How much of the price is determined by the regression equation that contains all independent variables? Based on the ANOVA F and Significance F values, how likely is it that the regression equation coefficients (taken together, or as a global test) are responsible for predicting the sales price? (choose: not likely, somewhat likely, very likely). Do you see any multicollinearity? Which independent variable has the greatest effect on Sales Price? Consider the t Stat and P-value values for each coefficient. Do they suggest that any independent variables. have too high of a probability of not affecting the dependent variable in the population? Use a significance level of 0.05. How much of the price is determined by the regression equation that contains the remaining independent variables? Examine the residual plots and normal probability plot. Write down any observations that might indicate the underlying assumptions of linear relationships, homoscedasticity, normal distribution of residuals, no multicollinearity, and no autocorrelation might not be true 1 SUMMARY OUTF This is for all variables; place your analysis over top of this guide. Tell Excel it is OK to overwrite this data.Regression StatisticsSUMMARY OUTPUTMultiple RR SquareAdjusted R SqualStandard ErrorObservationsANOVARegressionResidualTotalInterceptBedroomsSizePoolDistanceTwnshipGarageBathssizepool0.73050.53360.499933.311105distancetownship797104garagebaths62.2497.37550.0386-19.11-1.013-1.73935.49823.093Regression StatisticsMultiple F 0.7305RSquare 0.5336Adjusted 0.4999Standard 33.311Observat105ANOVA0.4674RegressiResidualTotal4 Correlation Table Complete the rest of the table.poolIndependent Var: Sales Pri bedroom sizebedrooms22 How much of the price is determined by the regression equation that contains all independent variable 62.2493 Based on the ANOVA F and Significance F values, how likely is it that the regression equation coefficients (taken together,or as a global test) are responsible for predicting the sales price? (choose: not likely, somewhat likely, very likely)Very LikelySS7 12313697 107631104 230768MSFSignificance F17591 15.853 1.008E-131109.6Coefficientandard Er +Star F-value Lower 95% Ipper 95%ower 95. Ooper 95.0%Bedroom 7.3755SizeIntercept 62.249 40.914 1.5215 0.1314 -18.954352 143.45 -18.95 143.452.59 2.8477 0.0054 2.23502257 12.516 2.23512.5160.0386 0.01482.6180.0103 0.00934311 0.0679 0.0093 0.0679Pool-19.11 7.1266 -2.682 0.0086 -33.255676 -4.967 -33.26 -4.967Distance -1.013 0.7414 -1.366 0.1751 -2.4841123 0.4588 -2.484 0.4588Twnship -1.739 2.6994 -0.644 0.521 -7.096602 3.6186 -7.097 3.6186Garage 35.498 7.6758 4.6246 1E-05 20.2636043 50.732 20.264 50.732Baths 23.093 9.0583 2.5493 0.0124 5.11431252 41.071 5.1143 41.071distance township garage0.371 0.3835-0.294 -0.005 -0.201-0.347 -0.153 -0.117 0.13940.1282 0.2001 0.1846 -0.2010.5263 0.2341 0.083 -0.114 0.0567 -0.3590.3822 0.3289 0.0244 -0.055 -0.195 0.0497 0.22135 Do you see any multicollinearity? No6 Which independent variable has the greatest effect on Sales Price' Garage7 Consider the t Stat and P-value values for each coefficient. Do they suggest that any independent variableshave too high of a probability of not affecting the dependent variable in the population? Use a significance level of 0.05.Yes, the T stat and P-value of pool, distance, and township has a high probability of not affecting the dependent variable in the population.Run the regression analysis again, but this time, exclude the independent variables you identified in step 7. You may have to copy the columns youneed to another place on the worksheet to put them all next to each other. Also this time, include residual plots and the normal probability plot.Move your residual plots and normal probability plot to the right of the summary output below.9 How much of the price is determined by the regression equation that contains the remaining independent variable 62.24910 Examine the residual plots and normal probability plot. Write down any observations that might indicate the underlyingQues 98 10 are placedhere due to the lengthof residual plot dataAnswer them aftercompleting #3.assumptions of linear relationships, homoscedasticity, normal distribution of residuals, no multicollinearity,and no autocorrelation might not be true.

When the two variables are associated with each other, one variable is used to estimate the value…

1 SUMMARY OUTF This is for all variables; place your analysis over top of this guide. Tell Excel it is OK to overwrite this data. Regression Statistics SUMMARY OUTPUT Multiple R RSquare Adjusted R Squar Standard Error Observations ANOVA Regression Residual Total Intercept Bedrooms Size Pool Distance Twnship Garage Baths 0.7305 0.5336 0.4999 33.311 105 size pool 7 97 104 distance township garage baths 62.249 7.3755 0.0386 -19.11 -1.013 -1.739 35.498 23.093 Regression Statistics Multiple F 0.7305 RSquare 0.5336 Adjusted 0.4999 Standarc 33.311 Observat 105 ANOVA 0.4674 Regressi Residual Total af Coefficientandard Er. Intercept 62.249 40.914 Bedroom 7.3755 4 Correlation Table Complete the rest of the table. pool Independent Var: Sales Pri- bedroom size bedrooms 2 How much of the price is determined by the regression equation that contains all independent variable 62.249 3 Based on the ANOVA F and Significance F values, how likely is it that the regression equation coefficients (taken together, or as a global test) are responsible for predicting the sales price? (choose: not likely, somewhat likely, very likely) Very Likely MS F Significance F 17591 15.853 1.008E-13 7 123136 97 107631 1109.6 104 230768 Stat F-value Lower 95% pper 95%wer 95. Ooper 95.0% 1.5215 0.1314 -18.954352 143.45 -18.95 143.45 2.59 2.8477 0.0054 2.23502257 12.516 2.235 12.516 0.0386 0.0148 2.618 0.0103 0.00934311 0.0679 0.0093 0.0679 7.1266 -2.682 0.0086 -33.255676 -4.967 -33.26 -4.967 0.7414 -1.366 0.1751 -2.4841123 0.4588 -2.484 0.4588 0.521 -7.096602 3.6186 -7.097 3.6186 1E-05 20.2636043 50.732 20.264 50.732 0.0124 5.11431252 41.071 5.1143 41.071 0.371 0.3835 -0.294 -0.005 -0.201 Size Pool -19.11 Distance -1.013 Twnship -1.739 2.6994 -0.644 Garage 35.498 7.6758 4.6246 Baths 23.093 9.0583 2.5493 distance township garage -0.347 -0.153 -0.117 0.1394 0.1282 0.2001 0.1846 -0.201 0.5263 0.2341 0.083 -0.114 0.0567 -0.359 0.3822 0.3289 0.0244 -0.055 -0.195 0.0497 0.2213 5 Do you see any multicollinearity? No 6 Which independent variable has the greatest effect on Sales Price Garage 7 Consider the t Stat and P-value values for each coefficient. Do they suggest that any independent variables have too high of a probability of not affecting the dependent variable in the population? Use a significance level of 0.05. Yes, the T stat and P-value of pool, distance, and township has a high probability of not affecting the dependent variable in the population. Run the regression analysis again, but this time, exclude the independent variables you identified in step 7. You may have to copy the columns you need to another place on the worksheet to put them all next to each other. Also this time, include residual plots and the normal probability plot. Move your residual plots and normal probability plot to the right of the summary output below. es 9&10 are placed 9 How much of the price is determined by the regression equation that contains the remaining independent variable 62.249 10 Examine the residual plots and normal probability plot. Write down any observations that might indicate the underlying ere due to the length of residual plot data assumptions of linear relationships, homoscedasticity, normal distribution of residuals, no multicollinearity. and no autocorrelation might not be true. Answer them after station #