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. 
9.15  9.30  Summarizing Mondays exercises. 
9.30  10.30  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 . 
10.30  12.00  Exercises .
Data set used at the exercises: lung and fram200 . 
12.00  12.30  Lunch break

12.30  14.00  Lecture:
Prior to Stata 11 Multiple linear regression  2 Stata 11+12 Multiple linear regression  2 Stata 13 Multiple linear regression  2 Working med categorical explanatory variables Interaction/effectmodification. 
14.00  15.30  Exercises .
Data set used at the exercises: lung and fram200 . 
9.15  12.00  Exercises .
Data set used at the exercises: serumchol. 
12.00  12.30  Lunch break

12.30  13.30  Exercises continued . 
13.30  15.30  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 . 
9.15  10.00  Discussing the home work.

10:15  12:00  Lecture:
Prior to Stata 11 Logistic regression . Stata 11 Logistic regression . Odds ratios via logistic regression Continuous independent variables Categorical independent variables Interactions Wald and likelihood ratio test The logistic regression model in general Most of Stata code used at the lectures. Data set used at the lecture: obese and case_control. 
12.00  12.30  Lunch break.

12.30  14.00  The lecture continued. 
14.00  15.30  Exercises
Data set used at the exercises: obese. 
9.15  10.00  Exercises.  Monday afternoon continued 
10.15  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  15.30  Exercises
Data set used at the exercises: coffee. 
9.15  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 Models for relative risk or risk differences Missing data Binary data with several random components Some of Stata code used at the lectures. 
15.00  15.30  Course evaluation
