Course content

Sample space and events. Uniform probability model. The probability axioms. Probability laws. Conditional probability. Independence. Combinatorics. The urn model. Random variables. Definition of the integral. Mean value, variance and standard deviation. Discrete and continuous univariate probability distributions. Transformation of random variables. Discrete and continuous bivariate distributions. Covariance and correlation. Independent random variables. Conditional expectation. Moment and cumulant generating functions. Order statistics. Binomial and hyper-geometric distribution. Geometric, Poisson, exponential and normal distribution. The central limit theorem. Introduction to point estimation, interval estimation and hypothesis testing. Litt om programpakker i statistikk.

Learning outcome

1. Knowledge. The student has a firm knowledge in probability and statistical distributions as a basis for statistical inference. Further the student knows and understands central statistical concepts such as estimation, confidence interval and hypothesis testing. 2. Skills. The student is able to recognise statistical standard situations and knows how these best can be analysed. Further the student is able to perform statistical inference for normal distributed data and is able to communicate with statisticians in more complicated situations.

Learning methods and activities

Lectures and compulsory exercises. Written final examination is the basis for the grade awarded in the course. Parts of this course can be taught in English.

Compulsory assignments

• Exercises

Recommended previous knowledge

MA1101 Foundational calculus 1.

Course materials

Will be announced at the start of the course.

Credit reductions