KHÓA CHUYÊN ĐỀ HỒI QUY TUYẾN TÍNH - SPECIAL COURSE OF MODEL REGRESSION ANALYSIS

Date

Time

Topic

Contents

Day 1

8h30 - 11h30

Lesson 1: Simple Linear Regression

• Simple Linear Regression introduce
• Best Fit Line
• The Simple Linear Regression Model
• The Common Error Variance
• (Pearson) Correlation Coefficient r
• R-squared Cautions
• Hypothesis Test for the Population Correlation Coefficient
• Inference for the Population Intercept and Slope

13h00  - 16h00

Lesson 2:

SLR Model Evaluation

Lesson 3:

SLR Estimation & Prediction

• The Analysis of Variance (ANOVA) table and the F-test
• The Lack of Fit F-test
• Confidence Interval for the Mean Response
• Prediction Interval for a New Response
• Further Example

Day 2

8h30 - 11h30

Lesson 4: Multiple Linear Regression

• The Multiple Linear Regression Model
• A Matrix Formulation of the Multiple Regression Model
• Further Examples

13h00 - 16h00

Lesson 5: MLR Model Evaluation

MLR Estimation, Prediction & Model Assumptions

• Three Types of Hypotheses
• The General Linear F-Test
• The Hypothesis Tests for the Slopes
• Lack of Fit Testing in the Multiple Regression Setting
• Prediction Interval for a New Response
• MLR Model Assumptions

Day 3

8h30 - 11h30

Lesson 6: Categorical Predictors

• The Basics
• Two Separate Advantages
• Additive Effects and Interaction Effects
• Leaving an Important Interaction Out of a Model

13h00 - 16h00

Lesson 7: Multicollinearity

• Multicollinearity introduction
• Detecting Multicollinearity Using Variance Inflation Factors
• Reducing Data-based Multicollinearity
• Reducing Structural Multicollinearity

Day 4

8h30 - 11h30

Lesson 8: Logistic and Poisson Regression

• Logistic Regression
• Polytomous Logistic Regression
• Poisson Regression

13h00 - 16h00

Lesson 9: Nonlinear and Exponential   Regression

• Generalized Linear Models
• Nonlinear Regression
• Exponential Regression Example