30219 Advanced seminar

WiSe 20/21: Intermediate Regression Analysis

Jan Paul Heisig

Information for students

Regular hands-on exercises and assignments will help students apply these methods to actual data analysis problems in Stata. Some prior knowledge of Stata is therefore highly recommended.

Comments

  • the linear probability model and its limitations;
  • the basic logic of logistic regression and other generalized linear models;
  • prediction and interpretation (including average marginal/partial effects and marginal/partial effects at the mean);
  • fundamentals of maximum likelihood estimation;
  • model selection and goodness of fit; model diagnostics;
  • (non-)comparability of coefficients across models and groups (optional).
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15 Class schedule

Regular appointments

Thu, 2020-11-05 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2020-11-12 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2020-11-19 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2020-11-26 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2020-12-03 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2020-12-10 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2020-12-17 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2021-01-07 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2021-01-14 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2021-01-21 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2021-01-28 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2021-02-04 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2021-02-11 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2021-02-18 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Thu, 2021-02-25 12:00 - 14:00

Lecturers:
Univ.-Prof. Dr. Jan Paul Heisig

Subjects A - Z