D8 A 1 SUMMARY OUTPUT 2 3 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 9 10 ANOVA 11 12 Regression 13 Residual 14 Total 15 16 17 Intercept 18 19 20 21 22 23 24 25 26 27 28 29 30 21 X B Regression Statistics C 0.18393 CORRELATION COEFFICIENT 0.03383 COEFFICIENT OF DETERMINATION 0.022725 40.83543 89 SS df fx 1 87 88 Coefficients 55.43024 1765 -0.00317 D Standard Error E Styles F MS F ignificance F 5079.773861 5079.774 3.046283 0.084453 145075.2823 1667.532 150155.0562 t Stat P-value Lower 95% Upper 95%ower 95.0%pper 95.0% 69.4215 7.039246486 7.874456 8.9E-12 41.43897 69.4215 41.43897 -0.00677 0.000439 -0.00677 0.000439 0.001814051 -1.74536 0.084453 G H J
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- The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…A study investigated the link between speed of an automobile at the time of a crash, and the cost of the insurance claim. Below is the residual plot and the output from Excel.SUMMARY OUTPUT Regression Statistics Multiple R 0.17652 R Square 0.03116 Adjusted R Square 0.03116 Standard Error 0.42014 Observations 225 ANOVA df SS MS F Significance F Regression 1 1.266 1.266 7.1717 0.00795 Residual 223 39.363 0.1765 Total 224 40.629 Coefficients Standard Error t-Stat P-value Lower 95% Upper 95% Intercept 0.843 2.2068 0.382 0.703 -3.5066 5.1926 X Variable 1 0.003807 0.001422 2.678 0.00795 0.001 0.0066 Are there any assumptions for regression that you feel ought to be examined? The residuals to do not average zero since the middle is about 50Normality because there are a lot of outliers on the low end Linearity because of the rainbow type curveIndependence because there are more high speed crashes than low speed crashesConstant Variance because some…
- A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: Regression Statistics Multiple R 0.8479 R Square 0.7189 Adjusted R Square 0.7069 Standard Error 17.5571 Observations 50 ANOVA df SS MS F Significance F Regression 370443.3236 18521.662 0.0000 Residual 14487.7627 308.2503 Total 49 51531.0863 Coefficients Standard Error t Stat P-value Intercept -5.5146 7.2273 -0.763 0.4493 Income 0.4262 0.0392 10.8668 0.0000 Size 5.5437 1.6949 3.2708 0.00020 a. What is the population model of this regression problem?b. What is the sample estimates of the regression problem?c. Which of the independent variables in the model are significant at the 5% level?…SUMMARY OUTPUT Regression Statistics Multiple R 0.664798 R Square 0.441957 Adjusted R Square 0.376305 Standard Error 6.412199 Observations 20 ANOVA df SS MS F Significance F Regression 2 553.5729 276.7864 6.731793 0.007025498 Residual 17 698.9771 41.1163 Total 19 1252.55 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 26.6651 13.92768 1.91454 0.072535 -2.71974085 56.04995 X1 4.00929 1.1224 3.572068 0.002347 1.641232912 6.377348 X2 0.810165 0.477768 1.69573 0.108172 -0.19783686 1.818168 a. What can you say about the strength of this relationship for the model using the F test? Use α = .05. b. Is y significantly related to each independent variable? Use α = .05. c. Would your answer to b change if α = .001? If so, how? (3+4+3)A researcher’s results are shown below using Femlab (labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 U.S. states. Regression Statistics Multiple R 0.313422848 R Square 0.098233882 Adjusted R Square 0.079447088 Standard Error 32.07003698 Observations 50 Variable Coefficients Standard Error t Stat Intercept 343.619889 61.0823514 5.62552 Femlab –2.2833659 0.99855319 –2.28667 Which statement is valid regarding the relationship between Femlab and Cancer? Multiple Choice At the .05 level of significance, there isn’t enough evidence to say the two variables are related. This model explains about 10 percent of the variation in state cancer rates. If your sister starts working, the cancer rate in your state will decline. A rise in female labor participation rate will cause the cancer rate to decrease within a state.
- A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 1 of 2: How many independent variables are included in the regression modelA business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…
- A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…A linear regression was performed on a bivariate data set with variables x and y. Analysis by a computer software package included the following outputs: Sample Size: n=15 Regression Equation: y hate =0.359 - 1.264x Coefficient of Determination: r square = 0.915 Sums of Squares :SSy = 35.617. SSex = 32.589, SSresid = 3.028 a. Calculate the standard error Se. b. write a sentence interpreting the value of rsquare. c.What is the value of Pearson's correlation coefficient? d. Determine whether the variables x and y are significant using a 5% significance level. You may assume a simple random sample from a bivariate normal populaton.A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 20 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 2 of 2: Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?