Course description

Data Analysis with Programming (Planned autumn 2020)

Introduction

Complete course description will be ready summer 2020.

The students will learn the most important techniques in applied statistics and data analysis, with an emphasis on linear regression. Students are given hands-on experience with data analysis projects, and will gain further knowledge in working with data, using descriptive statistics to motivate models, and using models to turn data into actionable knowledge. Data examples for business applications will be given.

Course content

The following topics will be covered using Python as statistical analysis system.

  • Statistical inference for simple linear regression and basic
    residual analysis.
  • Linear regression with a linear and quadratic term: Introduction to
    multiple linear regression and OLS.
  • Briefly on the multiple linear regression model and general OLS. Residuals.
  • Multiple linear regression with categorical variables: OLS estimation, ANOVA and the comparison of group averages. Introduction to the F-test.
  • Introduction to multiple linear regression modelling: Assumptions, inference, diagnostics and influential observations.
  • An introduction to time-series models: Autoregressive models.

Learning outcome knowledge

The student will gain understanding of the important linear regression modelling framework, its motivation, assumptions, and applications, as well as a brief introduction to time-series models that are implementable through standard linear regression methods through an introduction to autoregressive models.