Statistics

Basic part (5 credits): Descriptive statistics. Probability of events, combinatorics and conditional probability. Stochastic variables, expectation and variance. Covariance, correlation and independence. Common probability distributions (e.g., binomial, poisson, exponential and normal distribution). The central limit theorem. Parameter estimation and confidence intervals. One-sample hypothesis tests. Simple linear regression.

Special part (2.5 credits): Multiple linear regression, classification, cluster analysis.

Knowledge

The candidate is familiar with the basic ideas in probability and statistics. The candidate has knowledge about simple statistical models and processes that are often used within his/her field of study. The candidate knows how to use statistics in a comprehensive way and understands that statistics is a necessary tool for measuring, describing and evaluating results. The student also knows how to use basic statistical inference methods to describe processes and populations based on independent trials and random samples. The candidate knows some methods for statistical learning and data science.

Skills

The candidate can:

• Present and describe the characteristics of a data material using descriptive statistics, tables and figures.
• Calculate the probability of events and conditional probabilities, using e.g. combinatorics, stochastic variables, the most common probability distributions (e.g., binomial, poisson, exponential and normal distribution) and the central limit theorem.
• Perform simple methods for statistical inference such as parameter estimation, confidence intervals, one-sample hypothesis tests, correlation and simple linear regression.
• Apply statistical principles and concepts in his/hers professional field.
• Use Python, or a similar statistical software, to perform basic statistical analysis.
• Perform regression, classification and cluster analysis on different data sets, and describe results from methods for statistical learning and data science.

General competence

The candidate sees the importance of statistical knowledge and expertise in the engineering role and is able to communicate with professionals about engineering problems by using statistical concepts and expressions. The candidate has gained confidence in simple statistical analysis, statistical learning and data science through student activities such as exercises and project work. This competence provides a platform for further engineering studies, and for various types of applications in industry, consulting and the public sector.

Lectures, collaborative project work and exercises.