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Statistics for Business and Financial Economics

Authors: Cheng-Few Lee, John C. Lee, Alice C. Lee

Publisher: Springer New York

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft" , Springer Professional "Technik"

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About this book

Statistics for Business and Financial Economics, 3rd edition is the definitive Business Statistics book to use Finance, Economics, and Accounting data throughout the entire book. Therefore, this book gives students an understanding of how to apply the methodology of statistics to real world situations. In particular, this book shows how descriptive statistics, probability, statistical distributions, statistical inference, regression methods, and statistical decision theory can be used to analyze individual stock price, stock index, stock rate of return, market rate of return, and decision making. In addition, this book also shows how time-series analysis and the statistical decision theory method can be used to analyze accounting and financial data. In this fully-revised edition, the real world examples have been reconfigured and sections have been edited for better understanding of the topics.

On the Springer page for the book, the solution manual, test bank and powerpoints are available for download.

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Table of Contents

Frontmatter

Introduction and Descriptive Statistics

Frontmatter

Chapter 1. Introduction

Abstract

Statistics is a body of knowledge that is useful for collecting, organizing, presenting, analyzing, and interpreting data (collections of any number of related observations) and numerical facts. Applied statistical analysis helps business managers and economic planners formulate management policy and make business decisions more effectively. And statistics is an important tool for students of business and economics. Indeed, business and economic statistics has become one of the most important courses in business education, because a background in applied statistics is a key ingredient in understanding accounting, economics, finance, marketing, production, organizational behavior, and other business courses.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 2. Data Collection and Presentation

Abstract

The collection, organization, and presentation of data are basic background material for learning descriptive and inferential statistics and their applications. In this chapter, we first discuss sources of data and methods of collecting them. Then we explore in detail the presentation of data in tables and graphs. Finally, we use both accounting and financial data to show how the statistical techniques discussed in this chapter can be used to analyze the financial condition of a firm and to analyze the recent deterioration of the financial health of the US banking industry. In addition, we use a pie chart to examine how Congress voted on the Gulf Resolution in 1991.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 3. Frequency Distributions and Data Analyses

Abstract

Using the tabular and graphical methods discussed in Chap. 2, we will now develop two general ways to describe data more fully. We discuss first the tally table approach to depicting data frequency distributions and then three other kinds of frequency tables. Next, we explore alternative graphical methods for describing frequency distributions. Finally, we study further applications for frequency distributions in business and economics.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 4. Numerical Summary Measures

Abstract

In this chapter, we extend the graphical descriptive method in data analysis by examining measures of central tendency, dispersion, position, and shape. All these numerical summary measures are important because they enable us to describe a set of data with only a small number of summary statistics. One use of these summary statistics is to compare individual observations from a data set. For example, a student in a statistics class could use one measure of central tendency, the class average, or mean, to determine how well her performance stacks up to the rest of the class. Measures of central tendency can also be used to compare two different sets of data. For example, a statistics teacher interested in comparing the performances of two different statistics classes could take the average, or mean, for each class and compare the two.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Probability and Important Distributions

Frontmatter

Chapter 5. Probability Concepts and Their Analysis

Abstract

In Part I of this book, we discussed the use of descriptive statistics, which is concerned mainly with organizing and describing a set of sample measurements via graphical and numerical descriptive methods. We now begin to consider the problem of making inferences about a population from sample data. Probability and the theory that surrounds it are discussed in this chapter. These topics provide an essential foundation for the methods of making inferences about a population on the basis of a sample. A well-known example is the election poll, in which pollsters select at random a small number of voters to question in order to predict the winner of an election. Probability is also used in daily decision making. For example, investment decisions are based on the investor’s assessment of the probable future returns of various investment opportunities, and such assessments are often based on some sample information.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 6. Discrete Random Variables and Probability Distributions

Abstract

In Chaps. 2, 3, and 4, we explored descriptive statistical measures, and we examined probability concepts and techniques in Chap. 5. Here we will build on this foundation as we establish the definitions of discrete and continuous random variables and discuss important discrete probability distributions in terms of specific numerical outcomes.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 7. The Normal and Lognormal Distributions

Abstract

In Chap. 6, we discussed discrete random variables and their distributions. Particularly, we focused on the means and variances of binomial, hypergeometric, and Poisson distributions. Although the distributions derived from these discrete random variables are useful, they are limited. And therefore, statisticians have derived several important continuous distributions to substitute for and/or complement the discrete distributions. The normal distribution is the first important continuous distribution discussed in this chapter. Examples of continuous random variables include the number of miles a car travels on 1 gal of gas and the exact weight of a box of cereal.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 8. Sampling and Sampling Distributions

Abstract

In this chapter, we take an in-depth look at the operational end of statistical analysis. Statistical analysis primarily involves selecting parts of populations (known as samples) and analyzing them in order to make inferences about the populations. Inferences made about a population by using sample data are widespread in business, economics, and finance. For example, the A. C. Nielsen Company infers the number of people who watch each television show on the basis of a sample of TV viewers. The use of political polls to project election winners is another example of statistical inference. And when you fill out a warranty card on an appliance you have bought, you are often asked to provide information about yourself that the warrantor compiles (and probably sells to someone who will later try to convince you to buy a magazine subscription). These data are also sample data.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 9. Other Continuous Distributions and Moments for Distributions

Abstract

Two very useful continuous distributions, the normal and lognormal distributions, were discussed in Chap. 7. Because many random variables have distributions that are not normal, in this chapter, we explore five other important continuous distributions and their applications. These five distributions are the uniform distribution, Student’s t distribution, the chi-square distribution, the F distribution, and the exponential distribution. All are directly or indirectly used in analyzing business and economic data. The relationship between moments and distributions is also discussed in this chapter. Finally, we explore business applications of statistical distributions in terms of the first four moments for stock rates of return.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Statistical Inferences Based on Samples

Frontmatter

Chapter 10. Estimation and Statistical Quality Control

Abstract

In the previous two chapters, we discussed the basic principles of sampling and sampling distributions – techniques that enable us to make inferences about a population by looking at a subset of that population. In this chapter, we continue our discussion of inferential statistics by examining point estimation, confidence intervals, and statistical quality control. Note that this chapter draws heavily on your understanding of the standard normal distribution discussed in Chap. 7, the fundamental concepts of sampling discussed in Chap. 8, and the t distribution and chi-square distribution discussed in Chap. 9.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 11. Hypothesis Testing

Abstract

Business managers must always be ready to make decisions and take action on the basis of available information. During the process of decision making, managers form hypotheses that they can scientifically test by using that available information. They then make decisions in the light of the outcome. In this chapter, we use the concepts of point estimate and interval estimate discussed in Chaps. 8, 9, and 10 to test hypotheses made about population parameters on the basis of sample data.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 12. Analysis of Variance and Chi-Square Tests

Abstract

Both χ ² and F distributions and their related testing statistics have been discussed in detail in the last three chapters. In this chapter, we will talk about how these two distributions can be used to do data analysis involving the means or the proportions of more than two populations. In other words, we will develop an understanding of (1) a technique known as analysis of variance (ANOVA), which enables us to test the significance of the differences among sample means in terms of an F distribution and (2) tests of goodness of fit and independence in an x ² distribution. The ANOVA is used to test the equality of more than two population means. The goodness-of-fit test is used to test the equality of more than two population proportions or to assess the appropriateness of a distribution. The test of independence determines whether the differences among several sample proportions are significant or are instead likely to be due to chance alone.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Regression and Correlation: Relating Two or More Variables

Frontmatter

Chapter 13. Simple Linear Regression and the Correlation Coefficient

Abstract

In Sect. 6.9, we used correlation to provide a measure of the strength of any linear relationship between a pair of random variables X and Y. The random variables are treated perfectly symmetrically; that is, “the correlation between X and Y” is equivalent to “the correlation between Y and X.” In this chapter, we first discuss the linear relationship between a pair of variables without perfect symmetry. In other words, we assume that Y is a dependent variable and X an independent variable: Y depends on X. Then we discuss the bivariate normal relationship and concepts related to the correlation coefficient.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 14. Simple Linear Regression and Correlation: Analyses and Applications

Abstract

This chapter clarifies and expands on the material presented in Chap. 13 by providing calculations, analyses, and applications to business and economics.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 15. Multiple Linear Regression

Abstract

Chapters 13 and 14 examined in detail the simple regression model with one independent variable (such as amount of fertilizer) and one dependent variable (such as yield of corn). In many cases, however, more than one factor can affect the outcome under study. In addition to fertilizer, rainfall and temperature certainly influence the yield of corn. In business, not only rates of return for the stock market at large affect the return on General Motors or Ford stock. Other variables, such as leverage ratio, payout ratio, and dividend yield also contribute. Therefore, regression analysis with more than one independent variable is an important analytical tool.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 16. Other Topics in Applied Regression Analysis

Abstract

In Chaps. 13, 14, and 15, we discussed in some detail the technique of regression analysis and its applications. The main objectives in fitting a regression equation are (1) to estimate the regression coefficients and related parameters and (2) to predict the value of the dependent variable in terms of that of the independent variable (or variables). Several alternative specifications are possible in this kind of applied regression analysis, and a number of problems may occur.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Selected Topics in Statistical Analysis for Business and Economics

Frontmatter

Chapter 17. Nonparametric Statistics

Abstract

In previous chapters, we discussed alternative tests of hypotheses. These tests were generally concerned with statistical measures such as the mean, variance, or proportion of a population. A mean, variance, or proportion is referred to as a parameter in statistics. To test these parameters, we generally assume that the sample observations were drawn from a normally distributed population. The assumption of normality is especially critical when the sample size is small. Tests such as the Z, t, and F tests discussed in Chap. 11 depend on assumptions about the parameters of the population, so all these tests are parametric tests or classical tests. A parametric test is generally a test based on a parametric model.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 18. Time Series: Analysis, Model, and Forecasting

Abstract

In the first 17 chapters of this book, we used both time-series and cross-sectional data to show how statistical analysis techniques can be used in economic and business decision making.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 19. Index Numbers and Stock Market Indexes

Abstract

Business executives and government officials often make judgments that involve summarizing how business, economic, and financial variables change with time or place. Examples of variation over time include variation in gross national production, variation in the price of consumer goods, and variation in stock market prices, As an example of variation with changes in place, consider a company that wishes to transfer an executive from Chicago to San Francisco. What should be the executive’s minimum salary increase to compensate for the higher cost of living in San Francisco?

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 20. Sampling Surveys: Methods and Applications

Abstract

In statistics, we are interested in information about a population. For example, we might be interested in how the residents of a community feel about the construction of a new high school. There are two ways to obtain information about how the residents feel about this issue. We could take a census and simply ask each and every resident about his or her attitude toward such a project. Or we could take a smaller sample of the residents and try to draw inferences about the community’s feelings from the feelings that members of this sample express.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Chapter 21. Statistical Decision Theory: Methods and Applications

Abstract

In business, decision making is at the heart of management. Using statistics as a guide, this chapter introduces and examines decision making in business and economics in terms of statistical decision theory. The branch of statistics called statistical decision theory is sometimes termed Bayesian decision statistics, in honor of research presented over 200 years ago by the English philosopher the Reverend Thomas Bayes (1702–1761). Nevertheless, statistical decision theory is a new branch of statistics. Propelled by research by Howard Raiffa, John Pratt, and Leonard Savage (among others), it developed rapidly in the 1950s, and it now occupies an important place in statistical literature. In contrast to classical statistics, where the focus is on estimation, constructing intervals, and hypothesis testing, statistical decision theory focuses on the process of making a decision. In other words, it is concerned with the situation in which an individual, group, or corporation has several feasible alternative courses of action in an uncertain environment.

Cheng-Few Lee, John C. Lee, Alice C. Lee

Backmatter

Title: Statistics for Business and Financial Economics
Authors: Cheng-Few Lee
John C. Lee
Alice C. Lee
Copyright Year: 2013
Publisher: Springer New York
Electronic ISBN: 978-1-4614-5897-5
Print ISBN: 978-1-4614-5896-8
DOI: https://doi.org/10.1007/978-1-4614-5897-5