Question

1. Using the Excel’s Regression Tool, develop the estimated regression equation to show how income (y annual income in $1000s) is related to the independent variables education (x1 $0CGOU8mpdxUQgAAAAASUVORK5CYII=$ level of education attained in number of years), age (x2 in years), and gender x3 dummy variable, 1= female, 0 = male. Develop the dummy variable for the gender variable first.

2. Use the t test to test whether each of the coefficients obtained in part (a) are significant at .05 level of significance. What are your conclusions?

3. Use the F test to test for overall significance of the relationship. What is your conclusion?

4. Fully interpret the meaning of the coefficient on gender, x3 $4I3YvsBgbuYAwMAAAAASUVORK5CYII=$ .  

5. Predict the annual income for a female aged 45 with 10 years of education. How much would the predicted income have changed for a male?

6. Plot the standardized residuals against predicted income, y from regression in part (a). Check for outliers and explain whether the residual plot supports the assumptions about Ɛ. What is your conclusion? Submit the graph to earn full points.

Answer 1

era y th how Som Regression equation will be Y = Bot B, X, + D, X ₂ + D₂ X3 te Dummy rariables takes ralues in following way /D, D₂ ) re por S.E If 197 So the estimated regoession e quarting will be } = R + Bix, + X2 (0, 3) = (10)] } = Bot Bix, +%3 [(@, ...) = (0,1)] 9 = Bot pix [ (P, Pz) = (0,0)]. i-fi Hoid, -o (null hypo) H:ßito (Alt. hypo) t calculated > t tabulated reject null hypothesis. © By running the regression in Spss or Excel we easily get F-test value from ANOVA table (But Here data set is not provided so, I'm giving just Sketch of sor). Coefficent of variable X₂ is Dz which with tells that geneder will have impact on dependent variable y only when D₂=1. © & 6 can be done by use of spss when data pet are available Cie By running regression).