9.4. Multiple Regression http://www.ck12.org
- Texas Instrument Website that includes supplemental activities and practice problems using the TI-83 calcu-
lator- education.ti.com/educationportal/activityexchange/activity_list.do
Review Questions
The lead English teacher is trying to determine the relationship between three tests given throughout the semester
and the final exam. She decides to conduct a mini-study on this relationship and collects the test data (scores for
Test 1, Test 2, Test 3 and the final exam) for 50 students in freshman English. She enters these data into Microsoft
Excel and arrives at the following summary statistics:
Multiple R 0. 6859
R Square 0. 4707
Adjusted R Square 0. 4369
Standard Error 7. 5718
Observations 50
TABLE9.16: ANOVA
d f SS MS F Significance
F
Regression 3 2342. 7228 780. 9076 13. 621. 0000
Residual 46 2637. 2772 57. 3321
Total 49 4980. 0000
TABLE9.17:
Coefficients Standard Error t Stat P−value
Intercept 10. 7592 7. 6268
Test 1 0. 0506. 1720. 2941. 7700
Test 2. 5560. 1431 3. 885. 0003
Test 3. 2128. 1782 1. 194. 2387
- How many predictor variables are there in this scenario? What are the names of these predictor variables?
- What does the regression coefficient for Test 2 tell us?
- What is the regression model for this analysis?
- What is theR^2 value and what does it indicate?
- Determine whether the multipleRis statistically significant.
- Which of the predictor variables are statistically significant? What is the reasoning behind this decision?
- Given this information, would you include all three predictor variables in the multiple regression model? Why
or why not?