Chapter 1: Descriptive Statistics
1.1: What is Statistics + Applications
1.2: Populations, Samples, and Processes
1.3: Pictorial ad Tabular Methods in Descriptive Statistics
1.4: Measures of Location
1.5: Measures of Variability
Chapter 2: Probability
2.1: Sample Spaces and Events
2.2: Axioms, Interpretations, and Properties of Probability
2.3: Counting Techniques
2.4: Conditional Probability
2.5: Independence
Chapter 3: Discrete Random Variables and Probability Distributions
3.1: Random Variables
3.2: Probability Distributions for Discrete Random Variables
3.3: Expected Values
3.4: Binomial Probability Distribution
3.5: Hypergeometric and Negative Binomial Distributions
3.6: Poisson Probability Distribution
Chapter 4: Continuous Random Variables and Probability Distributions
4.1: Probability Density Functions
4.2: Cumulative Distribution Functions and Expected Values
4.3: Normal Distribution
4.4: Exponential and Gamma Functions
4.5: Other Continuous Distributions
4.6: Probability Plots
Chapter 5: Joint Probability Distributions and Random Samples
5.1: Jointly Distributed Random Variables
5.2: Expected Values, Covariance and Correlation
5.3: Statistics and their Distributions
5.4: Distribution of the Sample Mean
5.5: Distribution of a Linear Combination
Chapter 6: Point Estimation
6.1: Some General Concepts of Point Estimation
6.2: Methods of Point Estimation
Chapter 7: Statistical Intervals Based on a Single Sample
7.1: Confidence Intervals
7.2: Large Sample Confidence Intervals for a Population Mean and Proportion
7.3: Intervals Based on a Normal Population Distribution
7.4: Confidence Intervals for the Variance and Standard Deviation of a Normal Population
Chapter 8: Test of Hypotheses Based on a Single Sample
8.1: Hypotheses and Test Procedures
8.2: Tests About a Population Mean
8.3: Tests Concerning a Population Proportion
8.4: P-Values
8.5: Selecting the Right Test
Chapter 9: Inferences Based on Two Samples
9.1: Z-Tests and Confidence Intervals for a Difference Between Two Population Means
9.2: Two-Sample t Test and Confidence Interval
9.3: Analysis of Paired Data
9.4: Inferences Concerning a Difference Between Population Proportions
9.5: Inferences Concerning Two Population Variances
Chapter 10: Single and Multi Factor Analysis of Variance
10.1: Single-Factor ANOVA
10.2: Multiple Comparisons in ANOVA
10.3: Two-Factor ANOVA with K = 1
10.4: Two-Factor ANOVA with K = 0
10.5: Three-Factor ANOVA
10.6: 2^p Factorial Experiments
Chapter 11: Linear Regression and Correlation
11.1: Simple Linear Regression Model
11.2: Estimating Model Parameters
11.3: Inferences About the Slope Parameter
11.4: Inferences Concerning Mean and Prediction of Future Y Values
11.5: Correlation
Chapter 12: Non-Linear and Multiple Regression
12.1: Assessing Model Adequacy
12.2: Regression with Transformed Variables
12.3: Polynomial Regression
12.4: Multiple Regression Analysis
12.5: Other Issues in Multiple Regression
Chapter 13: Goodness-of-Fit Tests and Categorical Data Analysis
13.1: Goodness-of-Fit Tests when Category Probabilities are Completely Specified
13.2: Goodness-of-Fit Tests for Composite Hypotheses
13.3: Two-Way Contingency Tables
Chapter 14: Distribution-Free Procedures
14.1: Wilcoxon Signed-Rank Test
14.2: Wilcoxon Rank-Sum Test
14.3: Distribution-Free Confidence Intervals
14.4: Distribution-Free ANOVA
Chapter 16: Quality Control Methods
16.1: General Control Charts
16.2: Control Charts for Process Location
16.3: Control Charts for Process Variation
16.4: Control Charts for Attributes
16.5: CUSUM Procedures
16.6: Acceptance Sampling