Lecture 3: Multiple Regression Model - McCombs School of Business

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Why not look at R 2 ◮ R 2 in MLR is still a measure of goodness of fit. ◮ However it ALWAYS grows as we increase the number of explanatory variables. ◮ Even if there is no relationship between the X ′ s and Y , R 2 > 0!! ◮ To see this let’s look at some “Garbage” Data 22
F-tests Garbage Data ./$0""$12*03$)"140$5"6"781"$0/9"$587:85"$+818$1281$280$6/12*65$1/$ +/$;*12$//7986?"$@ABC I made up 6 “garbage” variables that have nothing to do with Y ... D*701$)"140$98#"$E=$0/9"$F587:85"G$@HI3$H,3$J3$HKB$
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 and 12: Residual Standard Error First off,
Page 13 and 14: Residuals in MLR Consider the resid
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: Supervisor Performance Data F-tests
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

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