Statistics II Engineering & Social Sciences Program Fall 2019 Semester - Madrid

Chapter 1. Inference in one population
1.1 Introduction: parameters and statistical inference
1.2 Point estimators
1.3 The estimation of the mean and variance
1.4. The sampling distribution of the sample mean
1.5 Estimation using confidence intervals
1.5.1 Confidence interval for the mean of a normal population with known variance
1.5.2 Confidence interval for the mean in large samples
1.5.3 Confidence interval for the mean of a normal population with unknown variance: t distribution
1.5.4 Confidence interval for the variance of a normal population

Chapter 2. Basic concepts in hypothesis testing
2.1 Definition of a test of hypothesis
2.2 The null and alternative hypotheses
2.3 Type I and type II errors, power of the test
2.4 The concept of p-value and decision-making
2.5 Main steps needed to perform a test of hypothesis

Chapter 3. Comparing two populations
3.1 Independent samples from two populations
3.2 Inference for the population means in small samples
3.3 Inference for the population means in large samples
3.4 Comparing the variances of two normal populations: the F distribution

Chapter 4. Regression analysis: the simple linear regression model
4.1 The goal of regression analysis
4.2 The specification of a simple linear regression model
4.3 Least-squares estimators: construction and properties
4.4 Inference in the linear regression model
4.5 Inference for the slope
4.6 Inference for the variance
4.7 Mean response and confidence intervals
4.8 New response and prediction intervals

Chapter 5. Regression analysis: assumptions, model diagnostics, multiple linear regression model
5.1 The residual analysis
5.2 The ANOVA decomposition
5.3 Nonlinear relationships and linearizing transformations
5.4 The linear regression model in matrix form
5.5 Introduction to multiple linear regression