INSTRUCTOR’S SOLUTIONS MANUAL
STATISTICS FOR BUSINESS AND ECONOMICS
14TH EDITION
CHAPTER NO. 01: 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.
1.8 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. Electrical generation capacity can take on values such as 400, 10,000, etc.
Therefore, it is quantitative.
b. Hub height can take on values such as 100, 200, etc. Therefore, it is quantitative.
c. Rotor diameter can take on values such as 5, 10, etc. Therefore, it is quantitative.
d. Location can take on values "Florida," "Georgia," etc., which are not numeric.
Therefore, it is qualitative.
e. Number of turbines in the project can take on values such as 5, 10, etc. Therefore, it
is quantitative.
1.16 a. The experimental unit is a single-tenant of retail properties.
b. The capitalization rate can take on values such as 6.75, 7.40, etc. Therefore, it is
quantitative.
Years remaining on lease can take on values such as, 5, 10, etc. Therefore, it is
quantitative.
Credit rating can take on values such as “BBB,” “B,” etc. Therefore, it is qualitative.
c. The 13 tenants in the study represent a sample of the Boulder Group consulting firm.
STATISTICS FOR BUSINESS AND ECONOMICS
14TH EDITION
CHAPTER NO. 01: 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.
1.8 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. Electrical generation capacity can take on values such as 400, 10,000, etc.
Therefore, it is quantitative.
b. Hub height can take on values such as 100, 200, etc. Therefore, it is quantitative.
c. Rotor diameter can take on values such as 5, 10, etc. Therefore, it is quantitative.
d. Location can take on values "Florida," "Georgia," etc., which are not numeric.
Therefore, it is qualitative.
e. Number of turbines in the project can take on values such as 5, 10, etc. Therefore, it
is quantitative.
1.16 a. The experimental unit is a single-tenant of retail properties.
b. The capitalization rate can take on values such as 6.75, 7.40, etc. Therefore, it is
quantitative.
Years remaining on lease can take on values such as, 5, 10, etc. Therefore, it is
quantitative.
Credit rating can take on values such as “BBB,” “B,” etc. Therefore, it is qualitative.
c. The 13 tenants in the study represent a sample of the Boulder Group consulting firm.
