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Statistics is a thought process. In this comprehensive introduction to statistical methods and data analysis, the process is presented utilizing a four-step approach: 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses.

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• Part I Introduction
• 1 What Is Statistics?
• Introduction
• Why Study Statistics?
• Some Current Applications of Statistics
• What Do Statisticians Do?
• Quality and Process Improvement
• A Note to the Student
• Summary
• Supplementary Exercises
• Part III Collecting The Data
• 2 Using Surveys And Scientific Studies To Collect Data
• Introduction
• Surveys
• Scientific Studies
• Observational Studies
• Data Management: Preparing Data for Summarization and Analysis
• Summary
• Part III Summarizing Data
• 3 Data Description Introduction
• Describing Data on a Single Variable: Graphical Methods
• Describing Data on a Single Variable: Measures of Central Tendency
• Describing Data on a Single Variable: Measures of Variability
• The Box Plot
• Summarizing Data from More Than One Variable
• Calculators, Computers, and Software Systems
• Summary
• Key Formulas
• Supplementary Exercises
• Part IV Tools And Concepts
• 4 Probability And Probability Distributions
• How Probability Can Be Used in Making Inferences
• Finding the Probability of an Event
• Basic Event Relations and Probability Laws
• Conditional Probability and Independence
• Bayes''''s Formula
• Variables: Discrete and Continuous
• Probability Distributions for Discrete Random Variables
• A Useful Discrete Random Variable: The Binomial
• Probability Distributions for Continuous Random Variables
• A Useful Continuous Random Variable: The Normal Distribution
• Random Sampling
• Sampling Distributions
• Normal Approximation to the Binomial
• Summary
• Key Formulas
• Supplementary Exercises
• Part V Analyzing Data: Central Values, Variances, And Proportions
• 5 Inferences On A Population Central Value
• Introduction and Case Study
• Estimation of  
• Choosing the Sample Size for Estimating  
• A Statistical Test for  
• Choosing the Sample Size for Testing  
• The Level of Significance of a Statistical Test
• Inferences about   for Normal Population, s Unknown
• Inferences about the Population Median
• Summary
• Key Formulas
• Supplementary Exercises
• 6 Comparing Two Population Central Values
• Introduction and Case Study
• Inferences about  1 -  2: Independent Samples
• A Nonparametric Alternative: The Wilcoxon Rank Sum Test
• Inferences about  1 -  2: Paired Data
• A Nonparametric Alternative: The Wilcoxon Signed-Rank Test
• Choosing Sample Sizes for Inferences about  1 -  2
• Summary
• Key Formulas
• Supplementary Exercises
• 7 Inferences About Population Variances
• Introduction and Case Study
• Estimation and Tests for a Population Variance
• Estimation and Tests for Comparing Two Population Variances
• Tests for Comparing k > 2 Population Variances
• Summary
• Key Formulas
• Supplementary Exercises
• 8 Inferences About Population Central Values
• Introduction and Case Study
• A Statistical Test About More Than Two Population Variances
• Checking on the Assumptions
• Alternative When Assumptions are Violated: Transformations
• A Nonparametric Alternative: The Kruskal-Wallis Test
• Summary
• Key Formulas
• Supplementary Exercises
• 9 Multiple Comparisons
• Introduction and Case Study
• Planned Comparisons Among Treatments: Linear Contrasts
• Which Error Rate Is Controlled
• Multiple Comparisons with the Best Treatment
• Comparison of Treatments to a Control
• Pairwise Comparison on All Treatments
• Summary
• Key Formulas
• Supplementary Exercises
• 10 Categorical Data
• Introduction and Case Study
• Inferences about a Population Proportion p
• Comparing Two Population Proportions p1 - p2
• Probability Distributions for Discrete Random Variables
• The Multinomial Experiment and Chi-Square Goodness-of-Fit Test
• The Chi-Square Test of Homogeneity of Proportions
• The Chi-Square Test of Independence of Two Nominal Level Variables
• Fisher''''s Exact Test, a Permutation Test
• Measures of Association
• Combining Sets of Contingency Tables
• Summary
• Key Formulas
• Supplementary Exercises PART VI:
• Part IV Analyzing Data: Regression Methods, Model Building
• 11 Simple Linear Regression And Correlation
• Linear Regression a