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A Guide to Business Statistics

A Guide to Business Statistics

David McEvoy

ISBN: 978-1-119-13837-2

Apr 2018

194 pages


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An accessible text that covers the fundamental and empirical statistical concepts designed for use in the fields of business and economics

A Guide to Business Statistics offers a practical approach to statistics that encompasses the fundamental statistics concepts that are most applicable for the fields of business and economics. The text is filled with clear and insightful explanations of the core principles and techniques in statistics that do not rely on mathematical rigor.

The author—a noted expert in the field—offers concise and straightforward information with explanations of the fundamental statistical concepts that are designed to be accessible to a variety of readers with backgrounds in business and economics. Filled with illustrative examples, the book also features a brief summary of the key elements at the end of each chapter in order to reinforce the concepts presented. To enhance learning, the mathematical formulae and notation appears in technical appendices at the end of each chapter and the book features a glossary of terms.  This important resource:

  • Offers a comprehensive guide to business statistics targeting business and economics students and professionals
  • Includes easy-to-read mathematical formulae and notation in a technical appendix
  • Contains a summary of the key elements at the end of each chapter that help to reinforce the concepts
  • Features coverage of probability distributions, confidence intervals, hypothesis tests, simple linear regression, model building, time series and forecasting, and nonparametric statistics

Written for undergraduate business students, business and economics majors, teachers, and practitioners, A Guide to Business Statistics offers a basic guide to the key concepts and fundamental principles of statistics designed for business and economics.

David M. McEvoy, PhD,is Associate Professor in the Economics Department at Appalachian State University. He is also the coeditor of two books and the author of 10 journal articles, as well as recipient of the Excellence in Teaching Award from the Walker College of Business at the Appalachian State University in 2010. 

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0.1. Addressing two challenges

0.2. How to use this book

0.3. Target audience

Chapter 1: Types of Data

1.1. Categorical data

1.2. Numerical data

1.3. Level of measurement

1.4. Cross-sectional, time-series and panel data

1.5. Summary

Chapter 2: Populations and Samples

2.1. What is the population of interest?

2.2. How to sample from a population?

2.3. Getting the data

2.4. Summary

Chapter 3: Descriptive Statistics

3.1. Measures of central tendency

3.2. Measures of variability

3.3. The shape

3.4. Summary

Technical Appendix

Chapter 4: Probability

4.1. Simple probabilities

4.2. Empirical probabilities

4.3. Conditional probabilities

4.4. Summary

Technical Appendix

Chapter 5: The Normal Distribution

5.1. The bell shape

5.2. The Empirical Rule

5.3. Standard normal distribution

5.4. Normal approximations

5.5. Summary

Technical Appendix

Chapter 6: Sampling Distributions

6.1. Defining a sampling distribution

6.2. The importance of sampling distributions

6.3. An example of a sampling distribution

6.4. Characteristics of a sampling distribution of a mean

6.5. Sampling distribution of a proportion

6.6. Summary

Technical Appendix

Chapter 7: Confidence Intervals

7.1. Confidence intervals for means

7.2. Confidence intervals for proportions

7.3. Sample size and the width of confidence intervals

7.4. Comparing two proportions from the same poll

7.5. Summary

Technical Appendix

Chapter 8: Hypothesis Tests of a Population Mean

8.1. Two-tail hypothesis test of a mean

8.2. One-tail hypothesis test of a mean

8.3. p-value approach to hypothesis tests

8.4. Summary

Technical Appendix

Chapter 9: Hypothesis Tests of Categorical Data

9.1. Two-tail hypothesis test of a proportion

9.2. One-tail hypothesis test of a proportion

9.3. Using p-values

9.4. Chi-square tests

9.5. Summary

Technical Appendix

Chapter 10: Hypothesis Tests Comparing Two Parameters

10.1. The approach in this chapter

10.2. Hypothesis tests of two means

10.3. Hypothesis tests of two variances

10.4. Hypothesis tests of two proportions

10.5. Summary

Technical Appendix

Chapter 11: Simple Linear Regression

11.1. The population regression model

11.2. A look at the data

11.3. Ordinary Least Squares (OLS)

11.4. The distribution of b0 and b1

11.5. Tests of significance

11.6. Goodness of fit

11.7. Checking for violations of the assumptions

11.8. Summary

Technical Appendix

Chapter 12: Multiple Regression

12.1. Population regression model

12.2. The data

12.3. Sample regression function

12.4. Interpreting the estimates

12.5. Prediction

12.6. Tests of significance

12.7. Goodness of fit

12.8. Multicollinearity

12.9. Summary

Technical Appendix

Chapter 13: More Topics in Regression

13.1. Hypothesis tests comparing two means with regression

13.2. Hypothesis tests comparing more than two means (ANOVA)

13.3. Interacting variables

13.4. Non-linearities

13.5. Time-series analysis

13.6. Summary