Introduction to Business Statistics (Book Only) / Edition 7 available in Hardcover
This manual contains worked out solutions to the odd-numbered problems in the text.
|Edition description:||New Edition|
|Product dimensions:||6.50(w) x 1.50(h) x 9.50(d)|
About the Author
Dr. Ron Weiers is an award-winning teacher and textbook author in the fields of business statistics and marketing research. He holds a passion for "making complicated things understandable," which is evident in the clear, conversational writing style found in his INTRODUCTION TO BUSINESS STATISTICS. Dr. Weiers is a recipient of the Indiana University of Pennsylvania Distinguished Faculty Award for Teaching. He is an adjunct professor at the H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, and is Professor Emeritus at the Eberly College of Business and Information Technology, Indiana University of Pennsylvania. Dr. Weiers has served as a marketing, technical and automotive consultant to organizations such as the Coleman Company, the U.S. Department of Energy, and the Society of Automotive Engineers. He has authored 8 automotive books on topics ranging from repair and maintenance to fuel efficiency and safety. Dr. Weiers has provided research and advisory services to the U.S. Department of Energy, National Highway Traffic Administration, and National Public Services Research Institute. He has developed Public Affairs Programs on Urban Transportation, Fuel Efficiency, Vehicle Safety, and Exhaust Emissions for the U.S. Headquarters of the Society of Automotive Engineers, and has authored an SAE Public Affairs Report on Automotive Noise Pollution. Dr. Weiers earned his B.S. in Industrial Engineering at the University of Pittsburgh and his S.M. in Industrial Management from the Sloan School of Management at the Massachusetts Institute of Technology. He later received his Ph.D. in Marketing Research and Analysis from the University of Pittsburgh. Dr. Weiers is a member of several professional organizations, including the American Marketing Association, the American Statistical Association, the Decision Sciences Institute, and the Society of Automotive Engineers.
Table of Contents1. A PREVIEW OF BUSINESS STATISTICS. Introduction. Statistics: Yesterday and Today. Descriptive Versus Inferential Statistics. Types of Variables and Scales of Measurement. Statistics in Business Decisions. Business Statistics: Tools Versus Tricks. Summary. 2. VISUAL DESCRIPTION OF DATA. Introduction. The Data Array and the Frequency Distribution. The Stem and Leaf Display and the Dotplot. Visual Representation of the Data. The Scatter Diagram. Tabulation, Contingency Tables, and the Excel PivotTable Wizard. Summary. 3. STATISTICAL DESCRIPTION OF DATA. Introduction. Statistical Description: Measures of Central Tendency. Statistical Description: Measures of Dispersion. Additional Dispersion Topics. Descriptive Statistics from Grouped Data. Statistical Measures of Association. Summary. 4. DATA COLLECTION AND SAMPLING METHODS. Introduction. Research Basics. Survey Research. Experimentation and Observational Research. Secondary Data. The Basics of Sampling. Sampling Methods. Summary. 5. PROBABILITY: REVIEW OF BASIC CONCEPTS Introduction. Probability: Terms and Approaches. Unions and Intersections of Events. Addition Rules for Probability. Multiplication Rules for Probability. Bayes' Theorem and the Revision of Probabilities. Counting: Permutations and Combinations. Summary. 6. DISCRETE PROBABILITY DISTRIBUTIONS. Introduction. The Binomial Distribution. The Poisson Distribution. Simulating Observations from a Discrete Probability Distribution. Summary. 7. CONTINUOUS PROBABILITY DISTRIBUTIONS. Introduction. The Normal Distribution. The Standard Normal Distribution. The Normal Approximation to the Binomial Distribution. The Exponential Distribution. SimulatingObservations from a Continuous Probability Distribution. Summary. 8. SAMPLING DISTRIBUTIONS. Introduction. A Review of Sampling Distributions. The Sampling Distribution of the Mean. The Sampling Distribution of the Proportion. Sampling Distributions When the Population is Finite. Computer Simulation of Sampling Distributions. Summary. 9. ESTIMATION FROM SIMPLE DATA. Introduction. Point Estimates. A Preview of Interval Estimates. Confidence Interval Estimates for the Mean: s Known. Confidence Interval Estimates for the Mean: s Unknown. Confidence Interval Estimates for the Population Proportion. Sample Size Determination. When the Population is Finite. Summary. 10. HYPOTHESIS TESTS INVOLVING A SIMPLE MEAN OR PROPORTION. Introduction. Hypothesis Testing: Basic Procedures. Testing a Mean, Population Standard Deviation Known. Confidence Intervals and Hypothesis Testing. Testing a Mean, Population Standard Deviation Unknown. Testing a Proportion. The Power of a Hypothesis Test. Summary. 11. HYPOTHESIS TESTS INVOLVING TWO SIMPLE MEANS OR PROPORTIONS. Introduction. The Pooled-Variances t-Test for Comparing the Means of Two Independent Samples. The Unequal-Variances t-Test for Comparing the Means of Two Independent Samples. The z-Test for Comparing the Means of Two Independent Samples. Comparing Two Means When the Samples are Dependent. Comparing Two Sample Proportions. Comparing the Variances of Two Independent Samples. Summary. 12. ANALYSIS OF VARIANCE TESTS. Introduction. Analysis of Variance: Basic Concepts. One-Way Analysis of Variance. The Randomized Block Design. Two-Way Analysis of Variance. Summary. 13. CHI-SQUARE APPLICATIONS. Introduction. Basic Concepts in Chi-Square Testing. Tests for Goodness-of-Fit and Normality. Testing the Independence of Two Variables. Comparing Proportions from k Independent Samples. Estimation and Tests Regarding the Population Variance. Summary. 14. NONPARAMETRIC METHODS. Introduction. Wilcoxon Signed Rank Test for One Sample. Wilcoxon Signed Rank Test for Comparing Paired Samples. Wilcoxon Rank Sum Test for Comparing Two Independent Samples. Kruskal-Wallis Test for Comparing More Than Two Independent Samples. Friedman Test for the Randomized Block Design. Other Nonparametric Methods. Summary. 15. SIMPLE LINEAR REGRESSION AND CORRELATION. Introduction. The Simple Linear Regression Model. Interval Estimation Using the Sample Regression Line. Correlation Analysis. Estimation and Tests Regarding the Sample Regression Line. Additional Topics in Regression and Correlation Analysis. Summary. 16. MULTIPLE REGRESSION AND CORRELATION. Introduction. The Multiple Regression Model. Interval Estimation in Multiple Regression. Multiple Correlation Analysis. Significance Tests in Multiple Regression and Correlation. Overview of the Computer Analysis and Interpretation. Additional Topics in Multiple Regression and Correlation. Summary.17. MODEL BUILDING. Introduction. Polynomial Models with One Quantitative Predictor Variable. Polynomial Models with Two Quantitative Predictor Variables. Qualitative Variables. Data Transformations. Multicollinearity. Stepwise Regression. Selecting a Model. Summary. 18. TIME SERIES, FORECASTING AND INDEX NUMBERS. Introduction. Time Series. Smoothing Techniques. Seasonal Indexes. Forecasting. Evaluating Alternative Models: MAD and MSE. Autocorrelation, the Durbin-Watson Test, and Autoregressive Forecasting. Index Numbers. Summary. 19. DECISION THEORY. Introduction. Structuring the Decision Situation. Non-Bayesian Decision Making. Bayesian Decision Making. The Opportunity Loss Approach. Incremental Analysis and Inventory Decisions. Summary. Appendix: The Expected Value of Imperfect Information. 20. TOTAL QUALITY MANGEMENT. Introduction. A Historical Perspective and Defect Detection. The Emergence of Total Quality Management. Practicing Total Quality Management. Some Statistical Tools for Total Quality Management. Statistical Process Control: The Concepts. Control Charts for Variables. Control Charts for Attributes. More on Computer-Assisted Statistical Process Control. Summary.