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
, school in Canada; Why—the researchers wanted to investigate any patterns in female student acceptance and
attendance in the MBA program; How—data obtained from the admissions office; Variables—sex, whether or
not the students accepted, whether or not they attended, and the reasons for not attending if they did not accept
(all categorical) and age (years), which is quantitative.
9. Climate. Who—385 species of flowers; What—date of first flowering (in days); When—data gathered over the
course of 47 years; Where—southern England; Why—the researchers wanted to investigate if the first flowering
is indicating a warming of the overall climate; How—not specified; Variables—date of first flowering is a
quantitative variable; Concerns—date of first flowering should be measured in days from January 1 to address
leap year issues.
10. MBA admissions, part 2. Who—MBA students; What—each student’s standardized test scores and GPA in
the MBA program; When—the past five years; Where—London; Why—to investigate the association between
standardized test scores and performance in the MBA program over five years; How—not specified;
Variables—standardized test scores and GPA, which are both quantitative variables.
11. Schools. Who—students; What—age (years or years and months), number of days absent, grade level, reading
score, math score, and any disabilities/special needs; When—ongoing and current; Where—a Canadian
province; Why—keeping this information is a provincial requirement; How—data collected and stored as part
of school records; Variables—there are six variables. Grade level, and disabilities/special needs are categorical
variables. Number of absences, age (years or years and months), reading scores, and math scores are
quantitative variables; Concerns—what tests are used to measure reading and math ability and what are the
units of measurement?
12. Pharmaceutical firm. Who—experimental participants; What—herbal cold remedy or sugar solution, and cold
severity; When—not specified; Where—major pharmaceutical firm; Why—scientists were testing the
effectiveness of an herbal compound on the severity of the common cold; How—scientists conducted a
controlled experiment; Variables—there are two variables. Type of treatment (herbal or sugar solution) is
categorical, and severity rating is quantitative; Concerns—the severity of a cold might be difficult to quantify
(beneficial to add actual observations and measurements, such as body temperature). Also, scientists at a
pharmaceutical firm could have a predisposed opinion about the herbal solution or may feel pressure to report
negative findings about the herbal product.
13. Start-up company. Who—customers of a start-up company; What—customer name, ID number, region of the
country, date of last purchased, amount of purchase ($), and item purchased; When—present day; Where—
Canada (assumed); Why—the company is building a database of customers and sales information; How—
assumed that the company records the needed information from each new customer; Variables—there are six
variables: name, ID number, region of the country, and item purchased are categorical, and date and amount of
purchase ($) are quantitative; Concerns—although region is coded as a number, it is still a categorical variable.
14. Cars. Who—cars parked in executive and staff lots at a large company; What—make, country of origin, type of
vehicle (car, van, SUV, etc.), and age of vehicle (probably in years); When—not specified; Where—a large
company; Why—not specified; How—data recorded in executive and staff lots of a large company; Variables—
make, country of origin, and type of vehicle are categorical variables. Age is the single quantitative variable.
Whether or not the vehicle is in an executive or staff lot is also a categorical variable.
15. Vineyards. Who—vineyards; What—size (hectares), number of years in existence, province, varieties of grapes
grown, average case price ($), gross sales ($), and percent profit; When—not specified; Where—assume
Canada since province is recorded; Why—business analysts hope to provide information that would be helpful
to grape growers in Canada; How—not specified; Variables—size of vineyard (hectares), number of years in
existence, average case price ($), gross sales ($), and percent profit are quantitative variables. Province and
variety of grapes grown are categorical variables.
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
, school in Canada; Why—the researchers wanted to investigate any patterns in female student acceptance and
attendance in the MBA program; How—data obtained from the admissions office; Variables—sex, whether or
not the students accepted, whether or not they attended, and the reasons for not attending if they did not accept
(all categorical) and age (years), which is quantitative.
9. Climate. Who—385 species of flowers; What—date of first flowering (in days); When—data gathered over the
course of 47 years; Where—southern England; Why—the researchers wanted to investigate if the first flowering
is indicating a warming of the overall climate; How—not specified; Variables—date of first flowering is a
quantitative variable; Concerns—date of first flowering should be measured in days from January 1 to address
leap year issues.
10. MBA admissions, part 2. Who—MBA students; What—each student’s standardized test scores and GPA in
the MBA program; When—the past five years; Where—London; Why—to investigate the association between
standardized test scores and performance in the MBA program over five years; How—not specified;
Variables—standardized test scores and GPA, which are both quantitative variables.
11. Schools. Who—students; What—age (years or years and months), number of days absent, grade level, reading
score, math score, and any disabilities/special needs; When—ongoing and current; Where—a Canadian
province; Why—keeping this information is a provincial requirement; How—data collected and stored as part
of school records; Variables—there are six variables. Grade level, and disabilities/special needs are categorical
variables. Number of absences, age (years or years and months), reading scores, and math scores are
quantitative variables; Concerns—what tests are used to measure reading and math ability and what are the
units of measurement?
12. Pharmaceutical firm. Who—experimental participants; What—herbal cold remedy or sugar solution, and cold
severity; When—not specified; Where—major pharmaceutical firm; Why—scientists were testing the
effectiveness of an herbal compound on the severity of the common cold; How—scientists conducted a
controlled experiment; Variables—there are two variables. Type of treatment (herbal or sugar solution) is
categorical, and severity rating is quantitative; Concerns—the severity of a cold might be difficult to quantify
(beneficial to add actual observations and measurements, such as body temperature). Also, scientists at a
pharmaceutical firm could have a predisposed opinion about the herbal solution or may feel pressure to report
negative findings about the herbal product.
13. Start-up company. Who—customers of a start-up company; What—customer name, ID number, region of the
country, date of last purchased, amount of purchase ($), and item purchased; When—present day; Where—
Canada (assumed); Why—the company is building a database of customers and sales information; How—
assumed that the company records the needed information from each new customer; Variables—there are six
variables: name, ID number, region of the country, and item purchased are categorical, and date and amount of
purchase ($) are quantitative; Concerns—although region is coded as a number, it is still a categorical variable.
14. Cars. Who—cars parked in executive and staff lots at a large company; What—make, country of origin, type of
vehicle (car, van, SUV, etc.), and age of vehicle (probably in years); When—not specified; Where—a large
company; Why—not specified; How—data recorded in executive and staff lots of a large company; Variables—
make, country of origin, and type of vehicle are categorical variables. Age is the single quantitative variable.
Whether or not the vehicle is in an executive or staff lot is also a categorical variable.
15. Vineyards. Who—vineyards; What—size (hectares), number of years in existence, province, varieties of grapes
grown, average case price ($), gross sales ($), and percent profit; When—not specified; Where—assume
Canada since province is recorded; Why—business analysts hope to provide information that would be helpful
to grape growers in Canada; How—not specified; Variables—size of vineyard (hectares), number of years in
existence, average case price ($), gross sales ($), and percent profit are quantitative variables. Province and
variety of grapes grown are categorical variables.
