Probability and Statistics 1 (STA100)

Content

The course gives an introduction to descriptive statistics and basic probability theory for discrete and continuous probability models. Introductory theory for estimation and for statistical hypothesis testing in the most common situations is presented. Emphasis is made on both theoretical understanding and applications. An introduction to simulation is also given. Use of the software R for data-analysis and modelling is an integrated part of the course.

Topics covered: Introduction to basic probability theory, included conditional probability, expectation, variance and the most common probability distributions like binomial, hypergeometric, poisson, exponential and normal. Introduction to simulation. An introduction to point estimation, confidence intervals and hypothesis testing in one and two sample situations. An introduction to correlation, regression analysis, analysis of variance and chi-squared tests.

Learning outcome

After having completed the course the student should:

• Be able to use basic methods for analysis and presentation of data.
• Be able to do basic probability calculations.
• Know what a random variable, probability distribution, expectation and variance is.
• Be able to calculate expectation, variance and probabilities for random variables and simple functions of random variables.
• Be able to use basic probability distributions like binomial, poission, hypergeometric, exponential and normal.
• Be able to use the central limit theorem.
• Be able to find estimators and calculate confidence intervals for some important parameters in probability distributions.
• Have a basic understanding of hypothesis testing and be able to perform hypothesis testing for one and several samples.
• Know the theory for, and be able to use correlation, regression analysis and simple analysis of variance.
• Know the assumptions for the various methods and be able to judge whether the assumptions are fulfilled.
• Be able to use chi square tests
• Be able to use some R for basic data analysis and simulation.

Required prerequisite knowledge

Recommended prerequisites

Exam

Coursework requirements

Compulsory exercises have to be approved in order to take an examination.

Mandatory work demands (such as hand in assignments, lab- assignments, projects, etc) must be approved by subject teacher three weeks ahead of examination date.

Course teacher(s)

Course coordinator:

Head of Department:

Method of work

Open for

Course assessment

Literature