Applied Statistics 2

The course is normally included in Master's degree programs in other subjects than statistics and mathematics. It may also be taken independent of study program upon approval of the Department of mathematics and statistics. This course is also available for inbound exchange students.

The course is intended for students who collect and analyze data, and who need to know more about the use of statistics within their own field. Students attending the course should be familiar with the concepts of random variables and distributions, normal distribution, estimation, confidence interval, and hypothesis testing.

This course teaches the following:

• Single and multiple linear regression analysis

• Single and multiple correlations

• Confounding and interactions

• One- and two-way analysis of variance

• Understanding the regression model and the assumptions behind it

• Testing the significance of the different variables

• Selecting a best-fitting model based on regression diagnostics

• Randomized blocks and model design

The concepts will be presented through many examples and practical exercises with real data from several areas using Python as statistical software. A short introduction to Python will be given as well.

Bachelor of science degree or equivalent.

Application code is 9371.

Knowledge - The candidate will learn:

• The assumptions for using regression- and analysis of variance.

• The two types of independent variables (covariates): measurement- and nominal (categorical) variables.

• The concept of confounding.

• The concept of interaction between independent variables.

• Polynomial regression and transformation of variables.

• Criteria (R2, F, Mallows’ Cp) and strategies for selecting the best model (forward, backward, and stepwise regression, and best subset of variables).

• One- and two-way analysis of variance, balanced design, fixed and/or random factors.

• The general linear model as a framework for multiple regression and Anova models.

Skills - The candidate will become familiar with:

• Prepare data and perform regression analysis in Python.

• Use regression diagnostics, such as analysis of residuals to identify possible outliers, check the model assumptions, and address problems with collinearity.

General expertise - The candidate will learn how to:

• Choose a reasonable linear model to analyze his/her data sets.

• Explain the regression results to a person who is not familiar with statistical thinking.

• Interpret regression results. Understand the significance and the importance of the selected independent variables in explaining the response variable.

Anbefalte forkunnskaper

STA-0001 Applied statistics 1, STA-1001 Probability and statistics

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

• HEL-3070 Biostatistics II 2.5 ects
• HEL-3070 Biostatistics 2.5 ects
• FSK-3006 Model theory and data processing methods 3 ects

Language of instruction and examination

Schedule

This course is open to incoming exchange students.

Study level: Master’s

Admission prerequisites:
This course has admission prerequisites, which are listed under the Admission requirements section. Please review this information carefully before adding the course to your Learning Agreement.

For details on how to apply for exchange, course selection guidelines, or to contact the Incoming Admissions Team, please visit: Admissions for Student Exchange.

Re-sit examination