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4.1 STATISTICS AND SAMPLING DISTRIBUTIONS
4.1.1 Sampling from a Normal Distribution
4.1.2 Sampling from a Bernoulli Distribution
4.1.3 Sampling from a Poisson Distribution
4.2 POINT ESTIMATION OF PROCESS PARAMETERS
4.3 STATISTICAL INFERENCE FOR A SINGLE SAMPLE
4.3.1 Inference on the Mean of a Population, Variance Known
4.3.2 The Use of P-Values for Hypothesis Testing
4.3.3 Inference on the Mean of a Normal Distribution, Variance Unknown
4.3.4 Inference on the Variance of a Normal Distribution
4.3.5 Inference on a Population Proportion
4.3.6 The Probability of Type II Error and Sample Size Decisions
4.4 STATISTICAL INFERENCE FOR TWO SAMPLES
4.4.1 Inference for a Difference in Means, Variances Known
4.4.2 Inference for a Difference in Means of Two Normal Distributions, Variances Unknown
4.4.3 Inference on the Variances of Two Normal Distributions
4.4.4 Inference on Two Population Proportions
4.5 WHAT IF THERE ARE MORE THAN TWO POPULATIONS? THE ANALYSIS OF VARIANCE
4.5.1 An Example
4.5.2 The Analysis of Variance
4.5.3 Checking Assumptions: Residual Analysis
4.6 LINEAR REGRESSION MODELS
4.6.1 Estimation of the Parameters in Linear Regression Models
4.6.2 Hypothesis Testing in Multiple Regression
4.6.3 Confidence Intervals in Multiple Regression
4.6.4 Prediction of New Response Observations
4.6.5 Regression Model Diagnostics
Supplemental Material for Chapter 4
S4.1 Random Samples ...
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