Solution Manual For Business Statistics A First Course, Canadian Edition, 2nd edition Norean R. Sharpe Richard D. De Veaux Paul F. Velleman Jonathan Berkowitz ______________________________________________________________________________ Chapters 1-16
Chapter 1 – Statistics, Data & Decisions 1.
The news. Answers will vary.
2.
TFSA. Who—who was actually sampled: Canadian residents; What—what is being measured: TFSA participation rates (in percent) and amounts of contributions (in dollars); When—each year from 2009; Where—Canada; Why—to assess the usefulness of this investment option as one of many government programs for retirement planning; How—not specified; Variables—participation (Yes or No) which is categorical, and amount of contribution ($) which is quantitative; Concerns—how will the information be collected; from an audit of government records or with a polling company study?
3.
Oil spills. The description of the study must be broken down into its components in order to understand the study. Who—tankers having recent oil spills; What—date, spillage amount (no specified unit), and cause of puncture; When—recent years; Where—not specified; Why—not specified, but probably to determine whether or not spillage amount per oil spill has decreased as a result of improvements in new tanker design; How—not specified, although it is mentioned that the data are online; Variables—there are three variables: the date, the spillage amount which is quantitative, and the cause of the puncture which is categorical; Concerns—more detail needed on the specifics of the study.
4.
Sales. The description of the study must be broken down into its components in order to understand the study. Who—months at a major Canadian company; What—money spent on advertising ($ thousand) and sales ($ million); When—monthly for the past three years; Where—Canada (assumed); Why—to compare money spent on advertising to sales; How—not specified; Variables—there are three variables: the date; the amount of money spent on advertising, which is quantitative; and sales, which is quantitative.
5.
Food store. Who—existing stores; What—weekly sales ($), town population (thousands), median age of town (years), median income of town ($), and whether or not the stores sell beer/wine; When—not specified; Where—Canada (assumed); Why—the food retailer is interested in understanding if there is an association amongst these variables to help determine where to open the next store; How—data collected from their stores; Variables—sales ($), town population (thousands), median age of town (years), median income of town($), which are all quantitative. Whether or not the stores sell beer/wine is categorical.
6.
Sales, part 2. Who—quarterly data from a major U.S. company; What—quarterly sales ($ million), unemployment rate (%), inflation rate (%); When—quarterly for the past three years; Where—Canada; Why—to determine how sales are affected by the unemployment rate and inflation rate; How—not specified; Variables—quarterly sales ($ million), unemployment rate (%), and inflation rate (%) all of which are quantitative.
7.
Subway menu. Who—Subway sandwiches; What—type of meat, number of calories (in calories), and serving size (in ounces); When—not specified; Where—Subway restaurants; Why—assess the nutritional value of the different sandwiches; How—information gathered on each of the sandwiches offered on the menu; Variables— the number of calories and serving size (grams), which are quantitative; and the type of meat, which is categorical.
8.
MBA admissions. Who—MBA applicants; What—sex, age, whether or not accepted, whether or not they attended, and the reasons for not attending (if they did not accept); When—not specified; Where—a business