Lecture 3: Multiple Regression Model - McCombs School of Business

More documents

Recommendations

Info

Residuals in MLR As in the SLR model, the residuals in multiple regression are purged <strong>of</strong> any relationship to the independent variables. We decompose Y into the part predicted by X and the part due to idiosyncratic error. Y = Ŷ + e corr(X j , e) = 0 corr(Ŷ , e) = 0 12
Residuals in MLR Consider the residuals from the Sales data: residuals -0.03 -0.01 0.01 0.03 residuals -0.03 -0.01 0.01 0.03 residuals -0.03 -0.01 0.01 0.03 0.5 1.0 1.5 2.0 fitted 0.2 0.4 0.6 0.8 P1 0.2 0.6 1.0 P2 13
Page 1 and 2: DMBA: Statistics Lecture 3: Multipl
Page 3 and 4: The Multiple Regression Model Many
Page 5 and 6: The MLR Model Our interpretation of
Page 7 and 8: The Data and Least Squares The data
Page 9 and 10: The Data and Least Squares Fitted V
Page 11: Residual Standard Error First off,
Page 15 and 16: Inference for Coefficients As befor
Page 17 and 18: In Excel... -"./0$123$4$multiple 1"
Page 19 and 20: Supervisor Performance Data Suppose
Page 21 and 22: Supervisor Performance Data F-tests
Page 23 and 24: F-tests Garbage Data ./$0""$12*03$)
Page 25 and 26: The F -test Recall what we are test
Page 27 and 28: The F-test ((7 6 # 5 5#685 Two
Page 29 and 30: Partial F -test With the total F -t
Page 31 and 32: Partial F -test It turns out that u
Page 33: Glossary and Equations F -tests and

Lecture 3: Multiple Regression Model - McCombs School of Business

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?