## Type of course

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.

Bachelor of science degree or equal. Recommended prerequsites is STA-0001 Applied statistics 1 or STA-1001 Probability and statistics. Application code is 9371.

## Course overlap

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

## Course content

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 hypotesis testing.

This course teaches the following: Multiple linear regression analysis. Correlations. One- and two-way analysis of variance. Understanding the regression model and the assumptions for the model.Testing significance of the variables. Determining a best-fitting model.Evaluating the model. Using R2, the multiple correlation coeffisient. Examples and exercises with real data from several areas using statistical software.

## Recommended prerequisites

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

## Objectives of the course

After the course the student should:

• obtain a solid foundation in applied multiple regression analysis and analysis of variance
• be able to make an independent judgment as to whether regression- or analysis of variance is an appropriate method for the present data
• be able to utilize the program package SPSS in analyzing data

In more detail the student should have knowledge about:

• 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 between independent variables.
• The concept of interaction between independent variables.
• Polynomials and transformations of variables.
• Criteria (R2, F, Mallow's Cp) and strategies for selecting the best model (forward, backward or stepwise)
• Regression diagnostics, such as analyzing residuals to identify possible outliers, checking the model assumptions, colinearity.
• One-way and two-way analysis of variance, balanced design, fixed and/or random factors
• Interpretation of regression results. The significance and the importance of the selected independent variables in explaining the response variable.

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

## Language of instruction and examination

The language of instruction and the syllabus is English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.

## Teaching methods

Teaching: Approx 70 h

## Information to incoming exchange students

Do you have questions about this course? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412

## Examination

School exam 11.12.2024 09:00
4 Hours A–E, fail F

### Coursework requirements:

To take an examination, the student must have passed the following coursework requirements:

Mandatory homework sets Approved – not approved
UiT Exams homepage

## Re-sit examination

Students having failed the last ordinary examination are offered a re-sit examination early in the following semester.