 CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2020/2021

# Statistics 2

The course is not on the list Without time-table
Code Completion Credits Range Language
U63C4101 Z,ZK 6 2P+2C Czech
Lecturer:
Tutor:
Supervisor:
Institute of Economic Studies
Synopsis:

The course develops on the Statistics I themes; students will receive an extension basic principles of statistical knowledge, which they have acquired during the study Statistics I. Deepening relates to the field of random variables - random vector field and non-parametric testing. The other topics below mentioned is extending the knowledge acquired in previous Statistics I course. After completing the course, students will be ready to use the complicated methodological apparatus suitable for extraction of knowledge from both quantitative and qualitative data files.

Requirements:

Entrance requirements are composition of the exam from Statistics 1, Mathematics 1, Mathematics 2.

Syllabus of lectures:

The course develops on the Statistics I themes; students will receive an extension basic principles of statistical knowledge, which they have acquired during the study Statistics I. Deepening relates to the field of random variables - random vector field and non-parametric testing. The other topics below mentioned is extending the knowledge acquired in previous Statistics I course. After completing the course, students will be ready to use the complicated methodological apparatus suitable for extraction of knowledge from both quantitative and qualitative data files.

. Random vector, distribution and frequency functions of a random vector.

. Independence of random vector components, marginal probability distribution.

. Numerical characteristics of a random vector, conditional distribution of probability.

. Statistical induction and random selection.

. Maximum reliability method, point and interval estimates of parameters, determination of the range of random selection.

. Normality tests (Anderson-Darling test, Test of a good agreement, Shapiro-Wilks test, Kolmogorov-Smirnov test).

. Graphic analysis, Boxplot, histogram.

. Non-parametric testing of statistical hypotheses, single-choice and double-choice Wilcoxon test, two-sample Kolmogorov-Smirnov test.

. General analysis of categorical data, evaluation of frequencies, comparison of the relative frequency with the theoretical value.

. Analysis of dependencies of nominal and ordinal data type, pivot table, hypothesis testing of independence.

. Measurement of dependence force of nominal type quantities. Quadruple (association) table and independence testing in the four-pane table.

. Statistical meta-analysis, homogeneity test, aggregation of effect size, encoding of information.

. Methods of multidimensional analysis, external analysis, internal analysis (analysis of main components, factor analysis, cluster analysis).

. Logistic regression, dependency modeling using regression trees.

. Selection of statistical methods, classification of statistical methods, problems of significance tests, Bayesian approach, computationally intensive methods.

Syllabus of tutorials:

. The course develops on the Statistics I themes; students will receive an extension basic principles of statistical knowledge, which they have acquired during the study Statistics I. Deepening relates to the field of random variables - random vector field and non-parametric testing. The other topics below mentioned is extending the knowledge acquired in previous Statistics I course. After completing the course, students will be ready to use the complicated methodological apparatus suitable for extraction of knowledge from both quantitative and qualitative data files.

. Random vector, distribution and frequency functions of a random vector.

. Independence of random vector components, marginal probability distribution.

. Numerical characteristics of a random vector, conditional distribution of probability.

. Statistical induction and random selection.

. Maximum reliability method, point and interval estimates of parameters, determination of the range of random selection.

. Normality tests (Anderson-Darling test, Test of a good agreement, Shapiro-Wilks test, Kolmogorov-Smirnov test).

. Graphic analysis, Boxplot, histogram.

. Non-parametric testing of statistical hypotheses, single-choice and double-choice Wilcoxon test, two-sample Kolmogorov-Smirnov test.

. General analysis of categorical data, evaluation of frequencies, comparison of the relative frequency with the theoretical value.

. Analysis of dependencies of nominal and ordinal data type, pivot table, hypothesis testing of independence.

. Measurement of dependence force of nominal type quantities. Quadruple (association) table and independence testing in the four-pane table.

. Statistical meta-analysis, homogeneity test, aggregation of effect size, encoding of information.

. Methods of multidimensional analysis, external analysis, internal analysis (analysis of main components, factor analysis, cluster analysis).

. Logistic regression, dependency modeling using regression trees.

. Selection of statistical methods, classification of statistical methods, problems of significance tests, Bayesian approach, computationally intensive methods.

Study Objective:

The primary course objective is to create a follow-up methodological framework for the use of advanced stochastic methods (based on probability distribution of random variables) to enable students to sophistically analyze and regulate residuals of economic phenomena and business management processes.

Study materials:

OBLIGATORY

Freedman D. (2007). Statistics, Third Edition. Norton &amp; Company, Incorporated, W. W.ISBN-13: 978-0393970838

RECOMMENDED

Kutner, M., Nachtsheim, C., Neter, J., and Li, W. (2004). Applied Linear Statistical Models, McGraw-Hill/Irwin, Homewood, IL.

LIND, D., MARCHAL, W., WATHEN, S. (2015) Statistical Techniques in Business and Economics, (16th Edition). McGraw-Hill Education. ISBN-13: 978-0078020520.

TRIOLA, M., F. Essentials of Statistics (5th Edition). Pearson Education 2015. ISBN-13: 978-0321924599.

Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.) (p.178). Cheshire, CT: Graphics Press

Note:
Further information:
https://idp2.civ.cvut.cz/idp/profile/SAML2/Redirect/SSO?execution=e3s1
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2020-10-01
For updated information see http://bilakniha.cvut.cz/en/predmet5127706.html