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Suppose the following information is collected from robert on his application for a loan.

19/12/2020 Client: saad24vbs Deadline: 3 days

Chapter #1:


Beginning of the End … Or the End of the


Beginning?


The past few years have been challenging for Good Tunes & More (GT&M), a


business that traces its roots to Good Tunes, a store that exclusively sold music


CDs and vinyl records.


GT&M first broadened its merchandise to include home entertainment


and computer systems (the “More”), and then undertook an expansion to take


advantage of prime locations left empty by bankrupt former competitors. Today,


GT&M finds itself at a crossroads. Hoped-for increases in revenues that have


failed to occur and declining profit margins due to the competitive pressures of


online sellers have led management to reconsider the future of the business.


While some investors in the business have argued for an orderly retreat,


closing


stores and limiting the variety of merchandise, GT&M CEO Emma Levia


has decided to “double down” and expand the business


by purchasing Whitney


Wireless, a successful three-store chain that sells smartphones


and other mobile


devices.


Levia foresees creating a brand new “A-to-Z” electronics retailer but


first must establish a fair and reasonable price for the privately held Whitney


Wireless.


To do so, she has asked a group of analysts to identify the data that


would be helpful in setting a price for the wireless business. As part of that


group, you quickly realize that you need the data that would help to verify the


contents of the wireless company’s basic financial statements.


You focus on data associated with the company’s profit and loss statement


and quickly realize the need for sales and expense-related


variables.


You begin to


think about what the data for


such variables would look


like and how to collect those


data. You realize that you are


starting to apply the DCOVA


framework to the objective


of helping Levia acquire


Whitney Wireless.


Chapter Defining and


1 Collecting Data


Tyler Olson/Shutterstock


contents


1.1 Defining Variables


1.2 Collecting Data


1.3 Types of Sampling Methods


1.4 Types of Survey Errors


Think About This: New Media


Surveys/Old Sampling Problems


Using Statistics: Beginning of


the End … Revisited


Chapter 1 Excel Guide


Chapter 1 Minitab Guide


Objectives


Understand issues that arise


when defining variables


How to define variables


How to collect data


Identify the different ways to


collect a sample


Understand the types of


survey errors


Business Statistics: A First Course, Seventh Edition, by David M. Levine, Kathryn A. Szabat, and David F. Stephan. Published by Pearson.


Copyright © 2016 by Pearson Education, Inc.


ISBN: 978-1-323-26258-0


1.1 Defining Variables 11


When Emma Levia decides to purchase Whitney Wireless, she has defined a new


goal or business objective for GT&M. Business objectives can arise from any


level of management and can be as varied as the following:


• A marketing analyst needs to assess the effectiveness of a new online advertising campaign.


• A pharmaceutical company needs to determine whether a new drug is more effective


than those currently in use.


• An operations manager wants to improve a manufacturing or service process.


• An auditor needs to review a company’s financial transactions to determine whether the


company is in compliance with generally accepted accounting principles.


Establishing an objective marks the end of a problem definition process. This end triggers


the new process of identifying the correct data to support the objective. In the GT&M scenario,


having decided to buy Whitney Wireless, Levia needs to identify the data that would be helpful


in setting a price for the wireless business. This process of identifying the correct data triggers


the start of applying the tasks of the DCOVA framework. In other words, the end of problem


definition marks the beginning of applying statistics to business decision making.


Identifying the correct data to support a business objective is a two-part job that requires


defining variables and collecting the data for those variables. These tasks are the first two tasks


of the DCOVA framework first defined in Section GS.1 and which can be restated here as:


• Define the variables that you want to study to solve a problem or meet an objective.


• Collect the data for those variables from appropriate sources.


This chapter discusses these two tasks which must always be done before the Organize, Visualize,


and Analyze tasks.


Defining variables at first may seem to be the simple process of making the list of things one


needs to help solve a problem or meet an objective. However, consider the GT&M scenario.


Most would quickly agree that yearly sales of Whitney Wireless would be part of the data


needed to meet Levia’s objective, but just placing “yearly sales” on a list could lead to confusion


and miscommunication: Does this variable refer to sales per year for the entire chain or


for individual stores? Does the variable refer to net or gross sales? Are the yearly sales values


expressed in number of units or as currency amounts such as U.S. dollar sales?


These questions illustrate that for each variable of interest that you identify you must supply


an operational definition, a universally accepted meaning that is clear to all associated


with an analysis. Operational definitions should also classify the variable, as explained in the


next section, and may include additional facts such as units of measures, allowed range of


values, and definitions of specific variable values, depending on how the variable is classified.


Classifying Variables by Type


When you operationally define a variable, you must classify the variable as being either categorical


or numerical. Categorical variables (also known as qualitative variables) take categories


as their values. Numerical variables (also known as quantitative variables) have values


that represent a counted or measured quantity. Classification also affects a variable’s operational


definition and getting the classification correct is important because certain statistical methods


can be applied correctly to one type or the other, while other methods may need a specific mix


of variable types.


Categorical variables can take the form of yes-and-no questions such as “Do you have a


Twitter account?” (in which yes and no form the variable’s two categories) or describe a trait


or characteristic that has many categories such as undergraduate class standing (which might


have the defined categories freshman, sophomore, junior, and senior). When defining a categorical


variable, the list of permissible category values must be included and each category


1.1 Defining Variables


Student Tip


Providing operational


definitions for concepts


is important, too, when


writing a textbook! The


end-of-chapter Key


Terms gives you an index


of operational definitions


and the most fundamental


definitions are


presented in boxes such


as the page 3 box that


defines variable and data.


Business Statistics: A First Course, Seventh Edition, by David M. Levine, Kathryn A. Szabat, and David F. Stephan. Published by Pearson.


Copyright © 2016 by Pearson Education, Inc.


ISBN: 978-1-323-26258-0


12 Chapter 1 Defining and Collecting Data


value should be defined, too, e.g., that a “freshman” is a student who has completed fewer


than 32 credit hours. Overlooking these requirements can lead to confusion and incorrect data


collection. In one famous example, when persons were asked by researchers to fill in a value


for the categorical variable sex, many answered yes and not male or female, the values that the


researchers intended. (Perhaps this is the reason that gender has replaced sex on many data collection


forms—gender’s operational definition is more self-apparent.)


The operational definitions of numerical variables are affected by whether the variable being


defined is discrete or continuous. Discrete variables such as “number of items purchased”


or “total amount paid” are numerical values that arise from a counting process. Continuous


variables such as “time spent on checkout line” or “distance from home to store” have numerical


values that arise from a measuring process and those values depend on the precision of the


measuring instrument used. For example, “time spent on checkout line” might be 2, 2.1, 2.14,


or 2.143 minutes, depending on the precision of the timing instrument being used. Units of


measures and the level of precision should be part of the operational definitions of continuous


variables, e.g., “tenths of a second” for “time spent on checkout line.” The definitions of any


numerical variable can include the allowed range of values, such as “must be greater than 0”


for “number of items purchased.”


When defining variables for survey collection (discussed in Section 1.2), thinking about


the responses you seek helps classify variables as Table 1.1 demonstrates. Thinking about how


a variable will be used to solve a problem or meet an objective can also be helpful when you


define a variable. The variable age might be a numerical (discrete) variable in some cases or


might be categorical with categories such as child, young adult, middle-aged, and retirement


aged in other contexts.


Problems for Section 1.1


Learning the Basics


1.1 Four different beverages are sold at a fast-food restaurant:


soft drinks, tea, coffee, and bottled water. Explain why the


type of beverage sold is an example of a categorical variable.


1.2 U.S. businesses are listed by size: small, medium, and large. Explain


why business size is an example of a categorical variable.


1.3 The time it takes to download a video from the Internet is


measured. Explain why the download time is a continuous


numerical variable.


Applying the Concepts


SELF


Test


1.4 For each of the following variables, determine


whether the variable is categorical or numerical. If the


variable is numerical, determine whether the variable is discrete or


continuous.


a. Number of cellphones in the household


b. Monthly data usage (in MB)


c. Number of text messages exchanged per month


d. Voice usage per month (in minutes)


e. Whether the cellphone is used for email


1.5 The following information is collected


Question Responses Variable Type


Do you have a Facebook


profile?


❑ Yes ❑ No Categorical


How many text messages have


you sent in the past three days?


______ Numerical


(discrete)


How long did the mobile app


update take to download?


______ seconds Numerical


(continuous)


Problems for Section 1.1


Learning the Basics


1.1 Four different beverages are sold at a fast-food restaurant:


soft drinks, tea, coffee, and bottled water. Explain why the


type of beverage sold is an example of a categorical variable.


1.2 U.S. businesses are listed by size: small, medium, and large. Explain


why business size is an example of a categorical variable.


1.3 The time it takes to download a video from the Internet is


measured. Explain why the download time is a continuous


numerical variable.


Applying the Concepts


SELF


Test


1.4 For each of the following variables, determine


whether the variable is categorical or numerical. If the


variable is numerical, determine whether the variable is discrete or


continuous.


a. Number of cellphones in the household


b. Monthly data usage (in MB)


c. Number of text messages exchanged per month


d. Voice usage per month (in minutes)


e. Whether the cellphone is used for email


1.5 The following information is collected from students upon


exiting the campus bookstore during the first week of classes.


a. Amount of time spent shopping in the bookstore


b. Number of textbooks purchased


c. Academic major


d. Gender


Classify each of these variables as categorical or numerical. If the


variable is numerical, determine whether the variable is discrete or


continuous.


1.6 For each of the following variables, determine whether the


variable is categorical or numerical. If the variable is numerical,


determine whether the variable is discrete or continuous.


a. Name of Internet service provider


b. Time, in hours, spent surfing the Internet per week


c. Whether the individual uses a mobile phone to connect to the


Internet


d. Number of online purchases made in a month


e. Where the individual uses social networks to find sought-after


information


Learn More


Read the Short Takes for


Chapter 1 for more examples


of classifying variables


as either


categorical or numerical.


Ta ble 1 . 1


Identifying Types of


Variables


Question Responses Variable Type


Do you have a Facebook


profile?


❑ Yes ❑ No Categorical


How many text messages have


you sent in the past three days?


______ Numerical


(discrete)


How long did the mobile app


update take to download?


______ seconds Numerical


(continuous)


Business Statistics: A First Course, Seventh Edition, by David M. Levine, Kathryn A. Szabat, and David F. Stephan. Published by Pearson.


Copyright © 2016 by Pearson Education, Inc.


ISBN: 978-1-323-26258-0


1.2 Collecting Data 13


1.2 Collecting Data


After defining the variables that you want to study, you can proceed with the data collection


task. Collecting data is a critical task because if you collect data that are flawed by biases,


ambiguities, or other types of errors, the results you will get from using such data with even


the most sophisticated statistical methods will be suspect or in error. (For a famous example of


flawed data collection leading to incorrect results, read the Think About This essay on page 21.)


Data collection consists of identifying data sources, deciding whether the data you collect


will be from a population or a sample, cleaning your data, and sometimes recoding variables.


The rest of this section explains these aspects of data collection.


Data Sources


You collect data from either primary or secondary data sources. You are using a primary data


source if you collect your own data for analysis. You are using a secondary data source if the


data for your analysis have been collected by someone else.


You collect data by using any of the following:


• Data distributed by an organization or individual


• The outcomes of a designed experiment


• The responses from a survey


• The results of conducting an observational study


• Data collected by ongoing business activities


Market research companies and trade associations distribute data pertaining to specific industries


or markets. Investment services provide business and financial data on publicly listed


companies. Syndicated services such as The Nielsen Company provide consumer research data to


telecom and mobile media companies. Print and online media companies also distribute data that


they may have collected themselves or may be republishing from other sources.


The outcomes of a designed experiment are a second data source. For example, a consumer


electronics company might conduct an experiment that compares the sales of mobile


electronics merchandise for different store locations. Note that developing a proper experimental


design is mostly beyond the scope of this book, but Chapter 10 discusses some of the


fundamental experimental design concepts.


Survey responses represent a third type of data source. People being surveyed are asked


questions about their beliefs, attitudes, behaviors, and other characteristics. For example,


people could be asked which store location for mobile electronics merchandise is preferable.


(Such a survey could lead to data that differ from the data collected from the outcomes of the


1.7 For each of the following variables, determine whether the


variable is categorical or numerical. If the variable is numerical,


determine whether the variable is discrete or continuous.


a. Amount of money spent on clothing in the past month


b. Favorite department store


c. Most likely time period during which shopping for clothing


takes place (weekday, weeknight, or weekend)


d. Number of pairs of shoes owned


1.8 Suppose the following information is collected from Robert


Keeler on his application for a home mortgage loan at the Metro


County Savings and Loan Association.


a. Monthly payments: $2,227


b. Number of jobs in past 10 years: 1


c. Annual family income: $96,000


d. Marital status: Married


Classify each of the responses by type of data.


1.9 One of the variables most often included in surveys is income.


Sometimes the question is phrased “What is your income


(in thousands of dollars)?” In other surveys, the respondent is


asked to “Select the circle corresponding to your income level”


and is given a number of income ranges to choose from.


a. In the first format, explain why income might be considered


either discrete or continuous.


b. Which of these two formats would you prefer to use if you


were conducting a survey? Why?


1.10 If two students score a 90 on the same examination,


what arguments could be used to show that the underlying


variable—test score—is continuous?


1.11 The director of market research at a large department store


chain wanted to conduct a survey throughout a metropolitan area


to determine the amount of time working women spend shopping


for clothing in a typical month.


a. Indicate the type of data the director might want to collect.


b. Develop a first draft of the questionnaire needed in (a) by writing


three categorical questions and three numerical questions


that you feel would be appropriate for this survey


One of the variables most often included in surveys is income.


Sometimes the question is phrased “What is your income


1.2 Collecting Data


After defining the variables that you want to study, you can proceed with the data collection


task. Collecting data is a critical task because if you collect data that are flawed by biases,


ambiguities, or other types of errors, the results you will get from using such data with even


the most sophisticated statistical methods will be suspect or in error. (For a famous example of


flawed data collection leading to incorrect results, read the Think About This essay on page 21.)


Data collection consists of identifying data sources, deciding whether the data you collect


will be from a population or a sample, cleaning your data, and sometimes recoding variables.


The rest of this section explains these aspects of data collection.


Data Sources


You collect data from either primary or secondary data sources. You are using a primary data


source if you collect your own data for analysis. You are using a secondary data source if the


data for your analysis have been collected by someone else.


You collect data by using any of the following:


• Data distributed by an organization or individual


• The outcomes of a designed experiment


• The responses from a survey


• The results of conducting an observational study


• Data collected by ongoing business activities


Market research companies and trade associations distribute data pertaining to specific industries


or markets. Investment services provide business and financial data on publicly listed


companies. Syndicated services such as The Nielsen Company provide consumer research data to


telecom and mobile media companies. Print and online media companies also distribute data that


they may have collected themselves or may be republishing from other sources.


The outcomes of a designed experiment are a second data source. For example, a consumer


electronics company might conduct an experiment that compares the sales of mobile


electronics merchandise for different store locations. Note that developing a proper experimental


design is mostly beyond the scope of this book, but Chapter 10 discusses some of the


fundamental experimental design concepts.


Survey responses represent a third type of data source. People being surveyed are asked


questions about their beliefs, attitudes, behaviors, and other characteristics. For example,


people could be asked which store location for mobile electronics merchandise is preferable.


(Such a survey could lead to data that differ from the data collected from the outcomes of the


1.7 For each of the following variables, determine whether the


variable is categorical or numerical. If the variable is numerical,


determine whether the variable is discrete or continuous.


a. Amount of money spent on clothing in the past month


b. Favorite department store


c. Most likely time period during which shopping for clothing


takes place (weekday, weeknight, or weekend)


d. Number of pairs of shoes owned


1.8 Suppose the following information is collected from Robert


Keeler on his application for a home mortgage loan at the Metro


County Savings and Loan Association.


a. Monthly payments: $2,227


b. Number of jobs in past 10 years: 1


c. Annual family income: $96,000


d. Marital status: Married


Classify each of the responses by type of data.


1.9 One of the variables most often included in surveys is income.


Sometimes the question is phrased “What is your income


(in thousands of dollars)?” In other surveys, the respondent is


asked to “Select the circle corresponding to your income level”


and is given a number of income ranges to choose from.


a. In the first format, explain why income might be considered


either discrete or continuous.


b. Which of these two formats would you prefer to use if you


were conducting a survey? Why?


1.10 If two students score a 90 on the same examination,


what arguments could be used to show that the underlying


variable—test score—is continuous?


1.11 The director of market research at a large department store


chain wanted to conduct a survey throughout a metropolitan area


to determine the amount of time working women spend shopping


for clothing in a typical month.


a. Indicate the type of data the director might want to collect.


b. Develop a first draft of the questionnaire needed in (a) by writing


three categorical questions and three numerical questions


that you feel would be appropriate for this survey.


Business Statistics: A First Course, Seventh Edition, by David M. Levine, Kathryn A. Szabat, and David F. Stephan. Published by Pearson.


Copyright © 2016 by Pearson Education, Inc.


ISBN: 978-1-323-26258-0


14 Chapter 1 Defining and Collecting Data


designed experiment of the previous paragraph.) Surveys can be affected by any of the four


types of errors that are discussed in Section 1.4.


Observational study results are a fourth data source. A researcher collects data by directly


observing a behavior, usually in a natural or neutral setting. Observational studies are a common

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