Statistics : principles and methods

✍ Scribed by Johnson, Richard Arnold; Bhattacharyya, Gouri K

• Publisher: John Wiley & Sons
• Year: 2010
• Tongue: English
• Leaves: 706
• Edition: 6ed.
• Category: Library

✦ Synopsis

Johnson provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters have been updated with real-world data to make the material more relevant. The revised pedagogy will help them contextualize statistical concepts and procedures. The numerous examples clearly demonstrate the important points of the methods. New What Will We Learn opening paragraphs set the stage for the material being discussed. Using Statistics Wisely boxes summarize key lessons. In addition, Statistics in Context sections give business professionals an understanding of applications in which a statistical approach to variation is needed

✦ Table of Contents

Content: 3. Distribution of the Sample Mean and the Central Limit Theorem 4. Statistics in Context 8. Drawing Inferences From Large Samples 1. Introduction 2. Point Estimation of Population Mean 3. Confidence Interval for a Population Mean 4. Testing Hypotheses about a Population Mean 5. Inferences about a Population Proportion 9. Small-Sample Inferences for Normal Populations 1. Introduction 2. Student's t Distribution 3. Inferences about æ -Small Sample Size 4. Relationship between Tests and Confidence Intervals 5. Inferences About the Standard Deviation o (The Chi-Square Distribution) 6. Robustness of Inference Procedures 10. Comparing Two Treatments 1. Introduction 2. Independent Random Samples from Two Populations 3. Large Samples Inference about Difference of Two Means 4. Inferences from Small Samples: Normal Populations with Equal Variances 5. Inferences from Small Samples: Normal Populations but Unequal Variances 6. Randomization and its Role in Inference 7. Matched Pairs Comparisons 8. Choosing Between Independent Samples and a Matched Pairs Sample 9. Comparing Two Population Proportions 11. Regression Analysis I (Simple Linear Regression) 1. Introduction 2. Regression with a Single Predictor 3. A Straight-Line Regression Model 4. The Method of Least Squares 5. The Sampling Variability of the Least Squares Estimators --
Tools for Inference 6. Important Inference Problems 7. The Strength of a Linear Relation 8. Remarks About the Straight Line Model Assumption 12. Regression Analysis- II Multiple Linear Regression and Other Topics 1. Introduction 2. Nonlinear Relations and Linearizing Transformations 3. Multiple Linear Regression 4. Residual Plots to Check the Adequacy of a Statistical Model 5. Review Exercises 13. Analysis of Categorical Data 1. Introduction 2. Pearson's x 2 Test for Goodness of Fit 3. Contingency Table with One Margin Fixed (Test of Homogeneity).