Based on the following regression ANOVA table, how many predictors are studied? Source df SS MS F Regression 4 1793.2356 448.3089 7.4854 Residual 45 2695.0996 59.8911 Total 49 4488.3352
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Based on the following regression ANOVA table, how many predictors are studied?
| Source | df | SS | MS | F |
| Regression | 4 | 1793.2356 | 448.3089 | 7.4854 |
| Residual | 45 | 2695.0996 | 59.8911 | |
| Total | 49 | 4488.3352 |
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- Under what conditions might a firm use multiple forecasting methods?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.
- 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 file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.What are the various Assumptions of Distributed Lag regression Model? Explain in detail?
- Develop Linear Regression Model. Forecast sales for 8, 14 and 17 years of education. Sales(1000s) Years of Education 24 10 18 9 20 11 22 6 26 13 23 16 21 18A survey of 40 retail store reported the following amount of sales (in thousand) held in the month of September. Number of Sales Frequency 10-14 3 15-19 5 20-24 12 25-29 9 30-34 8 35-39 3 What is the standard deviation? What is the sample variance?Calculate the mean and standard deviation for the following distribution Class 1 2 5 6-10 10-20 20-30 30-50 50-70 70-100 f 3 4 10 23 20 20 15 3 2
- Based on the following regression ANOVA table, what is R2? Source df SS MS F Regression 4 1793.2356 448.3089 7.4854 Residual 45 2695.0996 59.8911 Total 49 4488.3352 0.6005 0.3995 0.8807 0.7230year quarterly sales (000 units) Q1 Q2 Q3 Q4 2016 1300 1500 1200 2000 2017 1600 1800 1100 2200 2018 1700 1900 1300 2300 2019 1800 2100 1400 2500 Using a simple regression analysis, determine the trend equation of the sales and use it to estimate the number of units of clothing sold throughout the fiscal year 2020. Assume that Q1 of 2016 is 1, Q2 of 2016 is 2, etc. Show all relevant cakculation detailThe following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females. Regression Analysis: Height Versus Shoe Size, Gender Coefficients Term Coef Constant 55.28 SE Coef 1.04 T-Value P-Value Shoe Size 0.105 Gender 0.268 0.12 0.489 53.1 0.875 0.000 0.000 0.548 0.000 (a) The dependent variable in this regression is which of the following? height gender shoe size constant (b) What is the regression coefficient of shoe size? (c) What is the regression coefficient of gender?
