Curry Rubber manufactures rubber bands for retail compaies. The accounting manager has performed a regression analysis of past data. You notice that the formula has an R-squared of .6, at-valueof 2.3, and a standard error of the estimate of $200,000. The estimate for next quarter costs is $2,584,072. What do these statistics tell you about the reliability and precision of his regression analysis?

Curry Rubber manufactures rubber bands for retail compaies. The accounting manager has performed a regression analysis of past data. You notice that the formula has an R-squared of .6, at-valueof 2.3, and a standard error of the estimate of $200,000. The estimate for next quarter costs is $2,584,072. What do these statistics tell you about the reliability and precision of his regression analysis?

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 34P: A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the...

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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?
A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?
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.
Stock market analysts are continually looking for reliable predictors of stock prices. Consider the problem of modeling the price per share of electric utility stocks (Y). Two variables thought to influence this stock price are return on average equity (X1) and annual dividend rate (X2). The stock price, returns on equity, and dividend rates on a randomly selected day for 16 electric utility stocks are provided in the file P13_15.xlsx. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Also, interpret the standard error of estimate and the R-square value for these data.
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
Management of a home appliance store would like to understand the growth pattern of the monthly sales of Blu-ray disc players over the past two years. Managers have recorded the relevant data in the file P13_33.xlsx. a. Create a scatterplot for these data. Comment on the observed behavior of monthly sales at this store over time. b. Estimate an appropriate regression equation to explain the variation of monthly sales over the given time period. Interpret the estimated regression coefficients. c. Analyze the estimated equations residuals. Do they suggest that the regression equation is adequate? If not, return to part b and revise your equation. Continue to revise the equation until the results are satisfactory.
Curry Rubber manufactures rubber bands for retail companies. The accounting manager has performed a regression analysis of past data. You notice that the formula has an R-squared of .6, at-value of 2.3, and a standard error of the estimate of $200,000. The estimate for next quarter costsis $2,584,072. What do these statistics tell you about the reliability and precision of his regressionanalysis?
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…
1. Problem: If a car driven 50,000 miles, how much would be the maintenance cost? 2. Use Linear Regression equation to find the maintenance cost. Show step by step calculations. 3. Also, calculate r^2 for the above model. Show step by step calculations.
Macomb Inc. used Excel to run a least-squares regression analysis on the production cost data resulting in the following output: Regression StatisticsMultiple R 0.9834 R Square 0.9596 Observations 30 Coefficients Standard Error T Stat P-ValueIntercept 174,980 61,580 2.84 0.021 Production units (X) 11.53 0.9265 12.44 0.000 What total cost would Macomb predict for a month in which production is 2,000 units? Multiple Choice $174,900 $63,433 $198,040 $23,037
A study to determine the correlation between bankdeposits and consumer price indices in Birmingham, Alabama,revealed the following (which was based on n = 5 years of da ta):• LX = 15• Lx2 = 55• Lxy = 70• Ly = 20• L/ = 130a) What is the equation of the least-squares regression line?b) Find the coefficient of correlation. What does it imply to you?c) What is the standard error of the estimate?
1.) Use the following dummy variables to develop an estimated regression equation to account for seasonal effects only in the data. Qtr1 = 1 if Quarter 1, 0otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise, Qtr3 = 1 if Quarter 3, 0otherwise. Based only on the seasonal effects in the data, compute estimates of quarterly sales for year 6.a. Report the estimate of sales for Year 6 Quarter 1. (Enter a whole value.)b. Report the estimate of sales for Year 6 Quarter 4. (Enter a whole value.)2.) Let Period t = 1 refer to the observation in quarter 1 of year 1; Period t = 2refer to the observation in quarter 2 or year 1; … and Period t = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and Period (t), develop an estimated regression equation to account for seasonal effects and any linear trend in the time series. Based upon the seasonal effects in the data and linear trend, compute the estimates of quarterly sales for year 6.a. Report the estimate…