Two independent samples are drawn from two populations, and the following information is provided. Population 1 Population 2 n 34 52 sample mean 550 650 population sigma 100 120 We want to test the following hypotheses: H0: m1 – m2 ≥ 0 Ha: m1 – m2 < 0 At 95% confidence, test the hypotheses. Assume the two populations are normally distributed and have equal variances. P value=0.284 Do Not Reject Null hypothesis, population proportion 1 is more than population proportion 2 P value=0.045 Do not Reject Null hypothesis, population proportion 1 is not more than population proportion 2 P value=0.36 Do not Reject Null hypothesis, population proportion 1 is not more than population proportion 2 P value=0 Reject Null hypothesis, population mean 1 is not more than population mean 2 P_Bar: z: P-Value: Hyphotheses Mean

Two independent samples are drawn from two populations, and the following information is provided. Population 1 Population 2 n 34 52 sample mean 550 650 population sigma 100 120 We want to test the following hypotheses: H0: m1 – m2 ≥ 0 Ha: m1 – m2 < 0 At 95% confidence, test the hypotheses. Assume the two populations are normally distributed and have equal variances. P value=0.284 Do Not Reject Null hypothesis, population proportion 1 is more than population proportion 2 P value=0.045 Do not Reject Null hypothesis, population proportion 1 is not more than population proportion 2 P value=0.36 Do not Reject Null hypothesis, population proportion 1 is not more than population proportion 2 P value=0 Reject Null hypothesis, population mean 1 is not more than population mean 2 P_Bar: z: P-Value: Hyphotheses Mean

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 trucking company wants to predict the yearly maintenance expense (Y) for a truck using the number of miles driven during the year (X1) and the age of the truck (X2, in years) at the beginning of the year. The company has gathered the data given in the file P13_13.xlsx. Note that each observation corresponds to a particular truck. 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.
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.
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.
Use Excels functions (not @RISK) to generate 1000 random numbers from a normal distribution with mean 100 and standard deviation 10. Then freeze these random numbers. a. Calculate the mean and standard deviation of these random numbers. Are they approximately what you would expect? b. What fraction of these random numbers are within k standard deviations of the mean? Answer for k = 1; for k = 2; for k = 3. Are the answers close to what they should be (about 68% for k = 1, about 95% for k = 2, and over 99% for k = 3)? c. Create a histogram of the random numbers using about 10 bins of your choice. Does this histogram have approximately the shape you would expect?
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.
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?
To better understand the relationship between one's region of US residency (South or North) and their voting behavior (voted or not) during the latest mid-term election, Student A collected a random sample of 4,960 individuals. Student A hypothesized that the region of US residency and voting behavior are related in the population. They further selected the Chi-square test and alpha = .05. Voted? Total South North Yes 1002 1635 2637 No 1458 865 2323 Total 2460 2500 4960 1) What is the null hypothesis? (10 Points) 2) Calculate the expected frequency for those who live in the North but did not vote. Please include the formula and the step with the numbers. (10 Points) fe 303.02. for Student A. 3) Student A conducted a Chi-Square test and got an answer of x²-Σ (fo¯fe)² Please look up the Chi-square table in the Appendix and make a Please clearly identify the p-value associated with the Chi-square score and state your conclusion. (10 Points) 4) Student B found an error in Student A's…
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.
You may need to use the appropriate appendix table or technology to answer this question. The following results come from two independent random samples taken of two populations. Sample 1 Sample 2 n1 = 60 n2 = 25 x1 = 13.6 x2 = 11.6 σ1 = 2.5 σ2 = 3 (a) What is the point estimate of the difference between the two population means? (Use x1 − x2.) (b) Provide a 90% confidence interval for the difference between the two population means. (Use x1 − x2. Round your answers to two decimal places.) to (c) Provide a 95% confidence interval for the difference between the two population means. (Use x1 − x2. Round your answers to two decimal places.) to
In a sample of 103 bags of dog food, the average weight was found to be 21.5⁢lb with a standard deviation of 0.5. Give a point estimate for the population variance of the weight of the bags of dog food. Round your answer to two decimal places, if necessary.

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		P value=0.284 Do Not Reject Null hypothesis, population proportion 1 is more than population proportion 2
		P value=0.045 Do not Reject Null hypothesis, population proportion 1 is not more than population proportion 2
		P value=0.36 Do not Reject Null hypothesis, population proportion 1 is not more than population proportion 2
		P value=0 Reject Null hypothesis, population mean 1 is not more than population mean 2