KIUA1008 Anvendt regresjon
- Number of credits10
- Teaching semester2025 Autumn
- Language of instructionNorwegian/English
- CampusHamar
- Required prerequisite knowledge
None
Applied regression provides students with a fundamental understanding of regression analysis and its practical applications in artificial intelligence. Regression analysis is a powerful statistical tool used to explore correlations between variables, make predictions and gain valuable insights from data. Students will acquire skills in data preparation, model selection, interpretation and effective communication of results. The course also provides students with updated information about innovative regression methods that contribute to the development of practical AI solutions and exchange of best practices within the field.
Learning Outcome
Upon successfully passing the course, students will have achieved the following learning outcomes:
The student will have
- knowledge and a comprehensive understanding of regression analysis, including concepts, assumptions and techniques involved in basic linear regression and multiple linear regression, with the capacity for knowledge of polynomial regression, non-linear models and regression with categorical predictors
- knowledge of model evaluation techniques, including assessment of model fit, goodness-of-fit (R-squared, adjusted R-squared) and diagnostic procedures for regression models.
- knowledge of applications of regression analyses in different fields, such as finance, marketing, healthcare and social science, with an understanding of how regression analyses are used to solve real-world problems and draw professionally grounded conclusions and predictions regarding developments
The student will be able to
- build regression models, select suitable predictors, estimate regression coefficients using the least squares method and evaluate model assumptions to implement regression models using statistical software
- evaluate the performance of regression models and diagnose model-related problems in order to assess model fit and identify individual data points that affect the analysis or prediction
- analyse data and interpret regression results and identify meaningful correlations between variables, make predictions and draw insights from regression models
The student will be able to
- use regression analysis techniques
- utilise computer skills and statistical reasoning skills to analyse and interpret data, apply statistical techniques in regression analyses and critically assess the validity and reliability of regression models
- take ethical considerations into account in data analyses, including privacy, confidentiality and recognising the importance of responsible data processing, objective modelling practices and transparent reporting of results
- collaborate on data analysis tasks to communicate findings and interpretations to different stakeholders and target groups
The course comprises a combination of lectures, practical exercises, independent study and academic supervision.
- Two individual assignments in accordance with the course curriculum
- Participation in teaching and laboratory exercises in accordance with the course curriculum
Compulsory coursework requirements that have been passed are valid for 12 months only. Students wishing to take examinations after 12 months must pass the compulsory coursework requirements again in connection with the next scheduled delivery of the course.
- One individual project-based assignment
The assignment is assessed using a grading scale from A-F, where E is the lowest passing grade.
Students are able to choose which language to use for their examination. The available options are Norwegian Bokmål, Nynorsk and English.
Permitted aids:
- Literature
- All printed and written resources
- Any use of AI-generated text and content must be clarified with the lecturer, clearly labelled and academically justified in the submission
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Written assignment | ECTS - A-F | Individual | 100 |