Basic Statistical Ideas for Managers (with CD-ROM) / Edition 2

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Designed for the one-term MBA or undergraduate introduction to business statistics course, this text places emphasis on data and the common techniques and methods used to analyze them in business. It introduces concepts using practical examples and illustrates them with computer output from MINITAB™, Microsoft Excel, and JMP. The book integrates a business decision-making case into each chapter for motivational and illustration purposes and includes a business case assignment at the end of each chapter. These cases revolve around realistic business settings with realistic data sets that put students in the role of managers who need to make business decisions based on data. Review problems requiring students to use previously learned concepts also appear throughout to promote understanding of the relationships among statistical methods.

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Editorial Reviews

From the Publisher
"This book is excellent overall in terms of topics covered, clarity and organization, reasonable brevity (necessary for shorter courses), great examples of statistical output from a many different types of statistical software, and the degree to which it illustrates a wide range of business applications. It also serves as a great reference for students after the introductory course is over. I think it superior to its competitors on these dimensions."

"What do you view to be the three key strengths of the text? Clear and lucid explanation of key concepts. Brevity and conciseness of treatment - a systematic and structured approach to explaining the concepts well, and thereby not testing student patience! The overall organization of the text in terms of the Definition Boxes, Example Boxes, Summary of key concepts at the start of each chapter, and overall flow of topics."

"What do you view to be the three key strengths of the text? Its inclusion of a fair number of modeling problems, its math level (more mature than college algebra), and its coverage of regression."

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Product Details

  • ISBN-13: 9780534378059
  • Publisher: Cengage Learning
  • Publication date: 6/4/2004
  • Series: Available Titles CengageNOW Series
  • Edition description: REV
  • Edition number: 2
  • Pages: 792
  • Product dimensions: 10.10 (w) x 8.10 (h) x 1.40 (d)

Meet the Author

The late David Hildebrand earned his Ph.D. at Carnegie-Mellon University, and was affiliated with the Wharton School of Business at the University of Pennsylvania.

Lyman Ott earned his Bachelor's degree in Mathematics and Education and Master's degree in Mathematics from Bucknell University, and Ph.D in Statistics from the Virginia Polytechnic Institute. After two years working in statistics in the pharmaceutical industry, Dr. Ott became assistant professor in the Statistic Department at the University of Florida in 1968 and was named associate professor in 1972. He joined Merrell-National laboratories in 1975 as head of the Biostatistics Department and then head of the company's Research Data Center. He later became director of Biomedical Information Systems, Vice President of Global Systems and Quality Improvement in Research and Development, and Senior Vice President Business Process Improvement and Biometrics. He retired from the pharmaceutical industry in 1998, and now serves as consultant and Board of Advisors member for Abundance Technologies, Inc. Dr. Ott has published extensively in scientific journals and authored or co-authored seven college textbooks including Basic Statistical Ideas for Managers, Statistics: A Tool for the Social Sciences and An Introduction to Statistical Methods and Data Analysis. He has been a member of the Industrial Research Institute, the Drug Information Association and the Biometrics Society. In addition, he is a Fellow of the American Statistical Association and received the Biostatistics Career Achievement Award from the Pharmaceutical research and Manufacturers of America in 1998. He was also an All-American soccer player in college and is a member of the Bucknell University Athletic Hall of Fame.

J. Brian Gray is Professor of Statistics in the Applied Statistics Program and in the Department of Information Systems, Statistics, and Management Science at The University of Alabama, where he teaches a variety of statistics courses at the undergraduate, masters, and doctoral levels. Dr. Gray has taught MBA and Executive MBA statistics courses for the past 20 years and has received several teaching awards from the MBA and Executive MBA Associations of The University of Alabama and Texas Christian University. He co-authored the book BUSINESS CASES IN STATISTICAL DECISION-MAKING: COMPUTER BASED APPLICATIONS. Dr. Gray is an active member of the American Statistical Association. His current research interests are in the areas of applied statistics, exploratory data analysis, data mining, regression analysis, statistical computing, and graphics. He has published research articles in journals including TECHNOMETRICS, JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, THE AMERICAN STATISTICIAN, COMPUTATIONAL STATISTICS AND DATA ANALYSIS, JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, STATISTICS AND COMPUTING, JOURNAL OF REAL ESTATE RESEARCH, and JOURNAL OF BUSINESS, FINANCE, & ACCOUNTING. Dr. Gray received the Wilcoxon Prize for Best Practical Application Paper in TECHNOMETRICS in 1984.

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Table of Contents

1. MAKING SENSE OF DATA. Executive Overview. What Do We Mean by "Data?" Data About What? Gathering Data. Summarizing Data. The Role of Probability. Evaluating Other Peoples Data and Conclusions. The Role of the Computer. Summary. 2.SUMMARIZING DATA. Executive Overview. Chapter Case Introduction. The Distribution of Values of a Variable. Two-Variable Summaries. On the Average: Typical Values. Measuring Variability. Calculators and Statistical Software. Statistical Methods and Quality Improvement. Chapter Case Analyses. Summary. Supplementary Exercises. Business Cases. 3. A FIRST LOOK AT PROBABILITY. Executive Overview. Chapter Case Introduction. Basic Principles of Probability. Statistical Independence. Probability Tables, Trees, and Simulations. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases. Review Exercises: Chapters 2 and 3. 4. RANDOM VARIABLES AND PROBABILITY DISTRIBUTION. Executive Overview. Chapter Case Introduction. Random Variables: Basic Ideas. Probability Distributions: Discrete Random Variables. Expected Value, Variance, and Standard Deviation. Joint Probability Distributions and Independence. Covariance and Correlation of Random Variables. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases. 5. SOME SPECIAL PROBABILITY DISTRIBUTIONS. Executive Overview. Chapter Case Introduction. Counting Possible Outcomes. Bernoulli Trials and the Binomial Distribution. The Poisson Distribution. The Normal Distribution. Checking Normality. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases. 6. RANDOM SAMPLING AND SAMPLING DISTRUBTIONS. Executive Overview. Chapter Case Introduction. Random Sampling. Sample Statistics and Sampling Distributions. Sampling Distributions for Means and Sums. Chapter Case Analysis. Summary. Appendix: Standard Error of a Mean. Supplementary Exercises. Business Cases. Review Exercises: Chapters 4-6. 7. POINT INTERVAL ESTIMATION. Executive Overview. Chapter Case Introduction. Point Estimators. Interval Estimation of a Mean, Known Standard Deviation. Confidence Intervals for a Proportion. How Large a Sample is Needed? The t Distribution. Confidence Intervals with the t Distribution. Assumptions for Interval Estimation. Chapter Case Analysis. Summary. Supplementary Exercises. Business Case. 8. HYPOTHESIS TESTING. Executive Overview. Chapter Case Introduction. A Test for a Mean, Known Standard Deviation. Type II Error, ß Probability, and Power of a Test. The p-Value for a Hypothesis Test. Hypothesis Testing with the t Distribution. Assumptions for t Tests. Testing a Proportion: Normal Approximation. Hypothesis Tests and Confidence Intervals. Chapter Case Analysis. Summary. Supplementary Exercises. Business Case. Review Exercises: Chapters 7-8. 9. COMPARING TWO SAMPLES. Executive Overview. Chapter Case Introduction. Comparing the Means of Two Populations. A Nonparametric Test: The Wilcoxon Rank Sum Test. Paired-Sample Methods. The Signed-Rank Method. Two-Sample Procedures for Proportions. Chi-Squared Test for Count Data. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases. 10. ANALYSIS OF VARIANCE AND DESIGNED EXPERIMENTS. Executive Overview / Chapter Case Introduction / Testing the Equality of Several Population Means / Comparing Several Distributions by a Rank Test. Specific Comparisons Among Means / Two-Factor Experiments / Randomized Block Designs / Chapter Case Analysis / Summary / Supplementary Exercises / Business Cases. 11.LINEAR REGRESSION AND CORRELATION METHODS. Executive Overview. Chapter Case Introduction. The Linear Regression Model. Estimating Model Parameters. Inferences about Regression Parameters. Predicting New Y Values using Regression. Correlation. Chapter Case Analysis. Summing Up. Supplementary Exercises. Business Cases. 12. MULTIPLE REGRESSION METHODS. Executive Overview. Chapter Case Introduction. The Multiple Regression Model. Estimating Multiple Regression Coefficients. Inferences in Multiple Regression. Testing a Subset of the Regression Coefficients. Forecasting Using Multiple Regression. Chapter Case Analysis. Summing Up. Supplementary Exercises. Business Cases. 13. CONSTRUCTING A MULTIPLE REGRESSION MODEL. Executive Overview. Chapter Case Introduction. Chapter Case Analysis. Summary. Supplementary Exercises. Business Cases.

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