### MS-C1620 - Statistical Inference, Lecture, 10.1.2022-13.4.2022

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

#### LEARNING OUTCOMES

After passing the course the students can conduct simple statistical analyses. They know how to calculate summary statistics and how to properly visualize data. Students are able to select suitable summary statistics and parameter estimates for different types of data sets and construct bootstrap confidence intervals for the estimated parameters. Students are able to select suitable statistical tests for different testing settings. They know how to apply different t-tests, chi-square tests and nonparametric tests and understand the general statistical assumptions that are required for applying these tests. Students are able to detect different types of dependencies between variables and they are familiar with univariate and multivariate linear regression analysis. They can conduct linear regression analysis in practice and they understand the underlying model assumptions.

Credits: 5

Schedule:

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Jukka Kohonen

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

#### CONTENT, ASSESSMENT AND WORKLOAD

##### Content
• valid for whole curriculum period:

The course is an introduction to statistical analysis and statistical inference. Course topics include estimation, simple parametric and nonparametric tests, statistical dependence and correlation, linear regression analysis and analysis of variance. Software R is used in this course.

##### Assessment Methods and Criteria
• valid for whole curriculum period:

lectures, exercises and course exam OR exam only

##### Workload
• valid for whole curriculum period:

Lectures 24 h (2), Exercises 24 h (2), Homework assignments 40 h, reading and studying the lecture materials 40 h

#### DETAILS

##### Substitutes for Courses
• valid for whole curriculum period:

##### Prerequisites
• valid for whole curriculum period:

#### FURTHER INFORMATION

##### Further Information
• valid for whole curriculum period:

Teaching Language : English

Teaching Period : 2022-2023 Spring III - IV
2023-2024 Spring III - IV

Enrollment :

Registration for Courses: In Sisu (sisu.aalto.fi).