9.15  10.30  Simple linear regression 1.
The model, the parameters, estimation and inference. All STATA code used at the lecture. Data set used at the lecture STATA: lung.dta. SPSS: lung.sav. 
10.30  12.00  Exercises.
The lung data STATA lung.dta. SPSS: lung.sav. 
12.00  13.00  Lunch break 
13.00  14.30  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: STATA: lung.dta and gfrdata.dta. SPSS: lung.sav and gfrdata.sav. 
14.30  16.00  Exercises.
The gfr data STATA gfrdata.dta SPSS: gfrdata.sav. The glyco data STATA glyco.dta SPSS: glyco.sav. 
9.15  9.30  Summarizing yesterdays exercises. 
9.30  10.30  Multiple linear regression 1.
The model, the parameters, estimation and inference. Checking the model. Data set used at the lecture STATA: fram200.dta. SPSS: fram200.sav. 
10.30  12.00  Exercises.
Data STATA: lung.dta. and fram200.dta. SPSS: lung.sav. and fram200.sav. 
12.00  13.00  Lunch break

13.00  14.30  Multiple linear regression. 2
Working med categorical explanatory variables Interaction/effectmodification. All of STATA code used at the lecture. Data set used at the lecture STATA: fram200.dta. SPSS: fram200.sav. 
14.30  16.00  Exercises.
Data STATA: lung.dta. and fram200.dta. SPSS: lung.sav. and fram200.sav. 
9.15  10.00  Summarizing the home work exercises.

10:15  12:00  Logistic regression
Odds ratios via logistic regression Continuous independendt variables Categorical independendt variables Interactions Wald and likelihood ratio test The logistic regression model in general Most of STATA code used at the lecture. Data set used at the lecture STATA: obese.dta. SPSS: obese.sav. 
12.00  13.00  Lunch break

13.00  14.30  The lecture continued 
14.30  16.00  Exercises.
The prostate cancer data set prossub.dta. SPSS: prossub.sav. 
8.30  9.15  Exercises.  Thursday afternoon continued 
9.30  11.15  Working with linear and logistics regression models
Diagnostics for logistic regression Test and estimation after the model has been fitted in STATA Colinearity Things to consider when specifying a model Model selection an its consequences All STATA code used at the lecture. Data set used at the lecture STATA: obese.dta. and serumchol194.dta. SPSS: obese.sav. and serumchol194.sav. 
11.15  11.45  Lunch break

11.45  13.45  Extensions
Conditional logistic regression Models for relative risk or risk differences Clustered data Nonlinear regression All STATA code used at the lecture. Data set used at the lecture STATA: obese.dta. 
13.45  14.45  Course evaluation
