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Statistics: Principles and Methods, 7th Edition

Book Description

Johnson/Bhattacharyya is unique in its clarity of exposition while maintaining the mathematical correctness of its explanations. Many other books that claim to be easier to understand often sacrifice mathematical rigor. In contrast, Johnson/ Bhattacharyya maintain a focus on accuracy without getting bogged down in unnecessary details.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Preface
  5. Contents
  6. CHAPTER 1: Introduction to Statistics
    1. 1. THE SUBJECT AND SCOPE OF STATISTICS
    2. 2. STATISTICS IN AID OF SCIENTIFIC INQUIRY
    3. 3. TWO BASIC CONCEPTS—POPULATION AND SAMPLE
    4. 4. THE PURPOSEFUL COLLECTION OF DATA
    5. CASE STUDY: STATISTICS IN CONTEXT
    6. 5. OBJECTIVES OF STATISTICS
  7. CHAPTER 1–ONLINE
  8. CHAPTER 2: Organization and Description of Data
    1. 1. MAIN TYPES OF DATA
    2. 2. DESCRIBING DATA BY TABLES AND GRAPHS
    3. 3. MEASURES OF CENTER
    4. 4. MEASURES OF VARIATION
    5. 5. CHECKING THE STABILITY OF THE OBSERVATIONS OVER TIME
    6. 6. MORE ON GRAPHICS
    7. CASE STUDY: STATISTICS IN CONTEXT
  9. CHAPTER 2–ONLINE
  10. CHAPTER 3: Descriptive Study of Bivariate Data
    1. 1. SUMMARIZATION OF BIVARIATE CATEGORICAL DATA
    2. 2. A DESIGNED EXPERIMENT FOR MAKING A COMPARISON
    3. 3. SCATTER DIAGRAM OF BIVARIATE MEASUREMENT DATA
    4. 4. THE CORRELATION COEFFICIENT—A MEASURE OF LINEAR RELATION
    5. 5. PREDICTION OF ONE VARIABLE FROM ANOTHER (LINEAR REGRESSION)
  11. CHAPTER 4: Probability
    1. 1. PROBABILITY OF AN EVENT
    2. 2. METHODS OF ASSIGNING PROBABILITY
    3. 3. EVENT OPERATIONS AND TWO LAWS OF PROBABILITY
    4. 4. CONDITIONAL PROBABILITY AND INDEPENDENCE
    5. 5. BAYES' THEOREM
    6. 6. RANDOM SAMPLING FROM A FINITE POPULATION
    7. CASE STUDY: STATISTICS IN CONTEXT
  12. CHAPTER 5: Probability Distributions
    1. 1. RANDOM VARIABLES
    2. 2. PROBABILITY DISTRIBUTION OF A DISCRETE RANDOM VARIABLE
    3. 3. MEAN (EXPECTED VALUE) AND STANDARD DEVIATION OF A PROBABILITY DISTRIBUTION
    4. 4. SUCCESSES AND FAILURES—BERNOULLI TRIALS
    5. 5. THE BINOMIAL DISTRIBUTION
    6. 6. THE POISSON DISTRIBUTION AND RARE EVENTS
  13. CHAPTER 6: The Normal Distribution
    1. 1. PROBABILITY MODEL FOR A CONTINUOUS RANDOM VARIABLE
    2. 2. THE NORMAL DISTRIBUTION—ITS GENERAL FEATURES
    3. 3. THE STANDARD NORMAL DISTRIBUTION
    4. 4. PROBABILITY CALCULATIONS WITH NORMAL DISTRIBUTIONS
    5. 5. THE NORMAL APPROXIMATION TO THE BINOMIAL
    6. *6. CHECKING THE PLAUSIBILITY OF A NORMAL MODEL
    7. *7. TRANSFORMING OBSERVATIONS TO ATTAIN NEAR NORMALITY
  14. CHAPTER 7: Variation in Repeated Samples—Sampling Distributions
    1. 1. THE SAMPLING DISTRIBUTION OF A STATISTIC
    2. 2. DISTRIBUTION OF THE SAMPLE MEAN AND THE CENTRAL LIMIT THEOREM
    3. CASE STUDY: STATISTICS IN CONTEXT
  15. CHAPTER 8: Drawing Inferences from Large Samples
    1. 1. TWO TYPES OF STATISTICAL INFERENCE: ESTIMATION AND TESTING
    2. 2. POINT ESTIMATION OF A POPULATION MEAN
    3. 3. CONFIDENCE INTERVAL ESTIMATION OF A POPULATION MEAN
    4. 4. TESTING HYPOTHESES ABOUT A POPULATION MEAN
    5. 5. INFERENCES ABOUT A POPULATION PROPORTION
  16. CHAPTER 9: Small Sample Inferences for Normal Populations
    1. 1. STUDENT'S t DISTRIBUTION
    2. 2. INFERENCES ABOUT μ —SMALL SAMPLE SIZE
    3. 3. RELATIONSHIP BETWEEN TESTS AND CONFIDENCE INTERVALS
    4. 4. INFERENCES ABOUT THE STANDARD DEVIATION σ (THE CHI-SQUARE DISTRIBUTION)
    5. 5. ROBUSTNESS OF INFERENCE PROCEDURES
  17. CHAPTER 10: Comparing Two Treatments
    1. 1. TWO DESIGNS: INDEPENDENT SAMPLES AND MATCHED PAIRS SAMPLE
    2. 2. INFERENCES ABOUT THE DIFFERENCE OF MEANS—INDEPENDENT LARGE SAMPLES
    3. 3. INFERENCES ABOUT THE DIFFERENCE OF MEANS—INDEPENDENT SMALL SAMPLES FROM NORMAL POPULATIONS
    4. 4. RANDOMIZATION AND ITS ROLE IN INFERENCE
    5. 5. MATCHED PAIRS COMPARISONS
    6. 6. CHOOSING BETWEEN INDEPENDENT SAMPLES AND A MATCHED PAIRS SAMPLE
    7. 7. COMPARING TWO POPULATION PROPORTIONS
  18. CHAPTER 11: Regression Analysis I Simple Linear Regression
    1. 1. REGRESSION WITH A SINGLE PREDICTOR
    2. 2. A STRAIGHT LINE REGRESSION MODEL
    3. 3. THE METHOD OF LEAST SQUARES
    4. 4. THE SAMPLING VARIABILITY OF THE LEAST SQUARES ESTIMATORS—TOOLS FOR INFERENCE
    5. 5. IMPORTANT INFERENCE PROBLEMS
    6. 6. THE STRENGTH OF A LINEAR RELATION
    7. 7. REMARKS ABOUT THE STRAIGHT LINE MODEL ASSUMPTIONS
  19. CHAPTER 12: Regression Analysis II Multiple Linear Regression and Other Topics
    1. 1. NONLINEAR RELATIONS AND LINEARIZING TRANSFORMATIONS
    2. 2. MULTIPLE LINEAR REGRESSION
    3. 3. RESIDUAL PLOTS TO CHECK THE ADEQUACY OF A STATISTICAL MODEL
  20. CHAPTER 13: Analysis of Categorical Data
    1. 1. FORMULATING TESTING PROBLEMS CONCERNING CATEGORICAL DATA
    2. 2. PEARSON'S χ² TEST FOR GOODNESS OF FIT
    3. 3. CONTINGENCY TABLE WITH ONE MARGIN FIXED (TEST OF HOMOGENEITY)
    4. 4. CONTINGENCY TABLE WITH NEITHER MARGIN FIXED (TEST OF INDEPENDENCE)
  21. CHAPTER 14: Analysis of Variance (ANOVA)
    1. 1. COMPARISON OF SEVERAL TREATMENTS—ONE-WAY ANALYSIS OF VARIANCE
    2. 2. POPULATION MODEL AND INFERENCES FOR A ONE-WAY ANALYSIS OF VARIANCE
    3. 3. SIMULTANEOUS CONFIDENCE INTERVALS
    4. 4. GRAPHICAL DIAGNOSTICS AND DISPLAYS TO SUPPLEMENT ANOVA
    5. 5. RANDOMIZED BLOCK EXPERIMENTS FOR COMPARING K TREATMENTS
  22. CHAPTER 15: Nonparametric Inference
    1. 1. THE WILCOXON RANK-SUM TEST FOR COMPARING TWO TREATMENTS
    2. 2. MATCHED PAIRS COMPARISONS
    3. 3. MEASURE OF CORRELATION BASED ON RANKS
    4. 4. CONCLUDING REMARKS
  23. APPENDIX A1: Summation Notation
  24. APPENDIX A2: Rules for Counting
  25. APPENDIX A3: Expectation and Standard Deviation—Properties
  26. APPENDIX A4: The Expected Value and Standard Deviation of X
  27. APPENDIX B: Tables
  28. Data Bank
  29. Answers to Selected Odd-Numbered Exercises
  30. Index