3. The Lottery data Excel le provides data on the topic of \"playing the lottery\". It is often claimed that individuals who play the lottery are those who can least afford to do so, making the impact of the lottery \"regressive\". In this context, regressive implies that lower income individuals play the lottery more than do higher income individuals. The data for this analysis include 100 randomly chosen men from a major metropolitan area. For each individual, the following information has been recorded: - The number of times in the preceding month the individual has played a state-sponsored lottery. This is the dependent variable in the accompanying regression analysis. - The education of the individual, measured by the number of years of schooling completed. Note that 12 years of schooling completed means that the person has graduated from \"high school\b. For the rst row of actual data only, use the independent variable values to \"predict\" the value of the dependent variable. For this row, also compute the \"residual\". c. For the independent variables \"Age\" and \"Income\" interpret the numerical value of the slope. d. Evaluate the statistical significance of each of the four slope estimates. This can be done in a very summary way. Start out by indicating the "null value" against which you will be testing each of the four slope estimates. Then, next to the name of each independent variable, state whether the slope is "significant", and why. The "why" should be stated in no more than a few words or a single sentence.e. Is the premise of this research, as stated in the opening paragraph of this question (#3) supported by these regression results? Why or why not (a short answer here is preferable). f. Interpret the R-square for this regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.658387462 Dep: Lottery R Square 0.43347405 Adjusted R Square 0.409620325 Standard Error 2.909780022 Observations 100 ANOVA df SS MS F Significance F Regression 4 615.442121 153.8605 18.17217 4.09443E-11 Residual 95 804.347879 8.46682 Total 99 1419.79 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 11.90609377 1.785196734 6.669345 1.69E-09 8.362030253 15.45016 Education -0.43001847 0. 132071926 -3.25594 0.001567 -0.69221439 -0.16782 Age 0.029189885 0.025227671 1.157058 0.25015 -0.02089337 0.079273 Children 0.093435096 0.224313376 0.416538 0.677956 -0.35188325 0.538753 Income -0.07447053 0.027726044 -2.68594 0.008537 -0. 12951369 -0.01943