SOLUTIONS MANUAL - Chapter 1 – Data and Decisions SECTION EXERCISES

SOLUTIONS MANUAL

For

BUSINESS S TATISTICS

A FIRST COURSE

Norean R. Sharpe Richard De Veau x Paul Velleman 1 / 4

1-1 Copyright © 2017 Pearson Education, Inc.Chapter 1 – Data and Decisions

SECTION EXERCISES

SECTION 1.1

• Each row represents a different house that was recently sold. It can be described as a case.

b) There are 7 quantitative variables in each row including the house identifier.

• Each row represents a different transaction (not customer or book). It can be described as a case.

b) There are 8 variables including two identifiers in each row, 6 of the variables are quantitative.

SECTION 1.2

• House_ID is an identifier (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.

• 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 2009, 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

• It is not specified whether or not the real estate data of Exercise 1 are obtained from a survey. The data are

not from a designed 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.

• The student is using a secondary data source (from the Internet). The data are not from a designed

experiment, a data gathering method with specific requirements. 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

• The news. Answers will vary.

• The Internet. Answers will vary.

• 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 2 / 4

1-2 Chapter 1 Data and Decisions Copyright © 2017 Pearson Education, Inc.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.

• Your survey. Answers will vary.

• 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)

• 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.

• MBA admissions. Who–MBA applicants (in Northeast US); 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.

• MBA admissions II. Who–MBA students (in a program outside of Paris); 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 6 years, the purpose is to examine them as cross- sectional rather than as time-series; Concerns–none.

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Chapter 1 Data and Decisions 1-3

Copyright © 2017 Pearson Education, Inc.

• 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.

• 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.

• 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.

• 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.

• 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.

• Zagat. Who–restaurants;

What–% of customers liking restaurant, average meal cost ($), food rating (1-30), decor rating (1-30), service rating (1-30); When–current; Where–United States; Why–service to provide information for consumers; How–not specified; Variables–there are 5 variables: % liking and average cost are quantitative variables; ratings (food, decor and service) are ordered categories, therefore, ordinal variables; Source–not specified; Type–the data are cross-sectional.

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