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Business Statistics: A First Course / Edition 5

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This book covers the concepts and applications of statistics used in the functional areas of business-accounting, marketing, management, and economics, and finance. Focused more on concepts than on statistical methods, it shows readers how to properly use statistics to analyze data and demonstrates how computer software is an integral part of this analysis. "Using Statistics" scenarios discuss how statistics is used in a real business setting. Includes contemporary business applications, many with real data sets, and an integrated case that runs throughout chapters. "PHSTAT," a custom designed Excel add-in, is packaged with each book. Introduction and Data Collection. Presenting Data in Tables and Charts. Summarizing and Describing Numerical Data. Basic Probability and Probability Distributions. Sampling Distributions and Confidence Interval Estimation. Fundamentals of Hypothesis Testing: One-Sample Tests. Two-Sample and C-Sample Tests with Numerical Data. Hypothesis Testing with Categorical Data. Statistical Applications in Quality and Productivity Management. The Simple Linear Regression Model and Correlations. Introduction to Multiple Regression. Time Series Analysis. An accessible introduction or refresher on statistics for those in accounting, marketing, management, economics, and finance.
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Editorial Reviews

This textbook introduces statistical methods for analyzing data in applied contexts related to accounting, economics, finance, management, and marketing. The primary methods covered involve probability, hypothesis testing, simple linear regression, and multiple regression. The third edition adds sections on preparing reports with Microsoft Office tools. The CD-ROM contains the PHStat2 add-in for Excel and data files. Annotation c. Book News, Inc., Portland, OR (
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Product Details

  • ISBN-13: 9780136065807
  • Publisher: Pearson
  • Publication date: 1/22/2009
  • Series: MyStatLab Series
  • Edition description: Older Edition
  • Edition number: 5
  • Pages: 624
  • Product dimensions: 8.40 (w) x 10.70 (h) x 1.10 (d)

Meet the Author

David M. Levine, Kathryn A. Szabat, and David F. Stephan are all experienced business school educators committed to innovation and improving instruction in business statistics and related subjects.

David Levine, Professor Emeritus of Statistics and CIS at Baruch College, CUNY is a nationally recognized innovator in statistics education for more than three decades. Levine has coauthored 14 books, including several business statistics textbooks; textbooks and professional titles that explain and explore quality management and the Six Sigma approach; and, with David Stephan, a trade paperback that explains statistical concepts to a general audience. Levine has presented or chaired numerous sessions about business education at leading conferences conducted by the Decision Sciences Institute (DSI) and the American Statistical Association, and he and his coauthors have been active participants in the annual DSI Making Statistics More Effective in Schools and Business (MSMESB) mini-conference. During his many years teaching at Baruch College, Levine was recognized for his contributions to teaching and curriculum development with the College’s highest distinguished teaching honor. He earned B.B.A. and M.B.A. degrees from CCNY. and a Ph.D. in industrial engineering and operations research from New York University.

As Associate Professor and Chair of Business Systems and Analytics at La Salle University, Kathryn Szabat has transformed several business school majors into one interdisciplinary major that better supports careers in new and emerging disciplines of data analysis including analytics. Szabat strives to inspire, stimulate, challenge, and motivate students through innovation and curricular enhancements, and shares her coauthors’ commitment to teaching excellence and the continual improvement of statistics presentations. Beyond the classroom she has provided statistical advice to numerous business, nonbusiness, and academic communities, with particular interest in the areas of education, medicine, and nonprofit capacity building. Her research activities have led to journal publications, chapters in scholarly books, and conference presentations. Szabat is a member of the American Statistical Association (ASA), DSI, Institute for Operation Research and Management Sciences (INFORMS), and DSI MSMESB. She received a B.S. from SUNY-Albany, an M.S. in statistics from the Wharton School of the University of Pennsylvania, and a Ph.D. degree in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania.

Advances in computing have always shaped David Stephan’s professional life. As an undergraduate, he helped professors use statistics software that was considered advanced even though it could compute only several things discussed in Chapter 3, thereby gaining an early appreciation for the benefits of using software to solve problems (and perhaps positively influencing his grades). An early advocate of using computers to support instruction, he developed a prototype of a mainframe-based system that anticipated features found today in Pearson’s MathXL and served as special assistant for computing to the Dean and Provost at Baruch College. In his many years teaching at Baruch, Stephan implemented the first computer-based classroom, helped redevelop the CIS curriculum, and, as part of a FIPSE project team, designed and implemented a multimedia learning environment. He was also nominated for teaching honors. Stephan has presented at the SEDSI conference and the DSI MSMESB mini-conferences, sometimes with his coauthors. Stephan earned a B.A. from Franklin & Marshall College and an M.S. from Baruch College, CUNY, and he studied instructional technology at Teachers College, Columbia University.

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Table of Contents

1. Introduction and Data Collection.
Why a Manager Needs to Know About Statistics. The Growth and Development of Modern Statistics. Statistical Thinking and Modern Management. Descriptive Versus Inferential Statistics. Why Do We Need Data? Types of Data. Types of Sampling Methods. Evaluating Survey Worthiness. Summary.
Appendix 1.1 Basics of the Windows User Interface. Appendix 1.2 Introduction to Microsoft Excel. Appendix 1.3 Introduction to Minitab. Case Study: Alumni Association Survey.

2. Presenting Data in Tables and Charts.
Organizing Numerical Data. Tables and Charts for Numerical Data. Tables and Charts for Categorical Data. Tabulating and Graphing Bivariate Categorical Data. Graphical Excellence. Summary. Springville Herald Case.
Appendix 2.1. Using Microsoft Excel for Tables and Charts. Appendix 2.2. Using Minitab for Tables and Charts.

3. Summarizing and Describing Numerical Data.
Exploring Numerical Data and Its Properties. Measures of Central Tendency, Variation, and Shape. Exploratory Data Analysis. Obtaining Descriptive Summary Measures from a Population. Recognizing and Practicing Proper Descriptive Summarization and Exploring Ethical Issues. Summary. Case Study: State Alcoholic Beverages Oversight Board Study on Beers. Springville Herald Case.
Appendix 3.1. Using Microsoft Excel for Descriptive Statistics. Appendix 3.2. Using Minitab for Descriptive Statistics.

4. Basic Probability and Probability Distributions.
Basic Probability Concepts. Conditional Probability. Bayes Theorem. The Probability Distribution for a Discrete Random Variable. Binomial Distribution. Poisson Distribution. The Normal Distribution. Assessing the Normality Assumption. The Covariance and its Application in Finance (Optional section). Ethical Issues and Probability. Summary. Springville Herald Case.
Appendix 4.1. Using Microsoft Excel with Probability and Probability Distributions. Appendix 4.2. Using Minitab with Probability Distributions.

5. Sampling Distributions and Confidence Interval Estimation.
Introduction to Sampling Distributions. Introduction to Confidence Interval Estimation. Confidence Interval Estimation for the Mean ( Known). Confidence Interval Estimation for the Mean ( Unknown). Confidence Interval Estimation for the Proportion. Determining Sample Size. Confidence Interval Estimation and Ethical Issues. Summary. Springville Herald Case.
Appendix 5.1. Using Microsoft Excel for Sampling Distributions and Confidence Interval Estimation. Appendix 5.2. Using Minitab for Sampling Distributions and Confidence Interval Estimation.

6. Fundamentals of Hypothesis Testing: One-Sample Tests.
Hypothesis-Testing Methodology. Z Test of Hypothesis for the Mean ( Known). The p-Value Approach to Hypothesis Testing. A Connection between Confidence Interval Estimation and Hypothesis Testing. One-Tailed Tests. t Test of Hypothesis for the Mean ( Unknown). Z Test of Hypothesis for the Proportion. Potential Hypothesis-Testing Pitfalls and Ethical Issues. Summary.
Appendix 6.1. Using Microsoft Excel for One-Sample Tests of Hypothesis. Appendix 6.2. Using Minitab for One-Sample Tests of Hypothesis.

7. Two-Sample and C-Sample Tests with Numerical Data.
Comparing Two Independent Samples: Tests for Differences in Two Means. Comparing Two Related Samples: Tests for the Mean Difference. F Test for Differences in Two Variances. One-Way ANOVA F Test for the Difference in c means. Summary.
Appendix 7.1. Using Microsoft Excel for Two-Sample and c-Sample Tests. Appendix 7.2. Using Minitab for Two-Sample c-Sample Tests. Case Study: Test Marketing and Promoting a Ball-Point Pen. Springville Herald Case.

8. Hypothesis Testing with Categorical Data.
Introduction. Z Test for Differences in Two Proportions. Test for Differences in Two Proportions. Test for Differences in c Proportions. Test of Independence. Summary. Case Study: Airline Satisfaction Survey. Springville Herald Case.
Appendix 8.1. Using Microsoft Excel for Hypothesis Testing with Categorical Data. Appendix 8.2. Using Minitab for Hypothesis Testing with Categorical Data.

9. The Simple Linear Regression Model and Correlation.
Types of Regression Models. Determining the Simple Linear Regression Equation. Measures of Variation in Regression and Correlation. Assumptions of Regression and Correlation. Residual Analysis. Inferences about the Slope. Estimation of Predicted Values. Pitfalls in Regression and Ethical Issues. Computations in Simple Linear Regression. Correlation-Measuring the Strength of the Association. Summary. Case Study: Predicting Sunday Newspaper Circulation. Springville Herald Case.
Appendix 9.1. Using Microsoft Excel for Simple Linear Regression and Correlation. Appendix 9.2. Using Minitab for Simple Linear Regression and Correlation.

10. Multiple Regression.
Developing the Multiple Regression Model. Residual Analysis for the Multiple Regression Model. Testing for the Significance of the Multiple Regression Model. Inferences concerning the Population Regression Coefficients. Testing portions of the Multiple Regression Model. The Curvilinear Regression Model. Dummy-Variable Models. Model Building. Summary. Case Study A: EastSideWestSide Movers. Case Study B: The Mountain States Potato Company.
Appendix 10.1. Using Microsoft Excel for Multiple Linear Regression and Correlation. Appendix 10.2. Using Minitab for Multiple Linear Regression and Correlation.

11. Time Series Analysis.
The Importance of Business Forecasting. Component Factors of the Classical Multiplicative Time-Series Model. Smoothing the Annual Time-Series. Least-Squares Trend Fitting and Forecasting. Autoregressive Modeling for Trend Fitting and Forecasting. Choosing an Appropriate Forecasting Model. Time-Series Forecasting of Monthly or Quarterly data. Pitfalls Concerning Time-Series Analysis and Ethical Considerations. Summary. Case Study Currency Trading. Springville Herald Case.
Appendix 11.1. Using Microsoft Excel for Time Series Analysis. Appendix 11.2. Using Minitab for Time Series Analysis.

12. Statistical Applications in Quality and Productivity Management.
Quality and Productivity: A Historical Perspective. Deming's Fourteen Points—A Theory of Management by Process. The Theory of Control Charts. Control Chart for the Proportion of Nonconforming Items—The p Chart. The Red Bead Experiment: Understanding Process Variability. Control Charts for the Mean ( ) and the Range (R ). Process Capability. Summary. Case Study: The Harnswell Sewing Machine Company Case. Springville Herald Case.
Appendix 12.1. Using Microsoft Excel for Control Charts. Appendix 12.2. Using Minitab for Control Charts.

Appendix A. Review of Arithmetic and Algebra.
Appendix B. Summation Notation.
Appendix C. Statistical Symbols and Greek Alphabet.
Appendix D. Documentation for Data Files.
Appendix E. Tables.
Appendix F. Installing the PHStat Add-In.
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