Construct a 90% confidence interval about the slope of the true least-squares regression line, for the data below, for the data below, given that y=-1.885x+0.758. Round to three decimal places as needed.
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- The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.
- 8. assignment variable at which the treatment status does NOT change In Regression Discontinuity framework, the cutoff is defined as the level of the True False 9. What regression equation do we use for regression discontinuity framework? a) Y = Bo + B1 T + B2Xu+ Ei b) Y = Bo + B,T+ B2C + ei c) Y, = Bo + B, T+ B2 (Xi-C) + ei d) Y, = Bo + B1T + B2 (Xu + C) + € 10. Regression Discontinuity Design accomplices apples-to-apples comparison by comparing individuals as far from the cutoff as possible. True False(a) Assuming that a simple linear regression model is appropriate, obtain the least squares fit relating selling price to taxes paid. What is the estimate of o²? (b) Find the mean selling price given that the taxes paid are x = 7.50. (c) Calculate the fitted value of y corresponding to x = 5.8980. Find the corresponding residual.Suppose you want to estimate the proportion of cars that are sport utility vehicles (SUVs) being driven in Kansas City, Missouri, at rush hour by standing on the corner of I-70 and I-470 and counting SUVs. You believe the figure is no higher than 0.40. If you want the error of the confidence interval to be no greater than .03, how many cars should you randomly sample? Use a 90% level of confidence. Appendix A Statistical Tables (Round your answer up to the nearest integer.) Sample enter a number of cars rounded to the nearest integer
- Consider the ANOVA table that follows. Analysis of Variance Source DF SS MS F p Regression 2 77.907 38.954 4.14 0.021 Residual Error 62 583.693 9.414 Total 64 661.600 a-1. Determine the standard error of estimate. (Round your answer to 3 decimal places.) a-2. About 95% of the residuals will be between what two values? (Round your answers to 3 decimal places.) b-1. Determine the coefficient of multiple determination. (Round your answer to 3 decimal places.) b-2. Interpret the coefficient of multiple determination. (Round your answer to 1 decimal place.) Determine the coefficient of multiple determination, adjusted for the degrees of freedom. (Round your answer to 3 decimal places.) Prev Question 5 of 30 Total5 of 3Please help with correct answers: step by step: last time submitted- answers were incorrect A statistical program is recommended. The quarterly sales data (number of copies sold) for a college textbook over the past three years follow. Quarter Year 1 Year 2 Year 3 1 1,690 1,800 1,850 2 940 900 1,100 3 2,625 2,900 2,930 4 2,500 2,360 2,615 a) Use a regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. (Round your numerical values to the nearest integer.) Qrt1 = 1 if quarter 1, 0 otherwise; Qrt2 = 1 if quarter 2, 0 otherwise; Qrt3 = 1 if quarter 3, 0 otherwise t = b) Using the equation developed in part (b), compute the quarterly forecasts for year 4. (Round your answers to the nearest ten.) quarter 1 forecast____? quarter 2 forecast____? quarter 3 forecast ____? quarter 4 forecast ____? c) Let t = 1 refer to the observation in Quarter 1 of Year 1; let t = 2 refer to the observation in Quarter 2 of Year…a. Complete the ANOVA table. (Round the MS- and F-values to 4 decimal places.) SOURCE Regression 100 Error Total 30 630 b. How large was the sample? n= c. Determine the standard error of estimate. (Round the final answer to 4 decimal places.) d. Determine the coefficient of determination. (Round the final answer to 2 decimal places.)
- data table below shows the number of computers sold at the Best Buy Store in a week, based on online ads. Online Ad Computers Sold 2 25 1 10 4 30 1 10 2 25 sigma)x=10 sigma)y=100 draw the graph of the regression equation, showing the slope and y intercepts?Bo Using data from 50 workers, a researcher estimates Wage = ẞe + B₁Education + B2Experience + B3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. A portion of the regression results is shown in the following table. Intercept Education Experience Age Coefficients Standard Error t Stat p-Value 7.17 4.26 1.68 0.0991 1.81 0.35 5.17 0.0000 0.45 0.10 4.50 0.0000 -0.01 0.06 -0.17 0.8684 a-1. Interpret the point estimate for ẞ1. As Education increases by 1 year, Wage is predicted to increase by 1.81/hour. As Education increases by 1 year, Wage is predicted to increase by 0.45/hour. As Education increases by 1 year, Wage is predicted to increase by 1.81/hour, holding Age and Experience constant. As Education increases by 1 year, Wage is predicted to increase by 0.45/hour, holding Age and Experience constant. a-2. Interpret the point estimate for $2. As Experience…Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? Interpret the fixed term, b 0b0, if appropriate. Choose the correct answer below. A. It is not appropriate to interpret b 0b0, because its value is the predicted billable hours for overhead costs of 0 dollars, but the firm cannot have overhead costs of 0 dollars associated with a client. B. The value of b 0b0 is the predicted overhead costs for 0 billable hours. C. It is not appropriate to interpret b 0b0, because its value is the predicted overhead costs for 0 billable hours, but someone with 0 billable hours would not actually be a client of the firm. D. For each increase of 1 unit in billable hours, the predicted overhead costs are estimated to increase by b 0b0. E. The value of b 0b0 is the predicted billable hours for an overhead cost of 0 dollars. F. For each increase of 1 unit in…