Macomb Inc. used Excel to run a least-squares regression analysis on the production cost data resulting in the following output: Regression Statistics Multiple R 0.9834 R Square 0.9596 Observations 30 Coefficients Standard Error T Stat P-Value Intercept 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

Macomb Inc. used Excel to run a least-squares regression analysis on the production cost data resulting in the following output: Regression Statistics Multiple R 0.9834 R Square 0.9596 Observations 30 Coefficients Standard Error T Stat P-Value Intercept 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

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.3: Simple Regression Models

Problem 6P: Do the sales prices of houses in a given community vary systematically with their sizes (as measured...

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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?
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.
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?
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.
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 3
Your company is preparing an estimate of its production costs for the coming period. The controller estimates that direct materials costs are $45 per unit and that direct labor costs are $23 per hour. Estimating overhead, which is applied on the basis of direct labor costs, is difficult. The controller's office estimated overhead costs at $4,000 for fixed costs and $17 per unit for variable costs. Your colleague, Lance, who graduated from a rival school, has already done the analysis and reports the "correct" cost equation as follows. Overhead = $10,511 + $15.94 per unit Lance also reports that the correlation coefficient for the regression is 0.81 and says, "With 81% of the variation in overhead explained by the equation, it certainly should be adopted as the best basis for estimating costs." When asked for the data used to generate the regression, Lance produces the following: Month LEMASTOSOHN3 1 2 4 5 6 7 8 9 10 11 12 13 Overhead Unit Production $57,064 60,683 77,008 56,624 81,700…
Star, Incorporated, used Excel to run a least-squares regression analysis, which resulted in the following output: Regression Statistics Multiple R R Square Observations 0.9798 0.9560 30 Coefficients Standard Error 60,902 0.9237 Intercept Production (x) How much of the variation in cost is explained by production? 174,302 11.06 T Stat 2.86 11.97 P-Value 0.021 0.000
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…
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?
(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.
The following data describes weekly gross revenue ($1000s), television advertising expenditures ($1000s), and newspaper advertising expenditures ($1000s) for Showtime Movie Theaters. × Weekly Gross + Revenue ($1000s) 99 90 95 92 95 94 94 94 a. Find an estimated regression equation relating weekly gross revenue to television advertising expenditures and newspaper advertising expenditures (to 2 decimals). 2.84 Television Advertising ($1000s) 5.0 2.0 4.0 2.5 3.0 3.5 2.5 3.0 Revenue = 81.73 1.29 News Adv TV Adv + b. Choose the correct plot of the standardized residuals against ĝ. Newspaper Advertising ($1000s) 1.5 2.0 1.5 2.5 3.3 2.3 4.2 2.5
Chart and Regression analysis : What does the intercept predict? X: C16 (number of cars on the sales lot) versus Y: C17 (cars sold per day) Equation: y=2.9x + 14.5 Slope:2.9 Intercept:14.5 Does the intercept mean the intercept is 14.5 means that the cars sold per day( Y) predicted number of cars on sale lot(X) to be 14.5, but this intercept has no meaning. So, I will not use to predict cars sold per day?