Course content

This course gives an introduction to various topics within statistical analysis and how to conduct data analyses using different tools and methods.

Topics covered in the course include:

• Basic data handling, processing, and visualization
• Hypothesis testing
• Chi-square tests
• Analysis of Variance (ANOVA)
• F-tests, one-factor, and two-factor models
• Repetition of simple linear regression and the least squares method
• Multiple linear regression models
• Confidence intervals and hypothesis testing for regression coefficients
• Correlation analysis
• Non-linear regression: quadratic, inverse, and log-linear models
• Multicollinearity and autocorrelation
• Residual analysis
• Non-parametric statistics

Learning outcome

Knowledge

Upon completion of the course, students should have acquired knowledge of how to conduct and apply data analysis using various statistical techniques. This includes knowledge of:

• The most commonly used types of statistical tests
• Simple and multiple regression analysis
• Hypothesis testing with regression analysis
• Strengths and weaknesses of various statistical analysis techniques

Skills

Upon completion of the course, students should be able to apply acquired knowledge in conducting data analyses. This includes the ability to:

• Handle, process, and visualize data
• Assess the use of tests and methods based on data characteristics
• Perform various types of statistical tests and methods using data tools
• Conduct data analysis and interpret results using multiple regression

General competence

The student should have an understanding of all stages involved in high-quality data analysis. They should have a thorough understanding of the general principles for conducting statistical tests and substantial knowledge of the possibilities and limitations in using multiple regression models.

Learning methods and activities

Lectures and exercises.

Compulsory assignments

• Obligatorisk skriftlig innlevering

Specific conditions

Recommended previous knowledge

Introductory courses in mathematics and statistics, MET1001 and MET1002 or equivalent.

Required previous knowledge

None.

Course materials

The final syllabus is announced at the beginning of the semester.

Credit reductions