STAT100 Statistics
Credits (ECTS):10
Course responsible:Kathrine Frey Frøslie
Campus / Online:Taught campus Ås
Teaching language:Norsk
Limits of class size:1000
Course frequency:Each spring and autumn
Nominal workload: Lectures or educational videos/indivdual study: 125 hours Working with mandatory assessments in colloquium groups or individually: 65 hours. Working with exercises at scheduled hours: 60 hours.
Teaching and exam period:This course starts in the parallel. This course has teaching/evaluation both in Autumn parallel and Spring parallel.
About this course
Numerical literacy and a basic understanding of quantitative research methods are cornerstones of scientific knowledge and communication within the fields of science and medicine. Therefore, in almost all educations in these fields, there is a mandatory course in introductory statistics, often at the bachelor level.
Topics that are addressed in this course: Descriptive statistics. Basic probability, conditional probability, discrete and continuous variables, expectation and variance. Binomial distribution and normal distribution. Covariance, correlation, and independence. Estimation, confidence intervals, and hypothesis testing. Z-tests, T-tests and non-parametric test. Simple linear regression. One-way analysis of variance. Chi square tests. Application of simple statistical software (R).
Learning outcome
KNOWLEDGE: The students will learn the basic concepts in probability theory and statistics. They will get familiar with the assumptions and the applications of the most commonly used statistical methods applied in science and everyday life.
SKILLS: The students should be able to carry out simple statistical analyzes. They should be able to interpret the results of the analyzes and pass on what has been done, the results and the weaknesses and limitations of the analyzes. 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 simple problems in their studies and later in the professional life and perform simple analyzes on their own data. They should also be able to ask critical questions about the statistical results presented to them (e.g. in the media or in reasearch) and assess the sustainability of these.
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