Below you are given a partial Excel output based on a sample of 16 observations. ANOVA           df    SS MS F Regression     4,853   2,426.5     Residual         485.3                       Coefficients Standard Error Intercept 12.924   4.425   x1 -3.682   2.630   x2 45.216   12.560   The sum of squares due to error (SSE) equals _____.   a. 4,853     b. 485.3     c. 37.33     d. 6,308.9

Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 293.3 293.3 2.01 0.158 Residual Error 174 25386.6 145.9     Total 175 25679.9 The F-statistic is _____________.The p-value is ___________________

Last year, TimeWise conducted a study that aimed at predicting the waiting time (in minutes) at the checkout line of the Ranch 99 Supermarket in South Jakarta based on the number of customers in one particular month. Using the simple regression method on a dataset with a sample size of 30, the regression output tables are given below: SUMMARY OUTPUT Regression Statistics Multiple R 0.88728417 R Square 0.7872732 Adjusted R Square 0.77967582 Standard Error 0.59521532 Observations 30 ANOVA MS Significance F Regression 36.7121241 36.7121241 103.6242271 6.48782E-11 Residual 28 9.91987592 0.35428128 Total 29 46.632 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept -0.4479791 0.2782914 -1.6097484 0.118670876 -1.018033233 0.12207495 Customers 0.12847188 0.01262053 10.1795986 6.48782E-11 0.102619905 0.15432385 a. Determine the simple regression model to predict the waiting time b. Interpret the meaning of the regression coefficient of the independent variable c. What is…

7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression…