Statistical analysis in research Field of study: Computer Science
Programme code: W4-S2INA19.2021

Module name: Statistical analysis in research
Module code: W4-INA-S2-20-1-ASwPB
Programme code: W4-S2INA19.2021
Semester: summer semester 2021/2022
Language of instruction: English
Form of verification: course work
ECTS credits: 2
Description:
The module's purpose is to present the basics of data analysis, including descriptive statistics, graphic methods for the presentation of qualitative and quantitative data, and statistical inference elements. Content: The module's purpose is to present the basics of data analysis, including descriptive statistics, graphic methods for the presentation of qualitative and quantitative data, and statistical inference elements. Content: 1. Descriptive statistics: average measures, measures of variability, dispersion, asymmetry, correlation analysis. 2. Graphic methods for presenting qualitative and quantitative data: histogram, frequency diagram, scatter plot, box plot. 3. Elements of statistical inference: concepts of the null and alternative hypothesis, significance level and p-value. Selection of test depending on the hypothesis and the data: Student's t-test, Wilcoxon test, Friedman test, Kruskal-Wallis test, Fisher test, chi-square test.
Prerequisites:
(no information given)
Key reading:
1. D.S. Moore, G.P. McCabe, Introduction to the Practice of Statistics, W.H. Freeman & Co., 2000. 2. C. Heumann, M. Schomaker, Shalabh, Introduction to Statistics and Data Analysis, Springer, 2016
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
The student knows the average measures of variability, dispersion, asymmetry, correlation analysis and can use them. [M_001]
 K_W01 [1/5] K_W09 [1/5] K_U03 [1/5] K_U08 [1/5]
The student knows various methods of graphic presentation of qualitative and quantitative data. They can choose the graph for the data and create it. [M_002]
 K_W09 [1/5] K_U01 [1/5]
The student knows statistical inference. They can use selected statistical tests to confirm the significance of the hypotheses. They can choose the right test, depending on a hypothesis and data. [M_003]
 K_W01 [1/5] K_W09 [1/5] K_U01 [1/5] K_U03 [1/5] K_U09 [1/5]
They can use the selected program to perform statistical analysis and to confirm the hypotheses. Based on the obtained experimental results, they can conclude and show their statistical significance. [M_004]
 K_U01 [1/5] K_U03 [1/5] K_U07 [1/5] K_K04 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Test [W_001]
The test verifies knowledge and skills based on completed tasks.
 M_001 M_002 M_003
Examination reports [W_002]
The students prepare written reports and their oral presentation at a specified time as verification of gained skills during problem-solving.
 M_001 M_002 M_003 M_004
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [Z_001]
The lectures present concepts and facts from the programme contents listed in the module and illustrate them with many examples.
 15
The students self-study the lectures and recommended literature.
 15 Test [W_001]
laboratory classes [Z_002]
During the laboratory classes, the students complete tasks with the teacher's help, which develops the skills listed in the set of learning outcomes of the module.
 15
The students improve the skills listed in the set of learning outcomes of the module.
 15 Test [W_001] Examination reports [W_002]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)