Chapter 9, Problem 3.5C

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.

The file P14_01.xlsx contains data on 100 consumers who drink beer. Some of them prefer light beer, and others prefer regular beer. A major beer producer believes that the following variables might be useful in discriminating between these two groups: gender, marital status, annual income level, and age. a. Use logistic regression to classify the consumers on the basis of these explanatory variables. How successful is it? Which variables appear to be most important in the classification? b. Consider a new customer: Male, Married, Income 42,000, Age 47. Use the logistic regression equation to estimate the probability that this customer prefers Regular. How would you classify this person?

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?

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.

In regression, the variable predicted is called the     regression variable     independent variable     dependent variable     predictor

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Sandy James thinks that housing prices have stabilized in the past few months. To convince her boss, she intends to compare current prices with last year's prices. She collects 12 housing prices from the want ads: 125,900 253,000 207,500 146,950 121.450 135,450 175,000 200,000 210,950 166,700 185,000 191,950 She then calculates the mean and standard deviation of the prices she has found. What are these two summary values?

Please read the case and answer the following questions. CompStat (short for COMPuter STATistics or COMParative STATistics) originated in the New York City Police Department (NYPD) in 1994 when William Bratton was police commissioner. CompStat is a comprehensive, city-wide database that records all reported crimes or complaints, arrests, and summonses issued in each of the city’s 76 precincts. City officials had previously believed that crime could not be prevented by better information and analytical tools but instead by using more foot patrols in neighborhoods along with the concept of “community policing” in which efforts were made to strengthen the involvement of community groups. In contrast, Bratton and Rudy Giuliani, then the mayor of New York City, believed that. police could be more effective in reducing crime if operational decisions took place at the precinct level and if decision makers had better information. Precinct commanders were in a better position than police…

List down the most important types of probability distributions used in quality management?