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Statistics for Management and Economics 7e Keller

Stan

Minimalne ślady używania na okładce. Wewnątrz jedna pieczątka, bez zaznaczeń.

O książce

This worldwide best-selling business statistics text teaches students how to apply statistics to real business problems through the author's unique three-step approach to problem solving. Students learn to IDENTIFY the right technique by focusing on the problem objective and data type. They then learn to COMPUTE the statistics either by hand, using Excel, or using MINITAB. Finally, they INTERPRET the results in the context of the problem. Keller's approach enhances student comprehension as well as practical skills. The book offers maximum flexibility to instructors wishing to teach concepts by hand or with the computer, or by using both hand and computer methods.

Spis treści

1. WHAT IS STATISTICS? Introduction. Key Statistical Concepts. Statistical Applications in Business. Statistics and the Computer. World Wide Web and Learning Center. Introduction to Microsoft Excel. Introduction to MINITAB.

2. GRAPHICAL AND TABULAR DESCRIPTIVE TECHNIQUES. Introduction. Types of Data and Information. Graphical and Tabular Techniques for Nominal Data. Graphical Techniques for Interval Data. Describing the Relationship Between Two Variables. Describing Time-Series Data. Summary.

3. ART AND SCIENCE OF GRAPHICAL PRESENTATIONS. Introduction. Graphical Excellence. Graphical Deception. Presenting Statistics: Written Reports and Oral Presentations. Summary.

4. NUMERICAL DESCRIPTIVE TECHNIQUES. Introduction. Measures of Central Location. Measures of Variability. Measures of Relative Standing and Box Plots. Measures of Linear Relationship. (Optional) Applications in Professional Sports: Baseball. Comparing Graphical and Numerical Techniques. General Guidelines for Exploring Data. Summary. REVIEW OF DESCRIPTIVE TECHNIQUES.

5. DATA COLLECTION AND SAMPLING. Introduction. Methods of Collecting Data. Sampling. Sampling Plans. Sampling and Nonsampling Errors. Summary.

6. PROBABILITY. Introduction. Assigning Probability to Events. Joint, Marginal, and Conditional Probability. Probability Rules and Trees. Bayes' Law. Identifying the Correct Method. Summary.

7. RANDOM VARIABLES AND DISCRETE PROBABILITY DISTRIBUTIONS. Introduction. Random Variables and Probability Distributions. Bivariate Distributions. (Optional) Applications in Finance: Portfolio Diversification and Asset Allocation. Binomial Distribution. Poisson Distribution. Summary.

8.CONTINUOUS PROBABILITY DISTRIBUTIONS. Introduction. Probability Density Functions. Normal Distribution. (Optional) Exponential Distribution. Other Continuous Distributions. Summary.

9. SAMPLING DISTRIBUTIONS. Introduction. Sampling Distribution of the Mean. Sampling Distribution of a Proportion. Sampling Distribution of the Difference Between Two Means. From Here to Inference. Summary.

10. INTRODUCTION TO ESTIMATION. Introduction. Concepts of Estimation. Estimating the Population Mean When the Population Standard Deviation is Known. Selecting the Sample Size. Summary.

11. INTRODUCTION TO HYPOTHESIS TESTING. Introduction. Concepts of Hypothesis Testing. Testing the Population Mean When the Population Standard Deviation is Known. Calculating the Probability of a Type II Error. The Road Ahead. Summary.

12. INFERENCE ABOUT A POPULATION. Introduction. Inference about a Population Mean When the Standard Deviation is Unknown. Inference about a Population Variance. Inference about a Population Proportion. (Optional) Applications in Marketing: Market Segmentation. (Optional) Applications in Accounting: Auditing. Summary.

13. INFERENCE ABOUT COMPARING TWO POPULATIONS. Introduction. Inference about the Difference Between Two Means: Independent Samples. Observational and Experimental Data. Inference about the Difference Between Two Means: Matched Pairs Experiment. Inference about the Ratio of Two Variances. Inference about the Difference Between Two Population Proportions. Summary.

Excel Instructions for Stacked and Unstacked Data. MINITAB Instructions for Stacked and Unstacked Data.

14. STATISTICAL INFERENCE: REVIEW OF CHAPTERS 12 AND
13. Introduction. Guide to Identifying the Correct Technique: Chapters 12 and
13.

15. ANALYSIS OF VARIANCE. Introduction. One-Way Analysis of Variance. Analysis of Variance Experimental Designs. Randomized Blocks (Two-Way) Analysis of Variance. Two-Factor Analysis of Variance. (Optional) Applications in Operations Management: Finding and Reducing Variation. Multiple Comparisons. Summary.

16. CHI-SQUARED TESTS. Introduction. Chi-Squared Goodness-of-Fit Test. Chi-Squared Test of a Contingency Table. Summary of Tests on Nominal Data. (Optional) Chi-Squared Tests for Normality. Summary.

17. SIMPLE LINEAR REGRESSION AND CORRELATION. Introduction. Model. Estimating the Coefficients. Error Variable: Required Conditions. Assessing the Model. (Optional) Applications in Finance: Market Model. Using the Regression Equation. Regression Diagnostics-I. Summary.

18. MULTIPLE REGRESSION. Introduction. Model and Required Conditions. Estimating the Coefficients and Assessing the Model. Regression Diagnostics-II. Regression Diagnostics-III (Time Series). Summary.

19. MODEL BUILDING. Introduction. Polynomial Models. Nominal Independent Variables. (Optional) Applications in Human Resources Management: Pay Equity. (Optional) Logistic Regression. (Optional) Stepwise Regression. Model Building. Summary.

20. TIME SERIES ANALYSIS AND FORECASTING. Introduction. Time Series Components. Smoothing Techniques. Trend and Seasonal Effects. Introduction to Forecasting. Forecasting Models. Summary.

21. NONPARAMETRIC STATISTICS. Introduction. Wilcoxon Rank Sum Test. Sign Test and Wilcoxon Signed Rank Sum Test. Kruskal-Wallis Test. Friedman Test. Spearman Rank Correlation Coefficient. Summary.

22. STATISTICAL PROCESS CONTROL. Introduction. Process Variation. Control Charts. Control Charts for Variables: and Charts. Control Charts for Attributes: p Chart. Summary.

23. DECISION ANALYSIS. Introduction. Decision Problem. Acquiring, Using, and Evaluating Additional Information. Summary.

24 STATISTICAL INFERENCE: CONCLUSION. Introduction. Identifying the Correct Technique: Summary of Statistical Inference. The Last Word.

Appendix A: DATA FILE SAMPLE STATISTICS.
Appendix B: TABLES. Binomial Probabilities. Poisson Probabilities. Normal Probabilities. Critical Values of t. Critical Values of. Critical Values of F. Critical Values of the Studentized Range. Critical Values for the Wilcoxon Rank Sum Test. Critical Values for the Wilcoxon Signed Rank Sum Test. Critical Values for the Spearman Rank Correlation Coefficient. Critical Values for the Durbin-Watson Statistic. Control Chart Constants.
Appendix C: ANSWERS TO SELECTED EVEN-NUMBERED EXERCISES.
INDEX.