Advanced Analytical Methods II

  • Credits: 12
  • Ending: Examination
  • Range: 16sP
  • Semester: summer
  • Year: 1
  • Faculty of Economic Informatics

Teachers

Included in study programs

Data Science in Economics

Data Science in Economics

Teaching results

After successful completion of this course, students will understand a relatively large set of statistical methods falling under general and generalized linear models in a wide context and will be able to apply them effectively in their scientific work.

In particular, students will acquire the following abilities:
− Students will get acquainted with the unifying conceptual framework of the most frequently used statistical methods in the field of Data Science, such as t-test of two population means, ANOVA, ANCOVA and regression analysis,
− Students will acquire knowledge for a comprehensive analysis of the impact of quantitative and qualitative factors on the target variable modelled through general and generalized linear models.
Students will acquire in particular the following skills:
- Students will be able to use the appropriate type of sum of squares to adequately evaluate the significance of the influence of factors on the target variable.
- They will be able to choose the appropriate type of coding of categorical factors to solve the relevant scientific question and correctly interpret the estimated parameters of general and generalized linear models for the chosen type of coding.
- Students will learn the general procedures of testing and estimating of estimable linear combinations and gain the ability to apply them through the statements LSMEANS, CONTRAST, ESTIMATE and LSMESTIMATE in the SAS programming language.
- They will acquire the skill to use the PROC GLM, PROC MIXED, PROC LOGISTIC, PROC GENMOD a PROC GLIMMIX procedures in SAS software and to make interventions in the SAS programming code in order to be able to use these procedures effectively in their scientific activities in solving specific tasks, or for deeper analysis.
Students will acquire the following competencies:
− Students will be able to analyse complex relationships between economic phenomena through general and generalized linear models (including mixed models).
− Students will learn to adequately apply the analysis of marginal means and contrast analysis, thanks to which they can make full use of the potential of statistical modelling in their empirical research.

Indicative content

1. lecture: ANOVA, ANCOVA and linear regression in the form of general linear models. Method of generalized inverse. Parameter estimation in general linear models. Analysis of marginal means (LS means). Multiple comparison methods (Post hoc tests).
2. lecture: Estimable functions. General linear hypothesis. Testing linear hypothesis. Analysis of contrasts. Using of estimable functions in an analysis of contrast and in prediction.
3. lecture: Logistic regression and generalized linear models. Analysis of LS means and contrasts in logistic regression and generalized linear models.
4. lecture: General linear mixed models and generalized linear mixed models. Analysis of LS means and contrasts in general and generalized linear mixed models.

Support literature

1. Searle, S. R., Gruber, M. H. J. (2017). Linear Models. 2nd ed. John Wiley & Sons.
2. Littell, R. C., Stroup, W. W., Freund, R. J. (2010). SAS for Linear Models. 4th ed. Cary, NC: SAS Institute Inc.
3. Kim, K., Timm, N. (2006). Univariate and Multivariate General Linear Models: Theory and Applications with SAS. Chapman and Hall/CRC.
4. Rutherford, A. (2001). Introducing ANOVA and ANCOVA: a GLM Approach. Sage.
5. Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. New York: John Wiley & Sons.
6. Chen, H. (2008). Using ESTIMATE and CONTRAST Statements for Customized Hypothesis Tests. SAS Institute Inc. Paper SP09-2008.
7. Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models. New York: Sage Publications.
8. Haans, A. (2018). Contrast analysis: A tutorial. Practical Assessment, Research, and Evaluation, 23(1), 9.
9. Lenth, R., V. (2016). Least-squares means: the R package lsmeans. Journal of Statistical Software. 69(1), 1-33.
10. SAS Institute Inc. (2017). The GLM Procedure. In SAS/STAT® 14.3 User’s Guide. Cary, NC: SAS Institute Inc.
11. Stroup, W. W., Milliken, G. A., Claassen, E. A., Wolfinger, R. D. (2018). SAS for Mixed Models: Introduction and Basic Applications. Cary, NC: SAS Institute.
12. Šoltés, E., Zelinová, S., Bilíková, M. (2019). General Linear Model: An Effective Tool for Analysis of Claim Severity in Motor Third Party Liability Insurance. Statistics in Transition: new Series. 20(4), 13-31.
13. Šoltés, E., Vojtková, M., & Šoltésová, T. (2018). Work Intensity of Households: Multinomial Logit Analysis and Correspondence Analysis for Slovak Republic. Statistika: Statistics and economy journal, 98(1), 19-36.
14. Hummel, R. M., Claassen, E. A., & Wolfinger, R. D. (2021). JMP for Mixed Models. Cary, NC: SAS Institute.
15. Kuznetsova. A.. Brockhoff. P. B.. & Christensen. R. H. B. (2017). lmerTest package: tests in linear mixed effects models. Journal of Statistical Software. 82(13).
16. Schad, D. J., Vasishth, S., Hohenstein, S., & Kliegl, R. (2020). How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Journal of Memory and Language, 110, 104038.
Literature will be continuously updated with the latest scientific and professional titles.

Requirements to complete the course

15 % - active participation on lectures
25 % - semester project processed in statistical software and/or free software environment (e.g. SAS, SPSS, R, Python)
25 % - presentation of the semester project
35 % - final exam

Student workload

Total study load (in hours): 312 hours
Distribution of study load
Lectures participation: 16 hours
Preparation for the lectures: 80 hours
Elaboration of the semester project: 128 hours
Preparation for the final exam: 88 hours

Language whose command is required to complete the course

Slovak

Date of approval: 11.03.2024

Date of the latest change: 03.02.2022