Statistical Thinking for Managers

Statistical Thinking for Managers

by David K. Hildebrand, Ott

Paperback(2nd ed)

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Product Details

ISBN-13: 9780871500366
Publisher: Brooks/Cole
Publication date: 02/28/1987
Edition description: 2nd ed
Pages: 894

About 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.

Table of Contents

1. DATA What Do We Mean by "Data?" / Data About What? / How Do You Gather Data? / What Should You Do with the Data? / How Can You Evaluate Other People's Data? / The Role of the Computer / Summary
2.SUMMARIZING DATA ABOUT ONE VARIABLE The Distribution of Values of a Variable / On the Average: Typical Values / Measuring Variability / The Normal Distribution: A Preview / Calculators and Computer Software Systems / Statistical Methods and Quality Improvement / Summing Up
3.A FIRST LOOK AT PROBABILITY Basic Principles of Probability / Statistical Independence / Probability Tables, Trees, and Simulations / Summing Up / Review Exercises for

Chapters 2 and 3
4.RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Random Variable: Basic Ideas / Probability Distributions: Discrete Random Variables / Probability Distributions: Continuous Random Variables / Expected Value and Standard Deviation: Discrete Random Variables / Expected Value and Standard Deviation: Continuous Random Variables / Joint Probability Distributions and Independence / Covariance and Correlation of Random Variables / Summing Up
5.SOME SPECIAL PROBABILITY DISTRIBUTIONS Counting Possible Outcomes / Bernoulli Trials and the Binomial Distribution / The Hypergeometric Distribution / Geometric and Negative Binomial Distributions / The Poisson Distribution / The Uniform Distribution / Exponential Distribution / The Normal Distribution / Summing Up
6.RANDOM SAMPLING AND SAMPLING DISTRIBUTIONS Random Sampling / Sample Statistics and Sampling Distributions / Sampling Distributions for Means and Sums / Checking Normality / Summing Up /
Appendix: Standard Error of a Mean / ReviewExercises for

Chapters 4 to 6
7.ESTIMATION 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 / Summing Up
8.HYPOTHESIS TESTING A Test for a Mean, Known Standard Deviation / Type II Error, Beta 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 / Summing Up / Review Exercises for

Chapters 7 and 8
9.COMPARING TWO SAMPLES Comparing the Means of Two Populations / A Nonparametric Test: The Wilcoxon Rank Sum Test / Paired-Sample Methods / The Signed-Rank Method / Summing Up
10.METHODS FOR PROPORTIONS Two-Sample Procedures for Proportions / Tests for Several Proportions / Chi-Squared Tests for Count Data / Measuring Strength of Relation / Odds / Summing Up
11.ANALYSIS OF VARIANCE AND DESIGNED EXPERIMENTS Testing the Equality of Several Population Means / Comparing Several Distributions by Rank Test / Specific Comparisons Among Means / Two-Factor Experiments / Randomized Block Designs / More Complex Experiments / Summing Up / Review Exercises for

Chapters 9-11
12.LINEAR REGRESSION AND CORRELATION METHODS The Linear Regression Model / Estimating Model Parameters / Inferences About Regression Parameters / Predicting New Y Values Using Regression / Correlation / Summing Up
13.MULTIPLE REGRESSION MODELS The Multiple Regression Model / Estimating Multiple Regression Coefficients / Inferences in Multiple Regression / Testing a Subset of the Regression Coefficients / Inferences in Multiple Regression / Testing a Subset of the Regression Coefficients / Forecasting Using Multiple Regression / Summing Up /
Appendix: Some Multiple Regression Theory
14.CONSTRUCTING A MULTIPLE REGRESSION MODEL Selecting Possible Independent Variables (Step 1) / Using Qualitative Predictors: Dummy Variables (Step 1) / Lagged Predictor Variables (Step 1) / Nonlinearity and Interaction (Step 2) / Choosing Among Regression Models (Step 3) / Residuals Analysis (Step 4) / Autoregression (Step 4) / Model Validation / Summing / Review Exercises for

Chapters 12-14
Index Numbers / The Classical, Cyclic, and Seasonal Approach / Smoothing Methods / The ARIMA Approach / Summing Up
16.SOME ALTERNATIVE SAMPLING METHODS Taking a Simple Random Sample / Stratified Random Sampling / Cluster Sampling / Selecting the Sample Size / Other Sampling Techniques / Summing Up
17.DATA MANAGEMENT AND REPORT PREPARATION Preparing Data for Statistical Analysis Guidelines for a Statistical Analysis and Report / Documentation and Storage of Results / SUMMARY

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