Data Analysis with Programming
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.
The following topics will be covered using Python as statistical analysis system.
- Statistical inference for simple linear regression and basic
- 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.
This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.