TEST BANK FOR BUSINESS STATISTICS AND ANALYTICS IN PRACTICE, 9 TH EDITION BY BOWERMAN (ALL CHAPTERS INCLUDED)

TEST BANK FOR BUSINESS STATISTICS AND ANALYTICS IN PRACTICE, 9 TH EDITION BY BOWERMAN (ALL CHAPTERS INCLUDED) 1. A population is a set of existing units. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-07 Describe the difference between a population and a sample. Topic: Populations, Samples, and Traditional Statistics 2. If we examine some of the population measurements, we are conducting a census of the population. FALSE A census is defined as examining all of the population measurements. AACSB: Reflective Thinking Blooms: Understand Difficulty: 2 Medium Learning Objective: 01-07 Describe the difference between a population and a sample. Topic: Populations, Samples, and Traditional Statistics 3. A random sample is selected so that every element in the population has the same chance of being included in the sample. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-09 Explain the concept of random sampling and select a random sample. Topic: Random Sampling, Three Case Studies That Illustrate Statistical Inference, and Statistical Modeling 4. An example of a quantitative variable is the manufacturer of a car. FALSE This is an example of a qualitative or categorical variable. AACSB: Reflective Thinking Blooms: Understand Difficulty: 1 Easy Learning Objective: 01-02 Describe the difference between a quantitative variable and a qualitative variable. Topic: Data 5. An example of a qualitative variable is the mileage of a car. FALSE This is an example of a quantitative variable. AACSB: Reflective Thinking Blooms: Understand Difficulty: 1 Easy Learning Objective: 01-02 Describe the difference between a quantitative variable and a qualitative variable. 1-3 Copyright © 2017 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. Topic: Data 6. Statistical inference is the science of using a sample of measurements to make generalizations about the important aspects of a population of measurements. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 2 Medium Learning Objective: 01-08 Distinguish between descriptive statistics and statistical inference. Topic: Populations, Samples, and Traditional Statistics 7. Time series data are data collected at the same time period. FALSE Time series data are collected over different time periods. AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-03 Describe the difference between cross-sectional data and time series data. Topic: Data 8. The number of sick days taken by employees in 2008 for the top 10 technology companies is an example of time series data. FALSE This is an example of cross-sectional data. Time series data are collected at different time periods. AACSB: Reflective Thinking Blooms: Understand Difficulty: 1 Easy Learning Objective: 01-03 Describe the difference between cross-sectional data and time series data. Topic: Data 9. The number of sick days per month taken by employees for the last 10 years at Apex Co. is an example of time series data. TRUE AACSB: Reflective Thinking Blooms: Understand Difficulty: 2 Medium Learning Objective: 01-03 Describe the difference between cross-sectional data and time series data. Topic: Data 10. A quantitative variable can also be referred to as a categorical variable. FALSE Qualitative variables are also known as categorical variables. 1-4 Copyright © 2017 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. AACSB: Reflective Thinking Blooms: Understand Difficulty: 1 Easy Learning Objective: 01-02 Describe the difference between a quantitative variable and a qualitative variable. Topic: Data 11. In a data set of information on college business students, an example of an element is their cumulative GPA. FALSE The element is college business students. The cumulative GPA is an example of a variable, which is a characteristic of the element college business students. AACSB: Reflective Thinking Blooms: Understand Difficulty: 2 Medium Learning Objective: 01-01 Define a variable. Topic: Data 12. In an observational study, the variable of interest is called a response variable. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-05 Identify the different types of data sources: existing data sources, experimental studies, and observational studies. Topic: Data Sources, Data Warehousing, and Big Data 13. In an experimental study, the aim is to manipulate or set the value of the response variable. FALSE In experimental studies, the aim is to manipulate the factor, which is related to the response variable. AACSB: Reflective Thinking Blooms: Understand Difficulty: 2 Medium Learning Objective: 01-05 Identify the different types of data sources: existing data sources, experimental studies, and observational studies. Topic: Data Sources, Data Warehousing, and Big Data 14. The science of describing the important aspects of a set of measures is called statistical inference. FALSE This is the definition of descriptive statistics. Statistical inference is the science of using a sample of measurements to make generalizations about the population of measurements. AACSB: Reflective Thinking Blooms: Understand Difficulty: 2 Medium Learning Objective: 01-08 Distinguish between descriptive statistics and statistical inference. Topic: Populations, Samples, and Traditional Statistics 1-5 Copyright © 2017 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. 15. It is possible to use a random sample from a population to make statistical inferences about the entire population. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-08 Distinguish between descriptive statistics and statistical inference. Topic: Random Sampling, Three Case Studies That Illustrate Statistical Inference, and Statistical Modeling 16. Processes produce outputs over time. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-09 Explain the concept of random sampling and select a random sample. Topic: Random Sampling, Three Case Studies That Illustrate Statistical Inference, and Statistical Modeling 17. Selecting many different samples and running many different tests can eventually produce a result that makes a desired conclusion be true. FALSE Using different samples and tests to produce a desired conclusion does not make the conclusion true. AACSB: Analytical Thinking Blooms: Understand Difficulty: 2 Medium Learning Objective: 01-10 Explain the basic concept of statistical (and probability) modeling. Topic: Random Sampling, Three Case Studies That Illustrate Statistical Inference, and Statistical Modeling 18. Cross-sectional data are data collected at the same point in time. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-03 Describe the difference between cross-sectional data and time series data. Topic: Data 19. Daily temperature in a local community collected over a 30-day time period is an example of cross-sectional data. FALSE Cross-sectional data are collected at the same point in time. This is an example of time series data. 1-6 Copyright © 2017 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. AACSB: Reflective Thinking Blooms: Understand Difficulty: 1 Easy Learning Objective: 01-03 Describe the difference between cross-sectional data and time series data. Topic: Data 20. Using a nonrandom sample procedure in order to support a desired conclusion is an example of an unethical statistical procedure. TRUE AACSB: Analytical Thinking Blooms: Understand Difficulty: 2 Medium Learning Objective: 01-10 Explain the basic concept of statistical (and probability) modeling. Topic: Random Sampling, Three Case Studies That Illustrate Statistical Inference, and Statistical Modeling 21. Primary data are data collected by an individual. TRUE AACSB: Reflective Thinking Blooms: Understand Difficulty: 1 Easy Learning Objective: 01-05 Identify the different types of data sources: existing data sources, experimental studies, and observational studies. Topic: Data Sources, Data Warehousing, and Big Data 22. Secondary data are data taken from an existing source. TRUE AACSB: Reflective Thinking Blooms: Understand Difficulty: 1 Easy Learning Objective: 01-05 Identify the different types of data sources: existing data sources, experimental studies, and observational studies. Topic: Data Sources, Data Warehousing, and Big Data 23. Data warehousing is defined as a process of centralized data management and retrieval. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-06 Describe the basic ideas of data warehousing and big data. Topic: Data Sources, Data Warehousing, and Big Data 24. The term big data was derived from the use of survey data. FALSE 1-7 Copyright © 2017 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. Big data is a term derived from the huge capacity of data warehouses that contain massive amounts of data. AACSB: Reflective Thinking Blooms: Remember Difficulty: 1 Easy Learning Objective: 01-06 Describe the basic ideas of data warehousing and big data. Topic: Data Sources, Data Warehousing, and Big Data 26. A common practice in selecting a sample from a large geographic area is multistage cluster sampling. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 2 Medium Learning Objective: 01-13 Describe the basic ideas of stratified random, cluster, and systematic sampling. Topic: Stratified Random, Cluster, and Systematic Sampling 27. Stratification can at times be combined with multistage cluster sampling to develop an appropriate sample. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 2 Medium Learning Objective: 01-13 Describe the basic ideas of stratified random, cluster, and systematic sampling. Topic: Stratified Random, Cluster, and Systematic Sampling 28. In systematic sampling, the first element is randomly selected from the first (N/n) elements. TRUE AACSB: Reflective Thinking Blooms: Remember Difficulty: 3 Hard Learning Objective: 01-13 Describe the basic ideas of stratified random, cluster, and systematic sampling. Topic: Stratified Random, Cluster, and Systematic Sampling 29. Sampling error can occur because of incomplete information.