Source df MS F Regression 2 118.8475 59.42375 40.92168993 Residual 13.0692 1.452133333 Total 11 131.9167 a) Complete the remaining entries in the table. e) Test the significance of the overall regression model using a=0.05.
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- The commercial division of a real estate firm is conducting a regression analysis of the relationship between I, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. ANOVA MS Significance F Regression 1 41587.6 Residual Total 51984.7 Coefficients Standard Error t Stat P-value Intercept 20.000 3.2213 6.21 Apnual Gros 7.240 1.3621 5.29 Rents a. How many apartment buildings were in the sample? b. Write the éstimated regression equation (to 2 decimals if necessary). c. Use the t statistic to test the significance of the relationship at a 0.05 level of significance. What is the p-value? Use Table 2 of Appendix D. P-value is-Sclect your answer - What is your condusion? - Select your answer - d. Use the F statistic to test the significance of the relalionship at a 0.06 level uf significanct. Compute the F test slatistic (to 2…please answer 11 and 12 according to other pictureIn the following regression, X = total assets ($ billions), Y = total revenue ($ billions), and n = 64 large banks. R2 0.519 Std. Error 6.977 n 64 ANOVA table Source SS df MS F p-value Regression 3,260.0981 1 3,260.0981 66.97 1.90E-11 Residual 3,018.3339 62 48.6828 Total 6,278.4320 63 Regression output confidence interval variables coefficients std. error t Stat p-value Lower 95% Upper 95% Intercept 6.5763 1.9254 3.416 .0011 2.7275 10.4252 X1 0.0452 0.0055 8.183 1.90E-11 0.0342 0.0563 (a) Write the fitted regression equation. yˆy^ = Not attempted + Not attempted X (b-1) State the degrees of freedom for a two-tailed test for zero slope, and use Appendix D to find the critical value at α = .05. (Round t critical value to 3 decimal places.) Degrees of freedom Not attempted tcrit ± Not attempted…
- If a regression model of the form y= B,+B,x,+... + B,x, is fit to 132 observations on each variable and yields an R´value of 0.87, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Source of Degrees of freedom Sums of Mean F statistic variation squares squares Regression 69 Error TotalA 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 US states. What is the R2 for this regression? Source of variation df ss ms f Regression 1 5377.836 5377.836 5.228879 residual 48 49367.389 1028.487 total 49 54745.225ul Sprint 11:42 AM 86% Done Attachment 8 Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. 1 3 5 7 9 9) y| 143 116 100 98 90 9) A) y = -150.7 + 6.8x B) y = -140.4 + 6.2x C) y = 140.4 – 6.2x D) y = 150.7 – 6.8x
- Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1Data for 34 results are given below: Source SS Regression Residual Total 2,306,075.92 1,217,176.18 3,823,252.10 ABC df 1 32 33 MS 2,306,075.92 47,411.76 Based on the results what do you conclude and why using a = .05? FAn automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
- 12 A regression was run to determine if there is a relationship between hours of study per week (xx) and the final exam scores (yy).The results of the regression were: y=ax+b a=5.531 b=23.45 r2=0.378225 r=0.615 Use this to predict the final exam score of a student who studies 8.5 hours per week, and please round your answer to a whole numbeRewrite the regression model to include coefficients from your regression analysis output and then answer the following question What would be the company's loss if the significant variable(s) change per unit? SUMMARY OUTPUT Regression Statistics Multiple R 0.93082 R Square 0.866425 Adjusted R Square 0.85833 Standard Error 4108.993 Observations 36 ANOVA df SS MS F Significance F Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15 Residual 33 5.57E+08 16883824 Total 35 4.17E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…Which of the following statements is the CORRECT expression in APA format of the SPSS simple linear regression results provided below? Model Summary Adjusted R Square R R Square 817 .668 627 a. Predictors: (Constant), Experience years (year) ANOVA Regression Residual Sum of Squares 92.844 46.118 138.962 df Std. Error of the Estimate 2.401 1 Total a. Dependent Variable: Income (10 million VND/month) b. Predictors: (Constant), Experience years (year) Mean Square 92.844 5.765 Coefficients Unstandardized Coefficients B Std. Error (Constant) -.651 Experience years (year) 7856642.174 37841636.85 a. Dependent Variable: Income (10 million VND/month) .162 Standardized Coefficients Beta -.817 F 16.105 -4.013 .208 Sig. 004 Sig. 004 .841 A regression analysis was conducted with Income as criterion variable and Experience in Years as the predictor. Experience in Years was NOT a significant predictor of Income, 3= -0.82, t(9)= -4.013, p>0.05, and accounted for 66.80% of the variance in Income…