8. Suppose the population is normally distributed. Specify the appropriate hypothesis test anddetermine the appropriate critical z or t point(s) for each of the following tests concerning thepopulation mean :(a) upper-tailed test: a - 0.025, n - 25,_ - 3.0.(b) lower-tailed test: a - 0.05, n - 30, s - 9.0.(c) two-tailed test: a - 0.02, n - 5I, s - 6.5.(d) two-tailed test: a - 0.10, n - 36, - 3.8.

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Answered: 8. Suppose the population is normally…

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?
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.
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.
An antique collector believes that the price received for a particular item increases with its age and with the number of bidders. The file P13_14.xlsx contains data on these three variables for 32 recently auctioned comparable items. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Is the antique collector correct in believing that the price received for the item increases with its age and with the number of bidders? Interpret the standard error of estimate and the R-square value for these data.
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.
An advertising executive claims that there is a difference in the mean household income for credit cardholders of Visa Gold and of MasterCard Gold. A random survey of 15 Visa Gold cardholders resulted in a mean household income of $⁢68,420 with a standard deviation of $⁢9100. A random survey of 6 MasterCard Gold cardholders resulted in a mean household income of $⁢59,200 with a standard deviation of $⁢9200. Is there enough evidence to support the executive's claim? Let μ1 be the true mean household income for Visa Gold cardholders and μ2 be the true mean household income for MasterCard Gold cardholders. Use a significance level of α=0.05 for the test. Assume that the population variances are not equal and that the two populations are normally distributed. Step 1 of 4: State the null and alternative hypotheses for the test. Step 2 of 4: Compute the value of the t test statistic. Round your answer to three decimal places. Step 3 of 4: Determine the decision rule for rejecting the…
Given two independent random samples with the following results: n1=441 x1=185 n2=560 x2=351 Can it be concluded that there is a difference between the two population proportions? Use a significance level of α=0.05 for the test. Step 1 of 6: State the null and alternative hypotheses for the test. Step 2 of 6: Find the values of the two sample proportions, p^1 and p^2. Round your answers to three decimal places. Step 3 of 6: Compute the weighted estimate of p,p‾. Round your answer to three decimal places. Step 4 of 6: Compute the value of the test statistic. Round your answer to two decimal places. Step 5 of 6: Determine the decision rule for rejecting the null hypothesis H0. Round the numerical portion of your answer to two decimal places. Reject H0 if ...... ...... ...... Step 6 of 6: Make the decision for the hypothesis test.
(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.
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
7. The following multiple-regression model was developed to predict job performance as measured by a company job performance evaluation index based on a preemployment test score and college grade point average (GPA): y=35+20x1+50x2, where y=job performance evaluation index, x1=preemployment test score, and x2=college GPA. Part 2 a) For an applicant who had a 3.0 GPA and scored 80 on the preemployment test, the forecast for the job performance index= __________ (enter your response as a whole number).

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