Statistics for Business and Economics

Citation

Newbold, P., Carlson, W. L., & Thorne, B. (2022). Statistics for Business and Economics (10th ed., Global Edition). Pearson.

Chapter Summary

The book has 17 chapters that run from description of data through probability and inference to regression, analysis of variance, forecasting and sampling. Pearson presents it as a text that lets readers carry out serious analysis of applied problems rather than run canned procedures, and as mathematically more demanding than most business statistics books.

Chapters 1–2: Describing Data: Graphical; Describing Data: Numerical

Chapter 1 covers graphical summaries of data. Chapter 2 turns to numerical measures such as the mean, median, variance and standard deviation, which condense a data set into a few numbers.

Chapters 3–5: Probability; Discrete Random Variables and Probability Distributions; Continuous Random Variables and Probability Distributions

The probability chapter sets out the rules used to reason about risk and uncertainty. The next two chapters develop discrete random variables, with the binomial and Poisson distributions as standard examples, and continuous random variables, where the normal distribution plays the central role.

Chapter 6: Sampling and Sampling Distributions

This chapter explains how samples are drawn and what the distribution of a sample statistic looks like. It is the bridge between probability and inference.

Chapters 7–8: Estimation: Single Population; Estimation: Additional Topics

Estimation of a single population parameter by point and interval estimates is followed by additional topics, including comparisons between populations.

Chapters 9–10: Hypothesis Testing: Single Population; Hypothesis Testing: Additional Topics

The framework for testing hypotheses about a single population is developed first, then extended to further tests, including those that compare several populations.

Chapters 11–13: Simple Regression; Multiple Regression; Additional Topics in Regression Analysis

Regression is the longest strand of the book: simple regression with one explanatory variable, multiple regression with several, and a third chapter of further topics such as the problems that arise in applied regression models.

Chapters 14–17: Analysis of Categorical Data; Analysis of Variance; Time-Series Analysis and Forecasting; Additional Topics in Sampling

The final chapters cover methods for categorical data, the comparison of means across groups through analysis of variance, the analysis and forecasting of time series, and more advanced sampling designs.

Key Concepts

Chapters 1–6

• Descriptive statistics: graphical and numerical summaries of a data set.
• Probability distributions: discrete and continuous models for random variables.
• Sampling distributions: the distribution of a statistic over repeated samples, the basis of inference.

Chapters 7–10

• Estimation: point and interval estimates of population parameters.
• Hypothesis testing: formal tests for one population, extended to additional settings.

Chapters 11–17

• Simple and multiple regression: models for the relationship between a response and one or several explanatory variables.
• Analysis of variance: comparing means across several groups.
• Time-series forecasting and categorical data analysis: two further sets of methods for common business data.

Critical Analysis

Strengths

• A mathematically firmer treatment. Pearson describes the book as a higher mathematical level than most business statistics texts, aimed at readers who will analyse applied problems rather than follow recipes.
• Complete inference sequence. Sampling distributions, estimation and hypothesis testing each have a main chapter and an additional-topics chapter, which gives a reader room to go beyond the basics.
• Regression gets space. Three chapters cover regression, more than any other single method in the book.
• Business and economics orientation. The title and the publisher description point to examples from business and economics throughout.

Limitations

• Not a gentle first course. Since the book is pitched above most business statistics texts, a reader with weak algebra may prefer a gentler first book.
• Method-centred table of contents. The chapter list contains no chapter on software or data handling, so readers who need to use a particular package will need separate material.
• Cumulative structure. The regression and analysis-of-variance chapters rest on the sampling, estimation and testing chapters, so selective reading is harder than in a collection of independent topics.

Where It Fits in the Course

Among the business statistics books on this list, Business Statistics: A First Course by Levine, Szabat and Stephan is an introductory text that emphasizes interpreting results and checking assumptions, with software guidance. Business Statistics by Sharpe, De Veaux and Velleman is a full-length undergraduate text that teaches data analysis through realistic business cases. Naked Statistics by Wheelan is a non-technical introduction to statistical reasoning, useful before any of the textbooks. Mathematics for Economics and Business by Jacques is a separate mathematical-preparation book.

Newbold, Carlson and Thorne is the book to choose when the course expects more mathematics than these introductory texts, or when a student has already met statistics and wants a firmer treatment of probability, inference and regression. Read the introductory book first if statistics is new.

How to Use This Book

The book is for students of business and economics who have met basic algebra and are ready for a mathematically careful treatment. Readers new to statistics may start with a gentler text and use this one as the main course book later.

A course can follow the order directly: Chapters 1–6 for description, probability and sampling; Chapters 7–10 for inference; Chapters 11–13 for regression. Analysis of variance, categorical data, time series and further sampling topics (Chapters 14–17) can be chosen according to the syllabus. Pearson lists MyLab Statistics with an eText for this edition; if the course provides it, use it for practice. Run regression examples in statistical software and compare the output with the book's results.