Chapter 1 – Data and Decisions
SECTION EXERCISES
SECTION 1.1
1. a) Each row represents a different house that was recently sold. It can be described as a case.
b) There are six quantitative variables in each row plus a house identifier.
2. a) Each row represents a different transaction (not customer or book). It can be described as a case.
b) There are six quantitative variables plus two identifiers in each row.
SECTION 1.2
3. a) House_ID is an identifier (special type of categorical, not ordinal); Neighborhood is categorical
(nominal); Mail_ZIP is categorical (nominal – ordinal in a sense, but only on a national level); Acres is
quantitative (units – acres); Yr_Built is quantitative (units – year); Full_Market_Value is quantitative
(units – dollars); Size is quantitative (units – sq. ft.).
b) These data are cross-sectional. Each row corresponds to a house that recently sold so at approximately
the same fixed point in time.
4. a) Transaction ID is an identifier (categorical, nominal, not ordinal); Customer ID is an identifier
(categorical, nominal); Date can be treated as quantitative (how many days since the transaction took place,
days since Jan. 1 2012, for example) or categorical (as month, for example); ISBN is an identifier
(categorical, nominal); Price is quantitative (units – dollars); Coupon is categorical (nominal); Gift is
categorical (nominal); Quantity is quantitative (unit – counts).
b) These data are cross-sectional. Each row corresponds to a transaction at a fixed point in time. However
the date of the transaction has been recorded. Consequently, since a time variable is included the data
could be reconfigured as a time series.
SECTION 1.3
5. It is not specified whether or not the real estate data of Exercise 1 are obtained from a survey. The data
would not be from an experiment, a data gathering method with specific requirements. Rather, the real
estate major’s data set was derived from transactional data (on local home sales). The major concern with
drawing conclusions from this data set is that we cannot be sure that the sample is representative of the
population of interest (e.g., all recent local home sales or even all recent national home sales). Therefore,
we should be cautious about drawing conclusions from these data about the housing market in general.
6. The student is using a secondary data source (from the Internet). The main concerns about using these data
for drawing conclusions is that the data were collected for a different purpose (not necessarily for
developing a stock investment strategy) and information about how, when, where and why these data were
collected may not be available. In addition, the companies may not be representative of companies in
general. Therefore, the student should be cautious about using this type of data to predict performance in
the future.
CHAPTER EXERCISES
7. The news. Answers will vary.
8. The Internet. Answers will vary.
9. Survey. The description of the study has to be broken down into its components in order to understand the
study. Who– who or what was actually sampled–college students; What–what is being measured–opinion of
electric vehicles: whether there will more electric or gasoline powered vehicles in 2025 and the likelihood
of whether they would purchase an electric vehicle in the next 10 years; When–current; Where–your
location; Why–automobile manufacturer wants college student opinions; How–how was the study
conducted–survey; Variables–what is the variable being measured–there is one categorical variable–what
1-1
Copyright © 2015 Pearson Education, Inc.
,1-2 Chapter 1 Data and Decisions
students think about whether or not there will be more electric or gasoline powered vehicles in 2025 and
one ordinal variable–how likely, using a scale, would the student be to buy an electric vehicle in the next 10
years; Source –the data are not from a designed survey or experiment; Type–the data are cross-sectional;
Concerns–none.
10. Your survey. Answers will vary.
11. World databank. Answers will vary but chosen from the following possible indicators:
• GDP growth (annual %)
• GDP (current US$)
• GDP per capita (current US$)
• GNI per capita, Atlas method (current US$)
• Exports of goods and services (% of GDP)
• Foreign direct investment, net inflows (BoP, current US$)
• GNI per capita, PPP (current international $)
• GINI index
• Inflation, consumer prices (annual %)
• Population, total
• Life expectancy at birth, total (years)
• Internet users (per 100 people)
• Imports of goods and services (% of GDP)
• Unemployment, total (% of total labor force)
• Agriculture, value added (% of GDP)
• CO2 emissions (metric tons per capita)
• Literacy rate, adult total (% of people ages 15 and above)
• Central government debt, total (% of GDP)
• Inflation, GDP deflator (annual %)
• Poverty headcount ratio at national poverty line (% of population)
12. Arby’s menu. Who–Arby’s sandwiches; What–type of meat, number of calories (in calories), and serving
size (in ounces); When–not specified; Where–Arby’s restaurants; Why–assess the nutritional value of the
different sandwiches; How–information gathered on each of the sandwiches offered on the menu;
Variables–there are 3 variables: the number of calories and serving size are quantitative, and the type of
meat is categorical; Source–data are not from a designed survey or experiment; Type–data are cross-
sectional; Concerns–none.
13. MBA admissions. Who–MBA applicants (in France); 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
school in the northeastern United States; 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–there are 5 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 which is quantitative;
Source–data are not from a designed survey or experiment; Type–data are cross-sectional; Concerns–
none.
14. MBA admissions II. Who–MBA students (in Northeast); What–each student’s standardized test scores and
GPA in the MBA program; When–2009 to 2014; Where–outside of Paris; Why–to investigate the
association between standardized test scores and performance in the MBA program over five years (2009–
2014); How–not specified; Variables–there are 2 quantitative variables: standardized test scores and GPA;
Source–data are not from a designed survey or experiment, data are available from student records; Type–
although the data are collected over 5 years, the purpose is to examine them as cross-sectional rather than
as time-series; Concerns–none.
Copyright © 2015 Pearson Education, Inc.
, Chapter 1 Data and Decisions 1-3
15. Pharmaceutical firm. Who–experimental volunteers; 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 2 variables: type of treatment (herbal or sugar solution) is
categorical, and severity rating is quantitative; Source – data come from an experiment; Type–data are
cross-sectional; 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.
16. Start-up company. Who–customers of a start-up company; What–customer name, ID number, region of
the country, date of last purchase, amount of purchase ($), and item purchased; When–present day; Where–
not specified; 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 6 variables:
name, ID number, region of the country, and item purchased which are categorical and date and amount of
purchase are quantitative; Source–data are not from a designed survey or experiment; Type–data are cross-
sectional; Concerns–although region is coded as a number, it is still a categorical variable.
17. Vineyards. Who–vineyards; What–size of vineyard (acres), number of years in existence, state, varieties of
grapes grown, average case price ($), gross sales ($), and percent profit; When–not specified; Where–not
specified; Why–business analysts hope to provide information that would be helpful to producers of
U.S. wines; How–not specified; Variables–there are 5 quantitative variables: the size of vineyard (acres),
number of years in existence, average case price ($), gross sales ($); there are 2 categorical variables: state
and variety of grapes grown; Source–data come from a designed survey; Type–data are cross-
sectional; Concerns–none.
18. Spectrem group polls. Who–not completely clear. Probably a sample of affluent and retired people; What–
pet preference, number of pets, services and products bought for pets (from a list); When–not specified;
Where–United States; Why–provide services for the affluent; How–survey; Variables–there are 3
categorical variables: pet preference, list of pets and list of services and products bought for pet; Source–
data from a designed survey; Type–data are cross-sectional; Concerns–none.
19. EPA. Who–every model of automobile in the United States; What–vehicle manufacturer, vehicle type (car,
SUV, etc.), weight (probably pounds), horsepower (units of horsepower), and gas mileage (miles per
gallon) for city and highway driving; When–the information is currently collected; Where–United States;
Why–the EPA uses the information to track fuel economy of vehicles; How– among the data EPA analysts
collect from the automobile manufacturers are the name of the manufacturer (Ford, Toyota, etc.), vehicle
type….”; Variables–there are 6 variables: vehicle manufacturer and vehicle type are categorical variables;
weight, horsepower, and gas mileage for both city and highway driving are quantitative variables; Source–
data are not from a designed survey or experiment; Type–data are cross-sectional; Concerns–none.
20. Consumer Reports. Who–46 models of smart phones; What–brand, price (probably dollars), display size
(probably inches) operating system, camera image size (megapixels), and memory card slot (yes/no);
When–2013; Where–United States; Why–the information was compiled to provide information to
readers of Consumer Reports; How–not specified; Variables–– there are a total of 6 variables: price,
display size and image size are quantitative variables; brand and operating system are categorical variables,
and memory card slot is a nominal variable; Source–not specified; Type–the data are cross-sectional;
Concerns–this many or may not be a representative sample of smart phones, or includes all of them, we
don’t know. This is a rapidly changing market, so their data are at best a snapshot of the state of the market
at this time.
21. Zagat. Who–restaurants; What–% of customers liking restaurant, average meal cost ($), food rating (0-30),
decor rating (0-30), service rating (0-30); When–current; Where–United States; Why–service to provide
information for consumers; How–not specified; Variables–there are 5 variables: % liking and average cost
Copyright © 2015 Pearson Education, Inc.
, 1-4 Chapter 1 Data and Decisions
are quantitative variables; ratings (food, decor and service) are ordered categories, therefore, ordinal
variables; Source–not specified; Type–the data are cross-sectional.
22. L.L. Bean. Who–catalog mailings; What–number of catalogs mailed out, square inches in catalog, and sales
($ million) in 4 weeks following mailing; When–current; Where–L.L. Bean (United States); Why–to
investigate association among catalog characteristics, timing, and sales; How–collection of internal data;
Variables–there are 3 variables: number of catalogs, square inches in catalog, and sales are all quantitative
variables; Source–not specified; Type–data are cross-sectional; Concerns–none.
23. Stock market. Who–students in an MBA statistics class; What–total personal investment in stock market
($), number of different stocks held, total invested in mutual funds ($), and the name of each mutual fund;
When–not specified; Where–a business school in the northeast US; Why–the information was collected for
use in classroom illustrations; How–an online survey was conducted, participation was probably required
for all members of the class; Variables– there are 4 variables: total personal investment in stock market,
number of different stocks held, total invested in mutual funds are quantitative variables; the name of each
mutual fund is a categorical variable; Source–data come from a designed survey; Type–data are cross-
sectional.
24. Theme park sites. Who–potential theme park locations in Europe; What–country of site, estimated cost
(probably €), potential population size (counts), size of site (probably hectares), whether or not mass
transportation within 5 minutes of site; When–2013; Where–Europe; Why–to present to potential developers
on the feasibility of various sites; How–not specified; Variables–there are 5 variables: country of site and
whether or not mass transportation is within 5 minutes of site are both categorical variables; estimated cost,
potential population size and size of site are quantitative variables; Source–data are not from a designed
survey or experiment; Type–data are cross-sectional.
25. Indy 2012. Who–Indy 500 races; What–year, winner, car model, time (hrs), speed (mph), and car number;
When–1911-2012; Where–Indianapolis, Indiana; Why–examine trends in Indy 500 race winners; How–
official statistics kept for each race every year; Variables–– there are 6 variables: winner, car model, and
car number are categorical variables; year, time and speed are quantitative variables; Source–all race
results; Type–data are time-series; Concerns–none.
26. Kentucky Derby. Who–Kentucky Derby races; What–date, winner, winning margin (in lengths), jockey,
winner’s payoff ($), duration of the race (minutes and seconds), and track conditions; When–1875-2011;
Where–Churchill Downs, Louisville, Kentucky; Why–examine trends in Kentucky Derby winners; How–
official statistics kept for each race every year; Variables–there are 7 variables: winner, winning jockey,
and track conditions are categorical variables; date, winning margin, winner’s payoff, and duration of the
race are quantitative variables (date could be categorical as well); Source–race results; Type–data are time-
series; Concerns–none.
27. Mortgages. Each row represents each individual mortgage loan. Headings of the columns would be:
borrower’s name, mortgage amount.
28. Employee performance. Each row represents each individual employee. Headings of the columns would
be: Employee ID Number (to identify the row instead of name), contract average ($), supervisor’s rating (1-
10), and years with the company.
29. Company performance. Each row represents a week. Headings of the columns would be: week number of
the year (to identify each row), sales prediction ($), sales ($), and difference between predicted sales and
realized sales ($).
30. Command performance. Each row represents a Broadway show. Headings of the columns would be: the
show name (identifies the row), profit or loss ($), number of investors and investment total ($).
Copyright © 2015 Pearson Education, Inc.
SECTION EXERCISES
SECTION 1.1
1. a) Each row represents a different house that was recently sold. It can be described as a case.
b) There are six quantitative variables in each row plus a house identifier.
2. a) Each row represents a different transaction (not customer or book). It can be described as a case.
b) There are six quantitative variables plus two identifiers in each row.
SECTION 1.2
3. a) House_ID is an identifier (special type of categorical, not ordinal); Neighborhood is categorical
(nominal); Mail_ZIP is categorical (nominal – ordinal in a sense, but only on a national level); Acres is
quantitative (units – acres); Yr_Built is quantitative (units – year); Full_Market_Value is quantitative
(units – dollars); Size is quantitative (units – sq. ft.).
b) These data are cross-sectional. Each row corresponds to a house that recently sold so at approximately
the same fixed point in time.
4. a) Transaction ID is an identifier (categorical, nominal, not ordinal); Customer ID is an identifier
(categorical, nominal); Date can be treated as quantitative (how many days since the transaction took place,
days since Jan. 1 2012, for example) or categorical (as month, for example); ISBN is an identifier
(categorical, nominal); Price is quantitative (units – dollars); Coupon is categorical (nominal); Gift is
categorical (nominal); Quantity is quantitative (unit – counts).
b) These data are cross-sectional. Each row corresponds to a transaction at a fixed point in time. However
the date of the transaction has been recorded. Consequently, since a time variable is included the data
could be reconfigured as a time series.
SECTION 1.3
5. It is not specified whether or not the real estate data of Exercise 1 are obtained from a survey. The data
would not be from an experiment, a data gathering method with specific requirements. Rather, the real
estate major’s data set was derived from transactional data (on local home sales). The major concern with
drawing conclusions from this data set is that we cannot be sure that the sample is representative of the
population of interest (e.g., all recent local home sales or even all recent national home sales). Therefore,
we should be cautious about drawing conclusions from these data about the housing market in general.
6. The student is using a secondary data source (from the Internet). The main concerns about using these data
for drawing conclusions is that the data were collected for a different purpose (not necessarily for
developing a stock investment strategy) and information about how, when, where and why these data were
collected may not be available. In addition, the companies may not be representative of companies in
general. Therefore, the student should be cautious about using this type of data to predict performance in
the future.
CHAPTER EXERCISES
7. The news. Answers will vary.
8. The Internet. Answers will vary.
9. Survey. The description of the study has to be broken down into its components in order to understand the
study. Who– who or what was actually sampled–college students; What–what is being measured–opinion of
electric vehicles: whether there will more electric or gasoline powered vehicles in 2025 and the likelihood
of whether they would purchase an electric vehicle in the next 10 years; When–current; Where–your
location; Why–automobile manufacturer wants college student opinions; How–how was the study
conducted–survey; Variables–what is the variable being measured–there is one categorical variable–what
1-1
Copyright © 2015 Pearson Education, Inc.
,1-2 Chapter 1 Data and Decisions
students think about whether or not there will be more electric or gasoline powered vehicles in 2025 and
one ordinal variable–how likely, using a scale, would the student be to buy an electric vehicle in the next 10
years; Source –the data are not from a designed survey or experiment; Type–the data are cross-sectional;
Concerns–none.
10. Your survey. Answers will vary.
11. World databank. Answers will vary but chosen from the following possible indicators:
• GDP growth (annual %)
• GDP (current US$)
• GDP per capita (current US$)
• GNI per capita, Atlas method (current US$)
• Exports of goods and services (% of GDP)
• Foreign direct investment, net inflows (BoP, current US$)
• GNI per capita, PPP (current international $)
• GINI index
• Inflation, consumer prices (annual %)
• Population, total
• Life expectancy at birth, total (years)
• Internet users (per 100 people)
• Imports of goods and services (% of GDP)
• Unemployment, total (% of total labor force)
• Agriculture, value added (% of GDP)
• CO2 emissions (metric tons per capita)
• Literacy rate, adult total (% of people ages 15 and above)
• Central government debt, total (% of GDP)
• Inflation, GDP deflator (annual %)
• Poverty headcount ratio at national poverty line (% of population)
12. Arby’s menu. Who–Arby’s sandwiches; What–type of meat, number of calories (in calories), and serving
size (in ounces); When–not specified; Where–Arby’s restaurants; Why–assess the nutritional value of the
different sandwiches; How–information gathered on each of the sandwiches offered on the menu;
Variables–there are 3 variables: the number of calories and serving size are quantitative, and the type of
meat is categorical; Source–data are not from a designed survey or experiment; Type–data are cross-
sectional; Concerns–none.
13. MBA admissions. Who–MBA applicants (in France); 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
school in the northeastern United States; 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–there are 5 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 which is quantitative;
Source–data are not from a designed survey or experiment; Type–data are cross-sectional; Concerns–
none.
14. MBA admissions II. Who–MBA students (in Northeast); What–each student’s standardized test scores and
GPA in the MBA program; When–2009 to 2014; Where–outside of Paris; Why–to investigate the
association between standardized test scores and performance in the MBA program over five years (2009–
2014); How–not specified; Variables–there are 2 quantitative variables: standardized test scores and GPA;
Source–data are not from a designed survey or experiment, data are available from student records; Type–
although the data are collected over 5 years, the purpose is to examine them as cross-sectional rather than
as time-series; Concerns–none.
Copyright © 2015 Pearson Education, Inc.
, Chapter 1 Data and Decisions 1-3
15. Pharmaceutical firm. Who–experimental volunteers; 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 2 variables: type of treatment (herbal or sugar solution) is
categorical, and severity rating is quantitative; Source – data come from an experiment; Type–data are
cross-sectional; 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.
16. Start-up company. Who–customers of a start-up company; What–customer name, ID number, region of
the country, date of last purchase, amount of purchase ($), and item purchased; When–present day; Where–
not specified; 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 6 variables:
name, ID number, region of the country, and item purchased which are categorical and date and amount of
purchase are quantitative; Source–data are not from a designed survey or experiment; Type–data are cross-
sectional; Concerns–although region is coded as a number, it is still a categorical variable.
17. Vineyards. Who–vineyards; What–size of vineyard (acres), number of years in existence, state, varieties of
grapes grown, average case price ($), gross sales ($), and percent profit; When–not specified; Where–not
specified; Why–business analysts hope to provide information that would be helpful to producers of
U.S. wines; How–not specified; Variables–there are 5 quantitative variables: the size of vineyard (acres),
number of years in existence, average case price ($), gross sales ($); there are 2 categorical variables: state
and variety of grapes grown; Source–data come from a designed survey; Type–data are cross-
sectional; Concerns–none.
18. Spectrem group polls. Who–not completely clear. Probably a sample of affluent and retired people; What–
pet preference, number of pets, services and products bought for pets (from a list); When–not specified;
Where–United States; Why–provide services for the affluent; How–survey; Variables–there are 3
categorical variables: pet preference, list of pets and list of services and products bought for pet; Source–
data from a designed survey; Type–data are cross-sectional; Concerns–none.
19. EPA. Who–every model of automobile in the United States; What–vehicle manufacturer, vehicle type (car,
SUV, etc.), weight (probably pounds), horsepower (units of horsepower), and gas mileage (miles per
gallon) for city and highway driving; When–the information is currently collected; Where–United States;
Why–the EPA uses the information to track fuel economy of vehicles; How– among the data EPA analysts
collect from the automobile manufacturers are the name of the manufacturer (Ford, Toyota, etc.), vehicle
type….”; Variables–there are 6 variables: vehicle manufacturer and vehicle type are categorical variables;
weight, horsepower, and gas mileage for both city and highway driving are quantitative variables; Source–
data are not from a designed survey or experiment; Type–data are cross-sectional; Concerns–none.
20. Consumer Reports. Who–46 models of smart phones; What–brand, price (probably dollars), display size
(probably inches) operating system, camera image size (megapixels), and memory card slot (yes/no);
When–2013; Where–United States; Why–the information was compiled to provide information to
readers of Consumer Reports; How–not specified; Variables–– there are a total of 6 variables: price,
display size and image size are quantitative variables; brand and operating system are categorical variables,
and memory card slot is a nominal variable; Source–not specified; Type–the data are cross-sectional;
Concerns–this many or may not be a representative sample of smart phones, or includes all of them, we
don’t know. This is a rapidly changing market, so their data are at best a snapshot of the state of the market
at this time.
21. Zagat. Who–restaurants; What–% of customers liking restaurant, average meal cost ($), food rating (0-30),
decor rating (0-30), service rating (0-30); When–current; Where–United States; Why–service to provide
information for consumers; How–not specified; Variables–there are 5 variables: % liking and average cost
Copyright © 2015 Pearson Education, Inc.
, 1-4 Chapter 1 Data and Decisions
are quantitative variables; ratings (food, decor and service) are ordered categories, therefore, ordinal
variables; Source–not specified; Type–the data are cross-sectional.
22. L.L. Bean. Who–catalog mailings; What–number of catalogs mailed out, square inches in catalog, and sales
($ million) in 4 weeks following mailing; When–current; Where–L.L. Bean (United States); Why–to
investigate association among catalog characteristics, timing, and sales; How–collection of internal data;
Variables–there are 3 variables: number of catalogs, square inches in catalog, and sales are all quantitative
variables; Source–not specified; Type–data are cross-sectional; Concerns–none.
23. Stock market. Who–students in an MBA statistics class; What–total personal investment in stock market
($), number of different stocks held, total invested in mutual funds ($), and the name of each mutual fund;
When–not specified; Where–a business school in the northeast US; Why–the information was collected for
use in classroom illustrations; How–an online survey was conducted, participation was probably required
for all members of the class; Variables– there are 4 variables: total personal investment in stock market,
number of different stocks held, total invested in mutual funds are quantitative variables; the name of each
mutual fund is a categorical variable; Source–data come from a designed survey; Type–data are cross-
sectional.
24. Theme park sites. Who–potential theme park locations in Europe; What–country of site, estimated cost
(probably €), potential population size (counts), size of site (probably hectares), whether or not mass
transportation within 5 minutes of site; When–2013; Where–Europe; Why–to present to potential developers
on the feasibility of various sites; How–not specified; Variables–there are 5 variables: country of site and
whether or not mass transportation is within 5 minutes of site are both categorical variables; estimated cost,
potential population size and size of site are quantitative variables; Source–data are not from a designed
survey or experiment; Type–data are cross-sectional.
25. Indy 2012. Who–Indy 500 races; What–year, winner, car model, time (hrs), speed (mph), and car number;
When–1911-2012; Where–Indianapolis, Indiana; Why–examine trends in Indy 500 race winners; How–
official statistics kept for each race every year; Variables–– there are 6 variables: winner, car model, and
car number are categorical variables; year, time and speed are quantitative variables; Source–all race
results; Type–data are time-series; Concerns–none.
26. Kentucky Derby. Who–Kentucky Derby races; What–date, winner, winning margin (in lengths), jockey,
winner’s payoff ($), duration of the race (minutes and seconds), and track conditions; When–1875-2011;
Where–Churchill Downs, Louisville, Kentucky; Why–examine trends in Kentucky Derby winners; How–
official statistics kept for each race every year; Variables–there are 7 variables: winner, winning jockey,
and track conditions are categorical variables; date, winning margin, winner’s payoff, and duration of the
race are quantitative variables (date could be categorical as well); Source–race results; Type–data are time-
series; Concerns–none.
27. Mortgages. Each row represents each individual mortgage loan. Headings of the columns would be:
borrower’s name, mortgage amount.
28. Employee performance. Each row represents each individual employee. Headings of the columns would
be: Employee ID Number (to identify the row instead of name), contract average ($), supervisor’s rating (1-
10), and years with the company.
29. Company performance. Each row represents a week. Headings of the columns would be: week number of
the year (to identify each row), sales prediction ($), sales ($), and difference between predicted sales and
realized sales ($).
30. Command performance. Each row represents a Broadway show. Headings of the columns would be: the
show name (identifies the row), profit or loss ($), number of investors and investment total ($).
Copyright © 2015 Pearson Education, Inc.
