Course date: 08.02.2023

Venue of the course: Trenčín (Slovak Republic)

Form of the course: face-to-face, online

Course price: 590  €

Difficulty level: medium

 

OBJECTIVE OF THE COURSE​

 

The aim of the course is to acquire knowledge and skills for a deeper understanding of causal relationships between variables, e.g. sales and costs in the company. Regression analysis is a method of statistical analysis of relationships. It is used for description, explanation and prediction. The course includes univariate and multivariate linear regression analysis models for numerical variables. Emphasis is placed on identifying significant factors that influence the dependent variable. Identifying the strength and order of significant factors is no less important. 

 

ASSUMPTIONS OF THE COURSE

The course assumes the ability to master computer knowledge of the basic concepts of descriptive statistics. Knowledge of the principles of testing and economic interpretation of hypotheses. The course also assumes knowledge of terms such as variable, dependency between variables, dependency analysis, correlation matrix. Experience with a statistical program is not necessary. Knowledge of MS Excel is required to complete the course. 

 

COURSE BENEFITS

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The basic benefits of the course include: 

• the content of the course is based on empirical analyzes of the real requirements of companies for their processing (time distribution: 20% theory; 80% examples);

• study materials in electronic and printed form;

• examples to verify acquired knowledge; 

• consultations with the lecturer on problems from business practice related to the course topic during and also after the course;

• completion of the course in various forms: individually or in smaller groups; directly in the company upon agreement; 

• obtaining a certificate of completion of the course.

 

COURSE CONTENT

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• Statistics as a tool for achieving the company's stated goal. Formulation of the goal and assumptions (hypothesis); successive steps leading to their evaluation.  Descriptive analysis (tables and graphs) - its meaning, use and interpretation.

• Causal relationships between variables. Defining variables (dependent, independent variable). Types of regression function. 

• Simple and multiple linear regression. Verification of assumptions for applying regression analysis (assumption of normal distribution, independence). Methods of calculating regression coefficients. Estimation and verification of the significance of variables. 

• Verification of the significance of the entire regression model. Verification of undesirable effects on the quality of the regression model (Multicollinearity, Autocorrelation). Verification of assumptions about the random component (independence, homogeneity, ...). Residue analysis. 

• Procedure for statistical and economic evaluation of the best regression model (Stepwise regression analysis). Interpretation and presentation of the achieved results and their economic interpretation for the middle and upper management of the company.

• Prediction of development of the dependent variable for the nearest monitored period in the future. Options for improving the regression model.