Stat 301 Lab 5 – In Lab
Overview
In this lab you will be introduced to using JMP for multiple linear regression analysis.
For this lab you need to be sitting in front of Windows PC that has JMP.
Warm-up Exercise
Multiple Linear Regression is available in JMP using the Fit Model platform. Fit Model
is under Modeling on the JMP Starter and is also available under the Analyze pull-down
menu. This handout illustrates obtaining multiple regression output from JMP’s Fit
Model analysis platform.
In class we introduced the example involving the evaluation of student teachers. The
response variable, Y, is the evaluation score given to the student teacher by an
experienced teacher. The explanatory variables are the scores on four standardized tests.
The data is available on the course web page
www.public.iastate.edu/~wrstephe/stat301.html
1. Go to the course web page and get the data for the teacher evaluation example
into JMP. Double click on the Lab 5 – JMP file for Teacher Evaluation Example
link.
2. In JMP go to Analyze and Fit Model. Cast EVAL into the role of the response
variable Y. Add Test1, Test2, Test3 and Test4 to the Construct Model Effects
box. Change the Emphasis to Minimal Report. Click on Run Model.
3. By right clicking on the Parameter Estimates table you can add Columns that
include the upper and lower 95% confidence interval values.
4. From the red triangle pull down menu next to Response EVAL select Row
Diagnostics – Plot Residual by Predicted. This plot will be used to assess model
adequacy and the equal standard deviation condition.
5. From the same red triangle pull down menu you can Save Columns of
 Predicted Values
 Residuals
 Mean Confidence Interval
 Indiv Confidence Interval
6. Plot residuals versus each of the explanatory variables.
7. Use Analyze – Distribution to look at the distribution of residuals.
1
Response: EVAL
Summary of Fit
RSquare
RSquare Adj
Root Mean Square Error
Mean of Response
Observations (or Sum Wgts)
0.802861
0.759052
37.53627
444.4783
23
Analysis of Variance
Source
Model
Error
C. Total
DF
4
18
22
Sum of Squares
103286.25
25361.49
128647.74
Mean Square
25821.6
1409.0
F Ratio
18.3265
Prob > F
<.0001*
Parameter Estimates
Term
Intercept
Test1
Test2
Test3
Test4
Estimate
–193.4994
1.1158539
2.243267
–1.367001
6.0482387
Std Error
125.3074
0.319746
0.628449
0.563965
1.202281
t Ratio
–1.54
3.49
3.57
–2.42
5.03
Prob>|t|
0.1399
0.0026*
0.0022*
0.0261*
<.0001*
Residual by Predicted Plot
2
Residual
3