105.725 General Regression Models
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2024S, VU, 3.0h, 5.0EC
TUWEL

Course evaluation

Properties

  • Semester hours: 3.0
  • Credits: 5.0
  • Type: VU Lecture and Exercise
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to (i) apply modern regression/statistical learning methods to build predictive models, (ii) select and validate statistical learning models, (iii) assess model fit and error and (iv) use the R language for modern regression and data analysis.

Subject of course

  • Matrix Algebra Review
  • Multivariate Normal Distribution
  • Review of Linear Models: 
    • Simple Linear Regression, 
    • Assumptions for Linear Models, 
    • Ordinary Least Squares (OLS) estimators 
  • Inference: 
    • inference for the slope and the intercept, 
    • interpretation of results, 
    • prediction, 
    • F-tests
  • Regression Diagnostics: 
    • outliers, influential points, 
    • graphical diagnostics, remedies, 
    • weighted least squares
    • Robust regression
  • Multiple linear regression: 
    • estimation, 
    • prediction, 
    • diagnostics, 
    • nested models, multi-collinearity, ridge regression, 
    • qualitative predictors, mixture of continuous and categorical variables,
    • model building, variable selection and model validation, LASSO
  • Dimension reduction
  • Principal Component Analysis and Principal Component Regression 
  • Partial Least Squares
  • Sufficient Dimension Reduction (lecture notes)
  • Smoothing techniques 
    • Kernel smoothing
    • local polynomial fitting
    • splines
    • generalized additive models 
    • regression trees
    • random forests
  • Neural Nets
  • Regression models with binary response (binary classification), simple and multiple logistic regression
  • Generalized linear model: 
    • exponential family, 
    • link functions, 
    • parameter estimation, 
    • inference and prediction

Teaching methods

The material will be presented in lecture slides in conjunction with derivations on board. Course related information and material, including notes and details about course assignments and exams will be posted in TUWEL.

The linear regression material and some nonparametric regression material is covered in Linear Regression Analysis by Seber and Lee https://onlinelibrary.wiley.com/doi/book/10.1002/9780471722199. Other reference books are Applied Linear Statistical Models, 5th Edition by Kutner et al.; Practical Regression and ANOVA Using R at ftp://cran.r-project.org/pub/R/doc/contrib/Faraway-PRA.pdf ; Introduction to Statistical Learning with applications in R by James, Witten, Tibshirani & Hastie; Elements of Statistical Learning by Hastie, Tibshirani & Friedman.

The statistical software we will use is R. It can be downloaded from the R home page . RStudio offers a GUI R platform.

 

Mode of examination

Written

Additional information

The prerequisite for the course is 

105.596 VO Econometrics 1: Linear Models

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue13:00 - 16:0005.03.2024 - 25.06.2024Seminarraum AEEG-1 General Regression Models
Tue13:00 - 16:0018.06.2024Hörsaal 14 General Regression Models
General Regression Models - Single appointments
DayDateTimeLocationDescription
Tue05.03.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue12.03.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue19.03.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue09.04.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue16.04.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue23.04.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue30.04.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue07.05.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue14.05.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue28.05.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue04.06.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue11.06.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models
Tue18.06.202413:00 - 16:00Hörsaal 14 General Regression Models
Tue25.06.202413:00 - 16:00Seminarraum AEEG-1 General Regression Models

Examination modalities

The final exam will be written and will cover all topics presented in class.

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Thu14:00 - 16:0027.06.2024Sem.R. DB gelb 03 written13.06.2024 08:00 - 25.06.2024 23:59TISSGeneral Regression Models final
Thu - 26.06.2025written12.06.2025 08:00 - 24.06.2025 23:59TISSGeneral Regression Models final

Course registration

Begin End Deregistration end
20.02.2024 17:00 16.03.2024 23:59 25.03.2024 22:59

Curricula

Literature

No lecture notes are available.

Previous knowledge

Basic probability and statistics; Linear algebra; Econometrics 1: Linear Models.

Preceding courses

Miscellaneous

  • Attendance Required!

Language

English