Entry requirements

5 credits in mathematics. Participation in Single Variable Calculus or Derivatives and Integrals, which also may be taken in parallel with this course.

Learning outcomes

On completion of the course, the student should be able to:

• conduct simple calculations of probabilities and conditional probabilities, in particular by using methods for independent events;
• give an account of basic properties for random variables and for the most common probability distributions, as well as calculations of expectations and variances for these distributions;
• explain the law of large numbers and the central limit theorem, and conduct approximate calculations of probabilities based on these, in particular by using normal distribution methods;
• use probabilistic methods in some area of application;
• explain the basics of statistical surveys and for methods of descriptive statistics;
• construct point estimates and interval estimates in some typical statistical contexts;
• construct estimates in simple linear regression.

Content

The probability concept. Independence, conditional probability. Random variables. Common probability distributions. Expected value, variance. The law of large numbers. The central limit theorem. Applied probability problems. Statistical investigations. Descriptive statistics. Point and interval estimation. Regression analysis.

Instruction

Lectures and problem solving sessions.

Assessment

Written examination at the end of the course (4 credits), assignments (1 credit).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.