Statistical Data Analysis

DESCRIPTION

The course “Statistical data analysis” was created within the project Project MOOC@PB-Modern technologies in the teaching process, co-financed by the European Union European under the European Social Fund, Operational Programme Knowledge Education Development, Priority Axis III. Higher education for economy and development, Measure 3.1 Competences in higher education, within the framework of Competition No. POWR.03.01.00-IP.08-00-MOC/18.

KThis course is mainly dedicated to secondary school graduates or higher who wish to acquire or deepen their statistical knowledge. It will facilitate the understanding of basic concepts of statistics and provide an introduction to selected tools used in data analysis. The course introduces the concepts of: statistics, a characteristic, a statistical unit, mass processes; describes measurement scales and stages of statistical research; as well as measures such as: mean, dominant, quartiles, measures of variance and asymmetry as well as dependence. The basics of statistical inference are also presented, including discussion of the following issues: estimation, a minimum sample size, parametric significance tests. The described issues are illustrated with examples that discuss the procedure step by step. The presented material is also enriched with calculations performed in the Excel file.

INITIAL REQUIREMENTS

Participation in the course requires the knowledge of mathematics at a secondary school level.

OBJECTIVES

The aim of the course is to make participants become familiarised with the procedure for carrying out statistical data analysis and the measures used in it, and to acquire the skills to interpret the obtained results. In particular, during the course the participants will:

• learn how a statistical survey is carried out;
• learn the basic concepts used in statistics;
• learn what types of data are subject to statistical analysis;
• get to know the basic measures of descriptive statistics together with the ability to interpret them;
• learn the basic techniques used in statistical inference, together with the ability to interpret the obtained results;
• learn to choose the appropriate statistical techniques for the analysed data.

ISSUES

MODULE 1

Module one presents issues of statistical description, such as:

1. Basic concepts
2. Graphical presentation of collected data
3. Arithmetic mean
4. Dominant
5. Median
6. First and third quartiles
7. Variability measures
8. Asymmetry measures

MODULE 2

The second module covers issues concerning statistical inference, such as:

1. Mean estimation
2. Variance and structural indicator estimation
3. Parametric significance tests of a single parameter
4. Parametric significance tests of two parameters
5. Correlation analysis
6. Non-parametric significance tests

MODULE 3

Module 3 contains a summary and an examination that concludes the course.

CREDITING

Each subject is followed by exercises and test questions to test the knowledge acquired so far. They account for 60% of the grade. The entire course concludes with an examination, with a 40% share in the final grade. The course is considered passed if the participant achieves at least 51%. The participant then receives a certificate of course completion.

CONDITIONS FOR OBTAINING THE CERTIFICATE

To receive the certificate the participant needs to reach the threshold of 51%.

COURSE STAFF

Anna Małgorzata Olszewska, PhD

PhD in Economics in the major: Management Science, Assistant Professor at the Faculty of Management Engineering at Bialystok University of Technology, a graduate of mathematics and computer sciences, an academic with a long-standing experience in lecturing statistics and the use of statistical tools in the widely understood field of management, an author of books on statistics and mathematics.