Solutions Manual
Statistics for Business and Economics 12th Edition
By
Nancy Boudreau,
James T. McClave,
P. George Benson,
Terry Sincich
( All Chapters Included - 100% Verified Solutions )
1
,Chapter 1
Statistics, Data, and Statistical Thinking
1.1 Statistics is a science that deals with the collection, classification, analysis, and interpretation of
information or data. It is a meaningful, useful science with a broad, almost limitless scope of applications
to business, government, and the physical and social sciences.
1.2 Descriptive statistics utilizes numerical and graphical methods to look for patterns, to summarize, and to
present the information in a set of data. Inferential statistics utilizes sample data to make estimates,
decisions, predictions, or other generalizations about a larger set of data.
1.3 The four elements of a descriptive statistics problem are:
1. The population or sample of interest. This is the collection of all the units upon which the variable is
measured.
2. One or more variables that are to be investigated. These are the types of data that are to be collected.
3. Tables, graphs, or numerical summary tools. These are tools used to display the characteristic of the
sample or population.
4. Identification of patterns in the data. These are conclusions drawn from what the summary tools
revealed about the population or sample.
1.4 The five elements of an inferential statistical analysis are:
1. The population of interest. The population is a set of existing units.
2. One or more variables that are to be investigated. A variable is a characteristic or property of an
individual population unit.
3. The sample of population units. A sample is a subset of the units of a population.
4. The inference about the population based on information contained in the sample. A statistical
inference is an estimate, prediction, or generalization about a population based on information
contained in a sample.
5. A measure of reliability for the inference. The reliability of an inference is how confident one is that
the inference is correct.
1.5 The first major method of collecting data is from a published source. These data have already been
collected by someone else and are available in a published source. The second method of collecting data is
from a designed experiment. These data are collected by a researcher who exerts strict control over the
experimental units in a study. These data are measured directly from the experimental units. The final
method of collecting data is observational. These data are collected directly from experimental units by
simply observing the experimental units in their natural environment and recording the values of the
desired characteristics. The most common type of observational study is a survey.
1.6 Quantitative data are measurements that are recorded on a meaningful numerical scale. Qualitative data are
measurements that are not numerical in nature; they can only be classified into one of a group of categories.
1.7 A population is a set of existing units such as people, objects, transactions, or events. A variable is a
characteristic or property of an individual population unit such as height of a person, time of a reflex,
amount of a transaction, etc.
1
Copyright © 2014 Pearson Education, Inc.
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,2 Chapter 1
1.8 A population is a set of existing units such as people, objects, transactions, or events. A sample is a subset
of the units of a population.
1.9 A representative sample is a sample that exhibits characteristics similar to those possessed by the target
population. A representative sample is essential if inferential statistics is to be applied. If a sample does
not possess the same characteristics as the target population, then any inferences made using the sample
will be unreliable.
1.10 An inference without a measure of reliability is nothing more than a guess. A measure of reliability
separates statistical inference from fortune telling or guessing. Reliability gives a measure of how
confident one is that the inference is correct.
1.11 A population is a set of existing units such as people, objects, transactions, or events. A process is a series
of actions or operations that transform inputs to outputs. A process produces or generates output over time.
Examples of processes are assembly lines, oil refineries, and stock prices.
1.12 Statistical thinking involves applying rational thought processes to critically assess data and inferences
made from the data. It involves not taking all data and inferences presented at face value, but rather
making sure the inferences and data are valid.
1.13 The data consisting of the classifications A, B, C, and D are qualitative. These data are nominal and thus
are qualitative. After the data are input as 1, 2, 3, and 4, they are still nominal and thus qualitative. The
only differences between the two data sets are the names of the categories. The numbers associated with
the four groups are meaningless.
1.14 Answers will vary. First, number the elements of the population from 1 to 200,000. Using MINITAB,
generate 10 numbers on the interval from 1 to 200,000, eliminating any duplicates.
The 10 numbers selected for the random sample are:
135075
89127
189226
83899
112367
191496
110021
44853
42091
198461
Elements with the above numbers are selected for the sample.
1.15 a. The experimental unit for this study is a single-family residential property in Arlington, Texas.
b. The variables measured are the sale price and the Zillow estimated value. Both of these variables are
quantitative.
c. If these 2,045 properties were all the properties sold in Arlington, Texas in the past 6 months, then
this would be considered the population.
d. If these 2,045 properties represent a sample, then the population would be all the single-family
residential properties sold in the last 6 months in Arlington, Texas.
Copyright © 2014 Pearson Education, Inc.
3
, Statistics, Data, and Statistical Thinking 3
e. No. The real estate market across the United States varies greatly. The prices of single-family
residential properties in this small area are probably not representative of all properties across the
United States.
1.16 a. The experimental unit for this study is an NFL quarterback.
b. The variables measured in this study include draft position, NFL winning ratio, and QB production
score. Since the draft position was put into 3 categories, it is a qualitative variable. The NFL winning
ratio and the QB production score are both quantitative.
c. Since we want to project the performance of future NFL QBs, this would be an application of
inferential statistics.
1.17 a. The population of interest is all citizens of the United States.
b. The variable of interest is the view of each citizen as to whether the president is doing a good or bad
job. It is qualitative.
c. The sample is the 2000 individuals selected for the poll.
d. The inference of interest is to estimate the proportion of all U.S. citizens who believe the president is
doing a good job.
e. The method of data collection is a survey.
f. It is not very likely that the sample will be representative of the population of all citizens of the
United States. By selecting phone numbers at random, the sample will be limited to only those
people who have telephones. Also, many people share the same phone number, so each person
would not have an equal chance of being contacted. Another possible problem is the time of day the
calls are made. If the calls are made in the evening, those people who work in the evening would not
be represented.
1.18 a. High school GPA is a number usually between 0.0 and 4.0. Therefore, it is quantitative.
b. Honors/awards would have responses that name things. Therefore, it would be qualitative.
c. The scores on the SAT's are numbers between 200 and 800. Therefore, it is quantitative.
d. Gender is either male or female. Therefore, it is qualitative.
e. Parent's income is a number: $25,000, $45,000, etc. Therefore, it is quantitative.
f. Age is a number: 17, 18, etc. Therefore, it is quantitative.
1.19 I. Qualitative; the possible responses are "yes" or "no," which are non-numerical.
II. Quantitative; age is measured on a numerical scale, such as 15, 32, etc.
III. Qualitative; the possible responses are “yes” or “no,” which are non-numerical.
IV. Qualitative; the possible responses are "laser printer" or "another type of printer," which are non-
numerical.
Copyright © 2014 Pearson Education, Inc.
4
Statistics for Business and Economics 12th Edition
By
Nancy Boudreau,
James T. McClave,
P. George Benson,
Terry Sincich
( All Chapters Included - 100% Verified Solutions )
1
,Chapter 1
Statistics, Data, and Statistical Thinking
1.1 Statistics is a science that deals with the collection, classification, analysis, and interpretation of
information or data. It is a meaningful, useful science with a broad, almost limitless scope of applications
to business, government, and the physical and social sciences.
1.2 Descriptive statistics utilizes numerical and graphical methods to look for patterns, to summarize, and to
present the information in a set of data. Inferential statistics utilizes sample data to make estimates,
decisions, predictions, or other generalizations about a larger set of data.
1.3 The four elements of a descriptive statistics problem are:
1. The population or sample of interest. This is the collection of all the units upon which the variable is
measured.
2. One or more variables that are to be investigated. These are the types of data that are to be collected.
3. Tables, graphs, or numerical summary tools. These are tools used to display the characteristic of the
sample or population.
4. Identification of patterns in the data. These are conclusions drawn from what the summary tools
revealed about the population or sample.
1.4 The five elements of an inferential statistical analysis are:
1. The population of interest. The population is a set of existing units.
2. One or more variables that are to be investigated. A variable is a characteristic or property of an
individual population unit.
3. The sample of population units. A sample is a subset of the units of a population.
4. The inference about the population based on information contained in the sample. A statistical
inference is an estimate, prediction, or generalization about a population based on information
contained in a sample.
5. A measure of reliability for the inference. The reliability of an inference is how confident one is that
the inference is correct.
1.5 The first major method of collecting data is from a published source. These data have already been
collected by someone else and are available in a published source. The second method of collecting data is
from a designed experiment. These data are collected by a researcher who exerts strict control over the
experimental units in a study. These data are measured directly from the experimental units. The final
method of collecting data is observational. These data are collected directly from experimental units by
simply observing the experimental units in their natural environment and recording the values of the
desired characteristics. The most common type of observational study is a survey.
1.6 Quantitative data are measurements that are recorded on a meaningful numerical scale. Qualitative data are
measurements that are not numerical in nature; they can only be classified into one of a group of categories.
1.7 A population is a set of existing units such as people, objects, transactions, or events. A variable is a
characteristic or property of an individual population unit such as height of a person, time of a reflex,
amount of a transaction, etc.
1
Copyright © 2014 Pearson Education, Inc.
2
,2 Chapter 1
1.8 A population is a set of existing units such as people, objects, transactions, or events. A sample is a subset
of the units of a population.
1.9 A representative sample is a sample that exhibits characteristics similar to those possessed by the target
population. A representative sample is essential if inferential statistics is to be applied. If a sample does
not possess the same characteristics as the target population, then any inferences made using the sample
will be unreliable.
1.10 An inference without a measure of reliability is nothing more than a guess. A measure of reliability
separates statistical inference from fortune telling or guessing. Reliability gives a measure of how
confident one is that the inference is correct.
1.11 A population is a set of existing units such as people, objects, transactions, or events. A process is a series
of actions or operations that transform inputs to outputs. A process produces or generates output over time.
Examples of processes are assembly lines, oil refineries, and stock prices.
1.12 Statistical thinking involves applying rational thought processes to critically assess data and inferences
made from the data. It involves not taking all data and inferences presented at face value, but rather
making sure the inferences and data are valid.
1.13 The data consisting of the classifications A, B, C, and D are qualitative. These data are nominal and thus
are qualitative. After the data are input as 1, 2, 3, and 4, they are still nominal and thus qualitative. The
only differences between the two data sets are the names of the categories. The numbers associated with
the four groups are meaningless.
1.14 Answers will vary. First, number the elements of the population from 1 to 200,000. Using MINITAB,
generate 10 numbers on the interval from 1 to 200,000, eliminating any duplicates.
The 10 numbers selected for the random sample are:
135075
89127
189226
83899
112367
191496
110021
44853
42091
198461
Elements with the above numbers are selected for the sample.
1.15 a. The experimental unit for this study is a single-family residential property in Arlington, Texas.
b. The variables measured are the sale price and the Zillow estimated value. Both of these variables are
quantitative.
c. If these 2,045 properties were all the properties sold in Arlington, Texas in the past 6 months, then
this would be considered the population.
d. If these 2,045 properties represent a sample, then the population would be all the single-family
residential properties sold in the last 6 months in Arlington, Texas.
Copyright © 2014 Pearson Education, Inc.
3
, Statistics, Data, and Statistical Thinking 3
e. No. The real estate market across the United States varies greatly. The prices of single-family
residential properties in this small area are probably not representative of all properties across the
United States.
1.16 a. The experimental unit for this study is an NFL quarterback.
b. The variables measured in this study include draft position, NFL winning ratio, and QB production
score. Since the draft position was put into 3 categories, it is a qualitative variable. The NFL winning
ratio and the QB production score are both quantitative.
c. Since we want to project the performance of future NFL QBs, this would be an application of
inferential statistics.
1.17 a. The population of interest is all citizens of the United States.
b. The variable of interest is the view of each citizen as to whether the president is doing a good or bad
job. It is qualitative.
c. The sample is the 2000 individuals selected for the poll.
d. The inference of interest is to estimate the proportion of all U.S. citizens who believe the president is
doing a good job.
e. The method of data collection is a survey.
f. It is not very likely that the sample will be representative of the population of all citizens of the
United States. By selecting phone numbers at random, the sample will be limited to only those
people who have telephones. Also, many people share the same phone number, so each person
would not have an equal chance of being contacted. Another possible problem is the time of day the
calls are made. If the calls are made in the evening, those people who work in the evening would not
be represented.
1.18 a. High school GPA is a number usually between 0.0 and 4.0. Therefore, it is quantitative.
b. Honors/awards would have responses that name things. Therefore, it would be qualitative.
c. The scores on the SAT's are numbers between 200 and 800. Therefore, it is quantitative.
d. Gender is either male or female. Therefore, it is qualitative.
e. Parent's income is a number: $25,000, $45,000, etc. Therefore, it is quantitative.
f. Age is a number: 17, 18, etc. Therefore, it is quantitative.
1.19 I. Qualitative; the possible responses are "yes" or "no," which are non-numerical.
II. Quantitative; age is measured on a numerical scale, such as 15, 32, etc.
III. Qualitative; the possible responses are “yes” or “no,” which are non-numerical.
IV. Qualitative; the possible responses are "laser printer" or "another type of printer," which are non-
numerical.
Copyright © 2014 Pearson Education, Inc.
4
