[ 551OKMELMOK14 ] KV Linear models

(*) Unfortunately this information is not available in english.
Workload Education level Study areas Responsible person Hours per week Coordinating university
4 ECTS B2 - Bachelor's programme 2. year Statistics Helmut Waldl 2 hpw Johannes Kepler University Linz
Detailed information
Pre-requisites (*)keine
Original study plan Bachelor's programme Statistics and Data Science 2021W
Objectives Students know methods for classic linear regression and can apply these to analyse data
Subject simple linear regression (model assumptions, least squares estimation, confidence intervals for regession parameter, hypothesis tests, coefficient of determination, residual analysis)

multiple regression: modelling of effects (dummy and effect coding, interactions, nonlinear effects), point estimation(WLS, ML), confidence regions, F tests, modell choice, model fit, multicollinearity

variance and covariance analysis

Criteria for evaluation homework and written exam
Methods presentation by the lecturer

presentation of homework examples by students

Language German
Study material Fahrmeir L., Kneib T. , Lang S. and Marx B., Regression: Models, Methods and Applications, Springer, 2013
Changing subject? No
Corresponding lecture (*)4MSOMKV: KV Ökonometrische Modelle (Statistik) (4 ECTS)
On-site course
Maximum number of participants 40
Assignment procedure Assignment according to priority