STAT200 Regression Analysis

The course deals with statistical methods that are essential in all interdisciplinary projects that collect and process data. Numerical literacy and a basic understanding of quantitative research methods are cornerstones of scientific knowledge and communication within the fields of science and medicine. The methods included in the course are therefore relevant to several of UN sustainability goals that includes data collecting and analyzes.

Course content: Estimation and testing in simple and multiple linear regression models and in logistic regression models. Subset selection. Analysis of residuals and assessment of models. Predictions. Application of statistical software.

KNOWLEDGE: The students will learn how to analyze data using linear, both single and multiple regression, and also with elements categorical variables. The student will also get a brief introduction to logistic regression.

SKILLS: The students should be able to perform statistical analyzes with the methods mentioned in Knowledge. They should be able to use different models on the same data and be able to validate the models and determine which ones are best suited. They should be able to interpret the results of the analyzes and convey what has been done, the results and the weaknesses and limitations of the analyzes and models. They should understand the importance of having good data (e.g. representativeness, independence) in order to draw useful and correct conclusions from a survey.

GENERAL COMPETENCE: The students should be able to apply what they have learned to problems in their studies and later in their professional life and perform analyzes on their own data. At the same time, they should be able to ask critical questions about statistical results that they generate or are presented to them and assess the quality of these results.