Course code InfT5043

Credit points 2

# Free Statistical Software R

Total Hours in Course80

Number of hours for lectures12

Number of hours for seminars and practical classes12

Independent study hours56

Date of course confirmation04.09.2019

Responsible UnitDepartment of Control Systems

### Course developers Vadības sistēmu katedra

## Līga Paura

Dr. agr. Vadības sistēmu katedra

## Irina Arhipova

Dr. sc. ing.

### Course abstract

Students acquire to use R for reading data, writing functions, making informative graphs, and applying statistical methods. During the studies the real examples related to different subject are using. The course topics are the following: descriptive statistic with R, hypothesis testing for two samples a mean with R, analyse of variance (ANOVA) with R, correlation and regression with R.

### Learning outcomes and their assessment

• Knowledge about possibilities of data manipulating and analysis in R and critical understanding about apply data analyses methods in students research projects (the practical works are developed, assessment tests successfully are written);
• skills to use R software for data analysis, to choose statistical methods according to the research project (the practical works are developed, independent work is developed);
• competences to realize data analysis in master work by R software; perform critical analysis and evaluation of the results (assessment tests successfully are written, independent work is developed and presented in practical classes).

### Course Content(Calendar)

1. Introduction to R. [L – 1h]
2. Data importing, data management in R. [P – 0.5h]
3. Basic computations in R. [P – 0.5h]
4. Computations of statistical parameters in R. [L – 1h, P – 1h]
5. Data graphical presentation in R. [L – 1h, P – 1h]
6. Test of differences - independent t-test. [L – 2h, P – 2h]
7. Test of differences - dependent t-test. [L – 2h, P – 2h]
1st test: Data graphical presentation, tests of differences. [P – 1h]
8. One-way Analysis of Variance (ANOVA) in R. [L – 1h, P – 0.5h]
9. Assumptions of ANOVA test. [L – 1h, P – 1h]
10. Two-way Analysis of Variance (ANOVA) in R. [L – 1h, P – 1h]
11. Assumptions of ANOVA test. [L – 1h, P – 1h]
12. Bivariate correlation analysis in R. [L – 1h, P – 0.5h]
13. Bivariate regression analysis in R. Assumptions of regression analysis. [L – 2h, P – 1h]
14. Working with databases in R. [L – 1h]
2nd test: ANOVA and regression analysis. [P – 1h]

### Requirements for awarding credit points

Formal test with a grade (Ia). The test assignment consists of a test of a practical task on the course subjects and the theoretical subjects acquired during the study course. All practical works and two tests should be executed.

### Description of the organization and tasks of students’ independent work

The organization of independent work during the semester is independently studying literature, using academic staff member consultations. Homework has been developed and defended. Homework: at least two different statistical methods for the real data analysis should be used.

### Criteria for Evaluating Learning Outcomes

Evaluation (Ia) depends of the semester cumulative assessment: 1st test - 40 points, 2nd test 40 points and homework – 20 points. Students who have a cumulative assessment of the study course less than 4 or wish to improve it (at least 4) hold the complex test during the session. The test includes practical part (80%) and theory (20%).

1. An Introduction to R. http://cran.r-project.org/doc/manuals/R-intro.html [skatīts 7.decembrī 2018.]
2. Robert I. Kabacoff (2015) R in action: data analysis and graphics with R. Shelter Island, NY: Manning, 579 p.
3. James Gareth, at al. (2017) An introduction to statistical learning: with applications in R. New York : Springer, 426 p.