EBA 3500 Data Analysis with Programming
EBA 3500 Data Analysis with Programming
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 and applications will be given.
After completing the course, the student should know:
- What a statistical model is, and how they may be of use in prediction, modelling, and explanations.
- The basic mathematical underpinnings of the linear regression model.
- The scope and limitations of a multiple linear regression model.
- That how a dataset is gathered (e.g. sampling design) and our degree of substantial knowledge of the data drives how strong statements we may make using statistical tools.
- Some of the most important practical limitations in using the multiple linear regression framework.
- The most common sets of assumptions underlying consistency, as well as exact or approximate normality for parameter estimates in linear regression.
- Know the basic interpretation of a multiple linear regression model, and when this may break down.
After completing the course, the students will:
- Possess basic skills in data-selection, data-reorganization, data-transformations.
- Develop further skills in descriptive statistics and data-visualization.
- Be able to use descriptive and transformative techniques to formulate a reasonable multiple linear regression model for a dataset.
- Develop basic and hand-on experience with model diagnostics, as well as having basic skills in choosing between competing models.
- Choosing appropriate exploratory tools for getting an overview of large datasets.
- Be able to turn a practical question into a question that can be addressed with multiple linear regression.
- Be able to use statistical tools to decide on a course of action for a given practical problem.
- Further develop programming skills in Python.
- Perform simulation studies to assess how well a statistical method performs.
Students will understand that in many situations a statistical analysis will help making better decisions, but also be aware that statistical methods can be wrongly applied and lead to false conclusions.
The following topics will be covered using Python as statistical analysis system.
- Statistical inference for simple linear regression and basic
residual analysis. - Briefly on the multiple linear regression model and general OLS. Residuals.
- Linear regression with a linear and quadratic term: Introduction to
multiple linear regression and OLS. - 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.
Teaching will done using a blend of lectures and applied data projects in Python.
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Higher Education Entrance Qualification
Covid-19
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
Teaching
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
EBA3400 Programming, data extraction and visualisation, EBA 2910 Mathematics for Business Analytics or equivalent courses.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group/Individual (1 - 3) Duration: 1 Week(s) Comment: Home exam. An oral defence may be required. Exam code: EBA 35001 Grading scale: ECTS Resit: Examination every semester |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: EBA 35002 Grading scale: ECTS Resit: Examination every semester |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | Lectures and blended learning with projects for students. |
Feedback activities and counselling | 9 Hour(s) | Project-work under supervision. |
Examination | 15 Hour(s) | Work related to the home exam. |
Student's own work with learning resources | 75 Hour(s) | |
Group work / Assignments | 62 Hour(s) | |
Examination | 3 Hour(s) | Final exam |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.