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Chapter 7 Using Indicator and Interaction Variables

Chapter 7 Using Indicator and Interaction Variables. Terry Dielman Applied Regression Analysis: A Second Course in Business and Economic Statistics, fourth edition. POP QUIZ #10 [2.5 points]. POP QUIZ #10. 1. A good way to incorporate gender information into a regression model is to

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Chapter 7 Using Indicator and Interaction Variables

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  1. Chapter 7Using Indicator and Interaction Variables Terry Dielman Applied Regression Analysis: A Second Course in Business and Economic Statistics, fourth edition Indicator Variables

  2. POP QUIZ #10 [2.5 points]

  3. POP QUIZ #10 1. A good way to incorporate gender information into a regression model is to • Add a variable X1, with X1=1 for males and X1=0 for females • Add a variable X1, with X1=1 for females and X1=0 for males • Either of (A) or (B); they are equivalent • Split the data into two groups, one for males and one for females, and run two regression models

  4. POP QUIZ #10 • In the Treasury vs. Harris case, what was the effect of adding the MALES dummy variable on the fitted line? • It moved the Salary vs. Education line up • It moved the Salary vs. Education line down • It rotated the Salary vs. Education line • It left the Salary vs. Education line unchanged • It split the Salary vs. Education line into two lines

  5. POP QUIZ #10 3. In the Meddicorp example, how many dummy variables do we need to model 3 regions? • 2 dummy variables • 3 dummy variables • 4 dummy variables • None of the above

  6. POP QUIZ #10 4. In the Treasury vs. Harris case, what was the effect of adding the interaction term MSLOPE = EDUCAT*MALES on the fitted line? • It had no effect on the Salary vs. Education line • It moved the Salary vs. Education line up • It split the Salary vs. Education line into two parallel lines • It split the Salary vs. Education line into two lines, not necessarily parallel

  7. POP QUIZ #10 The regression equation is SALES = 211 + 2.57 TIME + 3.75 Q1 - 26.1 Q2 - 25.8 Q3 Predictor Coef SE Coef T P Constant 210.846 3.148 66.98 0.000 TIME 2.56610 0.09895 25.93 0.000 Q1 3.748 3.229 1.16 0.254 Q2 -26.118 3.222 -8.11 0.000 Q3 -25.784 3.217 -8.01 0.000 • Regression output of the Sales vs. Time data is shown above. Sales were recorded every quarter. What does it mean for the p-value of Q1 to be 0.254? • SALES in quarter 1 are not related to TIME • SALES in quarter 1 are not different from quarter 4 • SALES in quarter 1 are not different from quarters 2 & 3 • SALES in quarter 1 should be deleted from the data

  8. Answers! √

  9. POP QUIZ #10 1. A good way to incorporate gender information into a regression model is to • Add a variable X1, with X1=1 for males and X1=0 for females • Add a variable X1, with X1=1 for females and X1=0 for males • Either of (A) or (B); they are equivalent • Split the data into two groups, one for males and one for females, and run two regression models √

  10. POP QUIZ #10 • In the Treasury vs. Harris case, what was the effect of adding the MALES dummy variable on the fitted line? • It moved the Salary vs. Education line up • It moved the Salary vs. Education line down • It rotated the Salary vs. Education line • It left the Salary vs. Education line unchanged • It split the Salary vs. Education line into two lines √

  11. POP QUIZ #10 3. In the Meddicorp example, how many dummy variables do we need to model 3 regions? • 2 dummy variables • 3 dummy variables • 4 dummy variables • None of the above √

  12. POP QUIZ #10 4. In the Treasury vs. Harris case, what was the effect of adding the interaction term MSLOPE = EDUCAT*MALES on the fitted line? • It had no effect on the Salary vs. Education line • It moved the Salary vs. Education line up • It split the Salary vs. Education line into two parallel lines • It split the Salary vs. Education line into two lines, not necessarily parallel √

  13. POP QUIZ #10 The regression equation is SALES = 211 + 2.57 TIME + 3.75 Q1 - 26.1 Q2 - 25.8 Q3 Predictor Coef SE Coef T P Constant 210.846 3.148 66.98 0.000 TIME 2.56610 0.09895 25.93 0.000 Q1 3.748 3.229 1.16 0.254 Q2 -26.118 3.222 -8.11 0.000 Q3 -25.784 3.217 -8.01 0.000 • Regression output of the Sales vs. Time data is shown above. Sales were recorded every quarter. What does it mean for the p-value of Q1 to be 0.254? • SALES in quarter 1 are not related to TIME • SALES in quarter 1 are not different from quarter 4 • SALES in quarter 1 are not different from quarters 2 & 3 • SALES in quarter 1 should be deleted from the data √

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