An auditor used regression analysis to evaluate the relationship between utility costs and machine hours. The following information was developed using a computer software program: Intercept 2,050 Standard error of estimate 200 Regression 0.825 Number of observations 36 Correlation coefficient 0.800 How much is the utility cost if the company’s 10 machines will use 2,400 hours next month? a. ₱3,830 b. ₱3,970 c. ₱4,030 d. ₱4,050

An auditor used regression analysis to evaluate the relationship between utility costs and machine hours. The following information was developed using a computer software program: Intercept 2,050 Standard error of estimate 200 Regression 0.825 Number of observations 36 Correlation coefficient 0.800 How much is the utility cost if the company’s 10 machines will use 2,400 hours next month? a. ₱3,830 b. ₱3,970 c. ₱4,030 d. ₱4,050

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 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?
At the beginning of each week, a machine is in one of four conditions: 1 = excellent; 2 = good; 3 = average; 4 = bad. The weekly revenue earned by a machine in state 1, 2, 3, or 4 is 100, 90, 50, or 10, respectively. After observing the condition of the machine at the beginning of the week, the company has the option, for a cost of 200, of instantaneously replacing the machine with an excellent machine. The quality of the machine deteriorates over time, as shown in the file P10 41.xlsx. Four maintenance policies are under consideration: Policy 1: Never replace a machine. Policy 2: Immediately replace a bad machine. Policy 3: Immediately replace a bad or average machine. Policy 4: Immediately replace a bad, average, or good machine. Simulate each of these policies for 50 weeks (using at least 250 iterations each) to determine the policy that maximizes expected weekly profit. Assume that the machine at the beginning of week 1 is excellent.
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?
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c. Overhead Cost Billable Hours $ 365,000.00 3000 $ 400,000.00 4000 $ 430,000.00 5000 $ 477,000.00 6000 $ 560,000.00 7000 $ 587,000.00 8000 a) Develop a trendline to identify the relationship between billable hours and overhead costs. b) Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? c) If a special job requiring 1000 billable hours that would contribute a margin of $38,000 before overhead was available, would the job be attractive?
Using multiple regression, you have identified P12,000 of unit level costs for 3,000 units, P1,000 of product level costs for 40 products, and P3,500 of customer-level costs for ten customers. The cost of Job 002 which used 800 unit level activities, 4 product level activities, and one customer-level activities amounts to * A. P3,650 B. P3,250 C. P3,050 D. P2,950
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
d. Now based on regression analysis predict the number of patients that Dr. Fok will see in year 11 and 12 based on the forecasted Robbery Rate - year 11 is 117.1 and year 12 is 120.8. e. What is the MAD? f. What is the correlation Coefficient – r-value? % g. Based on the overall analysis, how well does each model fit the data?
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…
Aqua Man Company manufactures and sells a single product. Planned and actual production in its first year of operation was 100,000 units. Planned and actual costs for the year were as follows: Manufacturing Non-manufacturing Variable P600,000 P500,000 Fixed P400,000 P300,000 The company sold 85,000 units of product at a selling price of P30 per unit. Using absorption costing, the company’s operating profit was Select one: a. P1,020,000 b. P975,000 c. P900,000 d. P750,000
All patients admitted to a particular hospital can be released on the same day or kept overnight forobservation. In a sample of 2550 patients, 1020 were kept overnight for observationa) What is the point estimate ?̂for the proportion of all patients kept overnight forobservation? [1]b) Construct a 99% confidence interval for the proportion of all patients kept overnight forobservation.
The following two demand sets are to be used to test two different basic exponential smoothingmodels. The first model uses 0.1, and the second uses 0.5. In both cases, the modelshould be initialized with a beginning forecast value of 50; that is, the ESF forecast for period 1made at the end of period 0 is 50 units. In each of the four cases (two models on two demandsets), compute the average forecast error and MAD. What do the results mean?Demand Set I Demand Set IIPeriod Demand Period Demand1 51 1772 46 2833 49 3904 55 4225 52 5106 47 6807 51 7168 48 8199 56 92710 51 10 7911 45 11 7312 52 12 8813 49 13 1514 48 14 2115 43 15 8516 46 16 2217 55 17 8818 53 18 7519 54 19 1420 49 20 16