9.15  10.30  Lecture:
Simple linear regression 1 .
The model, the parameters, estimation and inference. All Stata code used at the lecture. Data set used at the lecture: lung 
10.30  12.00  Exercises .
Data set used at the exercises: lung. 
12.00  13.00  Lunch break 
13.00  14.30  Lecture:
Simple linear regression 2 .
Checking the model, residuals, leverage, diagnostics plots,transformation of variables. Most of Stata code used at the lecture. Data set used at the lecture: lung and gfrdata. 
14.30  16.00  Exercises .
Data set used at the exercises: gfrdata and glyco. How to get from the log  model to the original scale. 
9.00  9.15  Summarizing Mondays exercises. 
9.15  10.15  Lecture:
Multiple linear regression  1
The model, the parameters, estimation and inference. Checking the model. All of Stata code used at the lectures today. Data set used at the lecture: fram200 . 
10.15  12.00  Exercises .
Data set used at the exercises: lung and fram200 . 
12.00  12.30  Lunch break

12.30  14.00  Lecture:
Stata 13 Multiple linear regression  2 Working med categorical explanatory variables Interaction/effectmodification. 
14.00  15.15  Exercises .
Data set used at the exercises: lung and fram200 . 
9.00  12.00  Exercises .
Data set used at the exercises: serumchol. Some answers to the exercises 
12.00  12.30  Lunch break

12.30  13.30  Exercises continued . 
13.30  15.15  Lecture:
Linear regression, collinerarity, splines and extensions
Collinearity Restricted cubic splines Clustered data Some off Stata code used at the lecture. Data set used at the lecture : serumchol194 , Framingham, and FEV . 
9.00  11.00  Lecture:
Regression model for binary data .
The logistic regression model in general Most of Stata code used at the lectures. Data set used at the lecture: obese . 
11.00  12.00  Morning exercises
Data set used at the exercises: obese. 
12.00  12.30  Lunch break.

12.30  14.00  The lecture continued. 
14.00  15.15  Afternoon exercises
Data set used at the exercises: obese. 
9.00  10.00  Exercises.  Monday afternoon continued 
10.00  12.00  Lecture:
Modelbuilding in regression models
Modelbuilding: this to consider Confounding and adjustment Model selection an its consequences Overfitting A strategy 
12.00  12.30  Lunch break

12.30  15.15  Exercises
Data set used at the exercises: coffee. 
9.00  12.00  Working with wednesdays exercise 
12.00  12.30  Lunch break

12.30  13.30  Discussing wednesdays exercise 
13.30  15.00  Lecture:
Working with logistics regression models and Extensions .
Diagnostics for logistic regression Conditional logistic regression Missing data Binary data with several random components Some of Stata code used at the lectures. 
15.00  15.15  Course evaluation
