lot_Area sliceby=Basement_Area=250 to 1000 by 250);
run;
/* Remove title from output */
title;
/*st103s02.sas*/ /*Part A*/
/* Turn off graphics output */
ods graphics off;
/* Run simple linear regression using all 12 predictors */
proc reg data=STAT1.BodyFat2;
model PctBodyFat2=Age Weight Height
Neck Chest Abdomen Hip Thigh
Knee Ankle Biceps Forearm Wrist;
/* Give title to the output */
title 'Regression of PctBodyFat2 on All '
'Predictors';
run;
/* End the regression procedure */
quit;
/*st103s02.sas*/ /*Part B*/
/* Run simple linear regression using all predictors except Knee */
proc reg data=STAT1.BodyFat2;
model PctBodyFat2=Age Weight Height
Neck Chest Abdomen Hip Thigh
Ankle Biceps Forearm Wrist;
/* Give title to the output */
title 'Regression of PctBodyFat2 on All '
'Predictors, Minus Knee';
run;
/* End the regression procedure */
quit;
/*st103s02.sas*/ /*Part C*/
/* Run simple linear regression using all predictors except Knee and Chest */
proc reg data=STAT1.BodyFat2;
model PctBodyFat2=Age Weight Height
Neck Abdomen Hip Thigh
Ankle Biceps Forearm Wrist;
/* Give title to the output */
title 'Regression of PctBodyFat2 on All '
'Predictors, Minus Knee, Chest';
run;
/* End the regression procedure */
quit;