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:
1. List quantitative and graphical examples of statistics within a business
context
2. Define important statistical terms, including population, sample, and
parameter, as they relate to descriptive and inferential statistics
3. Explain the difference between variables, measurement, and data.
4. 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.
CHAPTER OUTLINE
1.1 Statistics in Business
1.2 Basic Statistical Concepts
1.3 Variables and Data
1.4 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.
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.
Introduction to Statistics
LEARNING OBJECTIVES
The primary objective of Chapter 1 is to introduce you to the world of
statistics, thereby enabling you to:
1. List quantitative and graphical examples of statistics within a business
context
2. Define important statistical terms, including population, sample, and
parameter, as they relate to descriptive and inferential statistics
3. Explain the difference between variables, measurement, and data.
4. 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.
CHAPTER OUTLINE
1.1 Statistics in Business
1.2 Basic Statistical Concepts
1.3 Variables and Data
1.4 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.
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.
