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File: ch01, Chapter 1: Introduction to Statistics
True/False
1. Virtually all areas of business use statistics in decision making.
Ans: True
Response: See section 1.1, Statistics in Business
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
2. Statistics can be used to predict the business future.
Ans: True
Response: See section 1.1, Statistics in Business
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
3. Statistics are used to market vitamins.
Ans: True
Response: See section 1.1, Statistics in Business
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
4. A list of final grades in an introductory class in business is an example of statistics
Ans: false
Response: See section 1.1, Statistics in Business
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
5. The complete collection of all entities under study is called the sample.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
6. A portion or subset of the entities under study is called the statistic.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
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7. A descriptive measure of the population is called a parameter.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
8. A census is the process of gathering data on all the entities in the population.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
9. Statistics is commonly divided into two branches called descriptive statistics and summary statistics.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
10. A descriptive measure of the sample is called a statistic.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
11. Gathering data from a sample to reach conclusions about the population from which the sample was
drawn is called descriptive statistics.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Medium
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
12. Calculation of population parameters is usually either impossible or excessively time consuming and
costly.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
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13. The basis for inferential statistics is the ability to make decisions about population parameters without
having to complete a census of the population.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
14. A variable is a numerical description of each of the possible outcomes of an experiment.
Ans: True
Response: See section 1.3 Variable and data
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
15. Variables and measurement data are interchangeable terms.
Ans: False
Response: See section 1.3 Variable and data
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
16. Measurements occur when a standard process is used to assign numbers to attributes or characteristics
of a variable.
Ans: True
Response: See section 1.3 Variable and data
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
17. All numerical data must be analyzed statistically in the same way because all of them are represented
by numbers.
Ans: False
Response: See section 1.4, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
18. The manner in which numerical data can be analyzed statistically depends on the level of data
measurement represented by numbers being analyzed.
Ans: True
Response: See section 1.4, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
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19. The lowest level of data measurement is the ratio level.
Ans: False
Response: See section 1.4, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
20. The highest level of data measurement is the ratio level.
Ans: True
Response: See section 1.4, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
21. Numbers which are used only to classify or categorize the observations represent data measured at the
nominal level.
Ans: True
Response: See section 1.4, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
22. Numbers which are used to rank-order the performance of workers represent data measured at the
interval level.
Ans: False
Response: See section 1.4, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
23. Nominal and ordinal data are sometimes referred to as qualitative data.
Ans: True
Response: See section 1.4, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
24. Nominal and ordinal data are sometimes referred to as quantitative data.
Ans: False
Response: See section 1.4, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
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File: ch01, Chapter 1: Introduction to Statistics
True/False
1. Virtually all areas of business use statistics in decision making.
Ans: True
Response: See section 1.1, Statistics in Business
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
2. Statistics can be used to predict the business future.
Ans: True
Response: See section 1.1, Statistics in Business
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
3. Statistics are used to market vitamins.
Ans: True
Response: See section 1.1, Statistics in Business
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
4. A list of final grades in an introductory class in business is an example of statistics
Ans: false
Response: See section 1.1, Statistics in Business
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
5. The complete collection of all entities under study is called the sample.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
6. A portion or subset of the entities under study is called the statistic.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
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7. A descriptive measure of the population is called a parameter.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
8. A census is the process of gathering data on all the entities in the population.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
9. Statistics is commonly divided into two branches called descriptive statistics and summary statistics.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
10. A descriptive measure of the sample is called a statistic.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
11. Gathering data from a sample to reach conclusions about the population from which the sample was
drawn is called descriptive statistics.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Medium
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
12. Calculation of population parameters is usually either impossible or excessively time consuming and
costly.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
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13. The basis for inferential statistics is the ability to make decisions about population parameters without
having to complete a census of the population.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
14. A variable is a numerical description of each of the possible outcomes of an experiment.
Ans: True
Response: See section 1.3 Variable and data
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
15. Variables and measurement data are interchangeable terms.
Ans: False
Response: See section 1.3 Variable and data
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
16. Measurements occur when a standard process is used to assign numbers to attributes or characteristics
of a variable.
Ans: True
Response: See section 1.3 Variable and data
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
17. All numerical data must be analyzed statistically in the same way because all of them are represented
by numbers.
Ans: False
Response: See section 1.4, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
18. The manner in which numerical data can be analyzed statistically depends on the level of data
measurement represented by numbers being analyzed.
Ans: True
Response: See section 1.4, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
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19. The lowest level of data measurement is the ratio level.
Ans: False
Response: See section 1.4, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
20. The highest level of data measurement is the ratio level.
Ans: True
Response: See section 1.4, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
21. Numbers which are used only to classify or categorize the observations represent data measured at the
nominal level.
Ans: True
Response: See section 1.4, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
22. Numbers which are used to rank-order the performance of workers represent data measured at the
interval level.
Ans: False
Response: See section 1.4, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
23. Nominal and ordinal data are sometimes referred to as qualitative data.
Ans: True
Response: See section 1.4, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
24. Nominal and ordinal data are sometimes referred to as quantitative data.
Ans: False
Response: See section 1.4, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
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