Solutions Manual All Chapter

Business Statistics For Contemporary Decision Making 9e Ken Black

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Black / Business Statistics, 9 th edition Instructor’s Manual Copyright ©2017 John Wiley & Sons, Inc. IM 1-1

Chapter 1 Introduction to Statistics

LEARNING OBJECTIVES

The primary objective of Chapter 1 is to introduce you to the world of

statistics, thereby enabling you to:

• List quantitative and graphical examples of statistics within a business

context

• Define important statistical terms, including population, sample, and

parameter, as they relate to descriptive and inferential statistics

• Explain the difference between variables, measurement, and data.
• Compare the four different levels of data: nominal, ordinal, interval, and ratio

CHAPTER TEACHING STRATEGY

In chapter 1 it is very important to motivate business students to study statistics by presenting them with many applications of statistics in business. The definition of statistics as a science dealing with the collection, analysis, interpretation, and presentation of numerical data is a very good place to start. Statistics is about dealing with data. Data are found in all areas of business. This is a time to have the students brainstorm on the wide variety of places in business where data are measured and gathered. It is important to define statistics for students because they bring so many preconceptions of the meaning of the term. For this reason, several perceptions of the word statistics is given in the chapter.Chapter 1 sets up the paradigm of inferential statistics. The student will understand that while there are many useful applications of descriptive statistics in business, the strength of the application of statistics in the field of business is through inferential statistics. From this notion, we will later introduce probability, sampling, confidence intervals, and hypothesis testing. The process involves taking a sample from the population, computing a statistic on the sample data, and making an inference (decision or conclusion) back to the population from which the sample has been drawn.In chapter 1, levels of data measurement are emphasized. Too many texts present data to the students with no comment or discussion of how the data were gathered or the level of data measurement. In chapter 7, there is a discussion of sampling techniques.However, in this chapter, four levels of data are discussed. It is important for students to understand that the statistician is often given data to analyze without input as to how it was gathered or the type of measurement. It is incumbent upon statisticians and researchers to ascertain the level of measurement that the data represent so that appropriate techniques can be used in analysis. All techniques presented in this text cannot be appropriately used to analyze all data. 2 / 4

Black / Business Statistics, 9 th edition Instructor’s Manual Copyright ©2017 John Wiley & Sons, Inc. IM 1-2

CHAPTER OUTLINE

1.1 Basic Statistical Concepts

1.2 Data Measurement Nominal Level Ordinal Level Interval Level Ratio Level Comparison of the Four Levels of Data

Statistical Analysis Using the Computer: Excel and Minitab

KEY TERMS

Census Ordinal Level Data Data Parameter Descriptive Statistics Parametric Statistics Inferential Statistics Population Interval Level Data Ratio Level Data Measurement Sample Metric Data Statistic Nominal Level Data Statistics Nonmetric Data Variable Nonparametric Statistics

SOLUTIONS TO PROBLEMS IN CHAPTER 1

1.1 Examples of data in functional areas:

accounting - cost of goods, salary expense, depreciation, utility costs, taxes, equipment inventory, etc.

finance - World bank bond rates, number of failed savings and loans, measured risk of common stocks, stock dividends, foreign exchange rate, liquidity rates for a single-family, etc.

human resources - salaries, size of engineering staff, years experience, age of employees, years of education, etc.

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Black / Business Statistics, 9 th edition Instructor’s Manual Copyright ©2017 John Wiley & Sons, Inc. IM 1-3 marketing - number of units sold, dollar sales volume, forecast sales, size of sales force, market share, measurement of consumer motivation, measurement of consumer frustration, measurement of brand preference, attitude measurement, measurement of consumer risk, etc.

information systems - CPU time, size of memory, number of work stations, storage capacity, percent of professionals who are connected to a computer network, dollar assets of company computing, number of “hits” on the Internet, time spent on the Internet per day, percentage of people who use the Internet, retail dollars spent in e-commerce, etc.

production - number of production runs per day, weight of a product; assembly time, number of defects per run, temperature in the plant, amount of inventory, turnaround time, etc.

management - measurement of union participation, measurement of employer support, measurement of tendency to control, number of subordinates reporting to a manager, measurement of leadership style, etc.

1.2 Examples of data in business industries:

manufacturing - size of punched hole, number of rejects, amount of inventory, amount of production, number of production workers, etc.

insurance - number of claims per month, average amount of life insurance per family head, life expectancy, cost of repairs for major auto collision, average medical costs incurred for a single female over 45 years of age, etc.

travel - cost of airfare, number of miles traveled for ground transported vacations, number of nights away from home, size of traveling party, amount spent per day on besides lodging, etc.

retailing - inventory turnover ratio, sales volume, size of sales force, number of competitors within 2 miles of retail outlet, area of store, number of sales people, etc.

communications - cost per minute, number of phones per office, miles of cable per customer headquarters, minutes per day of long distance usage, number of operators, time between calls, etc.

computing - age of company hardware, cost of software, number of CAD/CAM stations, age of computer operators, measure to evaluate competing software packages, size of data base, etc.

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