Introduction to Statistical Methods and Regression Models
Introduction to Statistical Methods and Regression Models

Introduction to Statistical Methods and Regression Models

Lead Author(s): Lelys Bravo de Guenni

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This book is an introduction to the main methods used in Statistical Inference and Regression Analysis, using real life examples and a statistical package outputs.

Introduction to Statistical Methods and Regression Models

Lelys Bravo de Guenni, PhD

Table of Contents

Chapter 1: Introduction to Statistical Thinking

  • Questions to Chapter 1

Chapter 2: Descriptive Statistics and Sampling Distributions

  • Part I: Descriptive Statistics
  • Part II: Sampling Distributions
  • Question to Chapter 2

Chapter 3: Basic Statistical Inference

  • 3.1 Point Estimation
  • 3.2 Interval Estimation
  • 3.3 Hypothesis Testing
  • Questions to Chapter 3

Chapter 4: Correlation Analysis

  • Part I: Definition of the Correlation Coefficient
  • Part II: Hypothesis testing and confidence intervals for the correlation coefficient ρ
  • Questions to Chapter 4

Chapter 5: Simple Linear Regression Analysis

  • Part I: Model assumptions and least-square estimation
  • Part II: Inference on the linear  regression equation
  • Part III. The Analysis of Variance Table: Assessing the appropriateness of the straight-line regression
  • Questions to Chapter 5

Chapter 6: Multiple Regression Analysis

  • Part I: Examples and Analysis of Variance for the Multiple Regression equation
  • Part II: Statistical Inference in Multiple Regression
  • Part III. Multiple correlation coefficient, partial correlation and multiple partial correlation coefficient
  • Questions to Chapter 6

Chapter 7: Dummy variables in regression and straight-line comparisons

  • 7.1 Dummy Variables in Regression
  • 7.2 Comparing two straight-lines
  • Questions to Chapter 7

Chapter 8: Regression Diagnostics and Best Model Selection

  • Part I: Regression Diagnostics
  • Part II: Selecting the Best Regression Equation 
  • Questions to Chapter 8