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 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  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: 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 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. 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  Lecture:
Multiple linear regression  2 .
Working med categorical explanatory variables Interaction/effectmodification. 
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  Lecture: 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 lectures. Data set used at the lecture STATA: obese.dta and case_control.dta . SPSS: case_control.sav and 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 . 
9.15  10.00  Exercises.  Monday afternoon continued 
10.15  12.00  Lecture:
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 . 
12.00  13.00  Lunch break

13.00  15.00  Lecture: 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 , oralcancer.dta , FEV.dta and AZT.dta . SPSS: obese.sav , oralcancer.sav , FEV.sav and AZT.sav . 
15.15  16.00  Course evaluation
