BUSINESS STATISTICS FOR CONTEMPORARY DECISION MAKING 11TH EDITION
TEST BANK BUSINESS STATISTICS FOR CONTEMPORARY
DECISION-MAKING 11TH EDITION BY BLACK ALL CHAPTERS 1 TO
19 COVERED QUESTIONS AND ANSWERS GRADED A+ LATEST UPDATE.
,BUSINESS STATISTICS FOR CONTEMPORARY DECISION MAKING 11TH EDITION
Table of Contents
1. Chapter 1: Introduction to Statistics and Business Analytics
2. Chapter 2: Visualizing Data with Charts and Graphs
3. Chapter 3: Descriptive Statistics
4. Chapter 4: Probability
5. Chapter 5: Discrete Distributions
6. Chapter 6: Continuous Distributions
7. Chapter 7: Sampling and Sampling Distributions
8. Chapter 8: Statistical Inference: Estimation for Single Populations
9. Chapter 9: Statistical Inference: Hypothesis Testing for Single Populations
10. Chapter 10: Statistical Inferences About Two Populations
11. Chapter 11: Analysis of Variance and Design of Experiments
12. Chapter 12: Simple Regression Analysis and Correlation
13. Chapter 13: Multiple Regression Analysis
14. Chapter 14: Building Multiple Regression Models
15. Chapter 15: Time-Series Forecasting and Index Numbers
16. Chapter 16: Analysis of Categorical Data
17. Chapter 17: Nonparametric Statistics
18. Chapter 18: Statistical Quality Control
19. Chapter 19: Decision Analysis
,BUSINESS STATISTICS FOR CONTEMPORARY DECISION MAKING 11TH EDITION
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.
,BUSINESS STATISTICS FOR CONTEMPORARY DECISION MAKING 11TH EDITION
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.
TEST BANK BUSINESS STATISTICS FOR CONTEMPORARY
DECISION-MAKING 11TH EDITION BY BLACK ALL CHAPTERS 1 TO
19 COVERED QUESTIONS AND ANSWERS GRADED A+ LATEST UPDATE.
,BUSINESS STATISTICS FOR CONTEMPORARY DECISION MAKING 11TH EDITION
Table of Contents
1. Chapter 1: Introduction to Statistics and Business Analytics
2. Chapter 2: Visualizing Data with Charts and Graphs
3. Chapter 3: Descriptive Statistics
4. Chapter 4: Probability
5. Chapter 5: Discrete Distributions
6. Chapter 6: Continuous Distributions
7. Chapter 7: Sampling and Sampling Distributions
8. Chapter 8: Statistical Inference: Estimation for Single Populations
9. Chapter 9: Statistical Inference: Hypothesis Testing for Single Populations
10. Chapter 10: Statistical Inferences About Two Populations
11. Chapter 11: Analysis of Variance and Design of Experiments
12. Chapter 12: Simple Regression Analysis and Correlation
13. Chapter 13: Multiple Regression Analysis
14. Chapter 14: Building Multiple Regression Models
15. Chapter 15: Time-Series Forecasting and Index Numbers
16. Chapter 16: Analysis of Categorical Data
17. Chapter 17: Nonparametric Statistics
18. Chapter 18: Statistical Quality Control
19. Chapter 19: Decision Analysis
,BUSINESS STATISTICS FOR CONTEMPORARY DECISION MAKING 11TH EDITION
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.
,BUSINESS STATISTICS FOR CONTEMPORARY DECISION MAKING 11TH EDITION
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.
