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Statistics: Principles and Methods, 6th Edition International Student Version


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Statistics: Principles and Methods, 6th Edition International Student Version


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 maintains a focus on accuracy without getting bogged down in unnecessary details.

This highly regarded text provides a wide range of contemporary applications in its examples and exercises to ensure that all students will find something they can relate to and which will motivate them. The book is used in mathematics, statistics, biology, and engineering departments as an introductory text.

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1. Introduction
1. What is Statistics?
2. Statistics in Our Everyday Life
3. Statistics in Aid of Scientific Inquiry
4. Two Basic Concepts- Population and Sample
5. The Purposeful Collection of Data
6. Statistics in Context
7. Objectives of Statistics

2. Organization and Description of Data
1. Introduction
2. Main Types of Data
3. Describing Data by Tables and Graphs
4. Measures of Center
5. Measures of Variation
6. Checking the Stability of the Observations over Time
7. More on Graphics
8. Statistics in Context

3. Descriptive Study of Bivariate Data
1. Introduction
2. Summarization of Bivariate Categorical Data
3. A Designed Experiment for Making a Comparison
4. Scatter Diagram of Bivariate Measurement Data
5. The Correlation Coefficient- A Measure of Linear Relation
6. Prediction of One Variable from Another (Linear Regression)

4. Probability
1. Introduction
2. Probability of an Event
3. Methods of Assigning Probability
4. Event Relations and Two Laws of Probability
5. Conditional Probability and Independence
6. Bayes’ Theorem
7. Random Sampling from a Finite Population

 5. Probability Distributions
1. Introduction
2. Random Variables
3. Probability Distribution of a Discrete Random Variable
4. Expectation (Mean) and Standard Deviation of a Probability Distribution
5. Success and Failures- Bernoulli Trials
6. The Binomal Distribution
7. The Binomal Distribution in Context

6. The Normal Distribution
1. Probability Model for a Continuous Random Variable
2. The Normal Distribution-Its General Features
3. The Standard Normal Distribution
4. Probability Calculations with Normal Distributions
5. The Normal Approximation to the Binomial
6. Checking the Plausibility of a Normal Model
7. Transforming Observations to Attain Near Normality

7. Variation in Repeated Samples-Sampling Distribution
1. Introduction
2. The Sampling Distribution of a Statistic
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 µ -Small Sample Size
4. Relationship between Tests and Confidence Intervals
5. Inferences About the Standard Deviation σ
(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)
4. Contingency Table with Neither Margin Fixed
(Test of Independence)
5. Review Exercises

14. Analysis of Variance (ANOVA)
1. Introduction
2. Comparison of Several Treatments- The Completely Randomized Design
3. Population Model and Inferences for a Completely Randomized Design
4. Simultaneous Confidence Intervals
5. Graphical Diagnostics and Displays to Supplement ANOVA
6. Randomized Block Experiments for Comparing k Treatments
7. Review Exercises

Appendix A1 Summation Notation

Appendix A2 Rules for Counting

Appendix A3 Expectation and Standard Deviation—Properties

Appendix A4 The Expected Value and Standard Deviation of   X

Appendix B Tables

  • Technical Appendix A presents a few statistical topics of a mathematical nature.  New material on counting rules has been added.
  • Section 4.6 on Bayes’ Theorem has been added
  • New sections have been added to chapter 10: 10.3, Large Samples Inference about Difference of Two Means; 10.4, Inferences from Small Samples: Normal Populations with Equal Variances; 10.5, Inferences from Small Samples: Normal Populations but Unequal Variances
  • Applications have been updated and many new ones have been added.
·        Crucial elements are boxed to highlight important concepts and methods essential for learning statistics. At the end of each chapter, all of its key ideas and formulas are summarized.

·        A rich collection of examples and exercises is included. These are drawn from a large variety of real-life settings. In fact, many data sets stem from genuine experiments, surveys, or reports.

·        Using Statistics Wisely are important guidelines for using statistics now appear at the end of each chapter.

·        Technology: At the end of most chapters are step-by-step directions for using MINITAB, EXCEL, and the TI-84 calculator. This concentrates the presentation of special purpose instructions so that, with few exceptions, only computer output is needed in the text.

·        Regression analysis is a primary statistical technique so Johnson/Bhattacharyya provides a more thorough coverage of this topic than is usual at this level. The basics of regression are introduced in Chapter 11 and in Chapter 12 this discussion is expanded to methods of model checking, handling nonlinear relations, and multiple regression analysis. Complex formulas and calculations are judiciously replaced by computer output so main ideas can be easily learned and appreciated by students.

·        Computer Aided Statistical Analyses use software packages that can remove much of the drudgery of hand calculation and plotting. They allow students to work with larger data sets, where patterns are more pronounced, and make complicated calculations. In addition to the discussion of some computer output in the text, computer exercises are included in all chapters where relevant.

·        A Convenient Data Bank at the end of the book contains a substantial collection of data. These data sets, together with numerous others throughout the book, allow for considerable flexibility in the choice between concept-oriented and applications-oriented exercises. The Data Bank and the other larger data sets can be downloaded from the book’s Web site.