A linear regression model is fitted to the data using the statistical software, R. A summary of that model fit is given below: Coefficients Estimate Std. Error t value Pr( > It|) Intercept 3.42 9.826 0.348 0.73 Homework 0.871 0.1155 7.542 1.37e-08 Residual standard error: 9.493 on 32 degrees of freedom Multiple R-squared: 0.64, Adjusted R-squared: 0.6288 3. Which statement about the coefficient of determination is true? OA. About 80% of variability in quiz grades is explained by the linear association with homework grades. The remaining variation OB. About 62.88% of the variability in homework grades is explained by the linear association with quiz grades. The remaining variation is due to chance. OC. About 64% of the variability in quiz grades is explained by the linear association with homework grades. The remaining variation is due to chance. OD. On average about 80% of quiz grades come from homework. due to chance. 4. Use the information in the software output to write the equation for the regression line for this model: ŷ = 5. Use the information given in the software output to find a 95% confidence interval for the true slope of the linear equation representing the relationship between Homework Grade and Quiz Grade. 6. What are the test statistic and the value for the test of Ho : B1 = 0 versus H, : Bi # 0? Test Statistic: and p value:

Please help interpret this SPSS output of a multiple regression analysis result? DV-  To what extent the product safety incident have changed your perceptions of the involved brand(s) IV'S- Trustworthy , Reliability ,  I don’t know how good the company’s products will be before I buy it. , Preventability

The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, 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. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7   Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample?   (b) Write the estimated regression equation. ŷ =        (c) What is the value of  sb1?   Find the value of the test statistic. (Round your answer to two decimal places.)   Find the p-value. (Round your answer to three decimal places.) p-value =

A regression of infant mortality rates (annual deaths per hundred births) on median annual household income (in thousands of dollars) is performed on data collected from 40 counties. The results are summarized as follows. Intercept: Slope: 18.46 -0.14 Sum of df Mean Squares Square Regression Residual Total 2.4 4.5 (a) Fill in the blanks in the ANOVA table (b) What is the predicted infant mortality rate in a county where the median household income is $40,000? (c) In words, what is the meaning of the coefficient -0.14? Do NOT simply say that this is the slope or the regression coefficient. Indicate what it means and how it can be interpreted.

An 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.

Please assist second part? Thank you!

Rewrite 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…