LEARNING OBJECTIVES - LEARNING OBJECTIVES The primary objective of Ch...

Chapter 1 Introduction to Statistics and Business Analytics

LEARNING OBJECTIVES

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

Statistics and analytics, 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
• Define important business analytics terms including big data, business analytics,

data mining, and data visualization.

• List the four dimensions of big data and explain the differences between them.
• Compare and contrast the three categories of business analytics.

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 especially in this era of big data and business analytics. 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 Business Statistics For Contemporary Decision Making, 10e Ken Black (Solution Manual, For Complete File, Download link at the end of this File) 1 / 4

Chapter 1: Introduction to Statistics 2

was gathered or the type of measurement. It is incumbent upon statisticians and analysts 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 applied to analyze all different levels of data.In addition, chapter 1 introduces the student to the concepts of big data, business analytics, data mining, and data visualization. Throughout the text reference will be made to business analytics, big data, and data visualization. It is a really nice segue to discuss big data in conjunction with levels of data measurement. In this chapter, the student will be exposed to the four characteristics of big data: variety, velocity, veracity, and volume. From this they will perhaps understand that raw data, as part of data mining, needs to be extracted, transformed or cleaned, loaded into databases such that it can be in a form that is usable and useful for business statisticians and analysts.In this chapter, the student will be introduced to the three dimensions of business analytics. Currently, most everything in the first business statistics course falls under descriptive business analytics including hypothesis testing and correlation. The idea is that business analysts want to understand the data, its characteristics, its applications, and even its relatedness with other data. A second dimension of business analytics is predictive analytics within which fall regression and forecasting techniques. The third dimension is prescriptive analytics which offer businesses the best options among various alternatives give particular circumstances.Through the introduction to data visualization as part of descriptive analytics, the student may more fully understand the importance of chapter 2 which presents techniques for visualizing data with chapters and graphs.

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

1.3 Introduction to Business Analytics Big Data Business Analytics Categories of Business Analytics Descriptive Analytics Predictive Analytics Prescriptive Analytics Data Mining Data Visualization Statistical Analysis Using the Computer: Excel, Minitab, and Tableau 2 / 4

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KEY TERMS

Big Data Ordinal Level Data Business Analytics Parameter Census Parametric Statistics Data Population Data Mining Predictive Analytics Data Visualization Prescriptive Analytics Descriptive Analytics Ratio Level Data Descriptive Statistics Sample Inferential Statistics Statistic Interval Level Data Statistics Measurement Variable Metric Data Variety Nominal Level Data Velocity Nonmetric Data Veracity Nonparametric Statistics Volume

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.

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.

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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.

agriculture - number of farms per county, farm income, number of acres of corn per farm, wholesale price of a gallon of milk, number of livestock, grain storage capacity, etc.

banking - size of deposit, number of failed banks, amount loaned to foreign banks, number of tellers per drive-in facility, average amount of withdrawal from automatic teller machine, federal reserve discount rate, etc.

healthcare - number of patients per physician per day, average cost of hospital stay, average daily census of hospital, time spent waiting to see a physician, patient satisfaction, number of blood tests done per week.

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