Statistics: Principles and Methods, 8th Edition (PDF)

Statistics: Principles And Methods, 8Th Edition

By Richard A. Johnson and Gouri K. Bhattacharyya

Statistics: Principles and Methods, 8th Edition provides students and business professionals with a comprehensive introduction to statistics concepts, terminology, and methods with a wide array of practical applications. Real-world data provides an easily relatable frame of reference, while numerous examples reinforce key ideas and demonstrate critical concepts to help ease student comprehension. Designed for those seeking a highly practical introduction to statistical measurement, reasoning, and analysis, this book requires no specific mathematical background and leaves derivations behind in favor of logic, reasoning, and modern statistics software.

Concepts are introduced first in a real-life setting to illustrate immediate relevancy, and are subsequently expanded to relate underlying mechanisms, limitations, and further applications. An emphasis on the relationship between validity and assumptions underscores the importance of critical thinking and the use of appropriate models while instilling thoughtful habits that lead to accuracy in interpretation. Going beyond the typical introductory text to keep the focus on application, this book gives students a deeper understanding of statistics as it is used every day across disciplines and industries.

Recommended Book:

Basic and Clinical Biostatistics, 5th Edition

Table of Contents

1. Introduction to Statistics
- The Subject and Scope of Statistics
- Statistics in Aid of Scientific Inquiry
- Population and Sample
- The Purposeful Collection of Data
- Case Study: Statistics in Context
- Objectives of Statistics
2. Organization and Description of Data
- Main Types of Data
- Describing Data by Tables and Graphs
- Measures of Center
- Measures of Variation
- Checking Stability of Observations Over Time
- More on Graphics
- Case Study: Statistics in Context
3. Descriptive Study of Bivariate Data
- Bivariate Categorical Data
- Designed Experiment for Making a Comparison
- Scatter Diagram
- The Correlation Coefficient
- Prediction (Linear Regression)
4. Probability
- Probability of an Event
- Methods of Assigning Probability
- Event Operations and Laws of Probability
- Conditional Probability and Independence
- Bayes’ Theorem
- Random Sampling from a Finite Population
- Case Study: Statistics in Context
5. Probability Distributions
- Random Variables
- Discrete Distributions
- Expected Value & Standard Deviation
- Bernoulli Trials
- Binomial Distribution
- Poisson Distribution
6. The Normal Distribution
- Continuous Random Variable Model
- General Features of the Normal Distribution
- The Standard Normal
- Probability Calculations
- Normal Approximation to the Binomial
- Checking Normality
- Transformations to Achieve Normality
7. Variation in Repeated Samples — Sampling Distributions
- The Sampling Distribution of a Statistic
- The Distribution of the Sample Mean
- Central Limit Theorem
- Case Study: Statistics in Context
8. Drawing Inferences from Large Samples
- Estimation vs Testing
- Point Estimation
- Confidence Intervals for the Mean
- Testing Hypotheses about a Mean
- Inferences about a Population Proportion
9. Small Sample Inferences for Normal Populations
- Student’s t Distribution
- Small Sample Inference for μ
- Tests & Confidence Intervals
- Inference about σ (Chi-Square)
- Robustness of Procedures
10. Comparing Two Treatments
- Independent Samples vs Matched Pairs
- Difference of Means — Large Samples
- Difference of Means — Small Normal Samples
- Randomization
- Matched Pairs Comparisons
- Choosing Between Designs
- Comparing Two Proportions
11. Regression Analysis I – Simple Linear Regression
- Single Predictor Regression
- Straight Line Model
- Least Squares Method
- Sampling Variability of Estimators
- Inference Problems
- Strength of Linear Relation
- Assumptions of the Linear Model
12. Regression Analysis II – Multiple Regression
- Nonlinear Relations & Transformations
- Multiple Linear Regression
- Residual Diagnostics
13. Analysis of Categorical Data
- Formulating Testing Problems
- Pearson’s Chi-Square: Goodness of Fit
- Test of Homogeneity
- Test of Independence
14. Analysis of Variance (ANOVA)
- One-Way ANOVA
- Population Model & Inference
- Simultaneous Confidence Intervals
- Graphical Diagnostics
- Randomized Block Experiments
15. Nonparametric Inference
- Wilcoxon Rank-Sum Test
- Matched Pairs Nonparametric Tests
- Rank-Based Correlation
- Concluding Remarks
Appendix A1: Summation Notation
Appendix A2: Rules for Counting
Appendix A3: Expectation & SD Properties
Appendix A4: Expected Value & SD of X
Appendix B: Statistical Tables
- Random Digits
- Cumulative Binomial Probabilities
- Cumulative Poisson Probabilities
- Standard Normal Probabilities
- t Distribution Critical Values
- Chi-Square Critical Values
- F Distribution Values
- Wilcoxon Rank-Sum Tail Probabilities
- Wilcoxon Signed-Rank Tail Probabilities
- General Formulas Tables
Summary of Formulas Useful for Exams
Data Bank
Answers to Selected Exercises
Index

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