Statistical Data Analysis with SAS Pack 2400-ZEWW137
1. Organizational activities, discussion of the grading rules.
2. Creating data sets, step DATA, step PROC.
3. Loading data from a file and saving data, variable formats, information.
4.Creating data sets - preparation of data for analysis: adding, removing and processing variables, selecting and deleting observations, sorting data (SORT procedure), dividing data sets into parts, vertical and horizontal combining of data sets, transposition of a data set (TRANSPOSE procedure).
5. A summary description of the contents of the data set (PRINT, SUMMARY, MEANS and FREQ procedures).
6. Graphical presentation of data, including geographical data on the map (GPLOT, GCHART, GMAP, GREMOVE procedures).
7. Analysis of the distribution of variables (UNIVARIATE procedure).
8. Analysis of interdependencies of variables (CORR, RANK).
9. Verification of hypotheses regarding means (TTEST, BOXPLOT procedures).
10. Non-parametric statistical tests (NPAR1WAY procedure).
11. Analysis of variance and covariance (ANOVA procedure).
12. Cluster analysis (CLUSTER, TREE procedures).
13. Principal components analysis and factor analysis (PRINCOMP, FACTOR procedures).
14. Simulation methods
15. Non-parametric estimation of variable distribution
16. Analysis of discrimination (DISCRIM procedures, STEPDISC).
17. Correspondence analysis (CORRESP procedure).
Type of course
Prerequisites (description)
Course coordinators
Learning outcomes
After course completion students will have reliable and practical knowledge on descriptive statistics, statistical reasoning, statistical relationships testing for quantitative and qualitative data, graphical data analysis and selected multivariate analysis methods (i.e. principal components analysis, factor analysis and cluster analysis). They will be able to perform own research, select appropriate statistical methodology for the research questions.SU05, SU06, SK01, SK03, SU04, SU03, SU02, SU01, SW03, SW02, SW01, SW04, SW05, SK02, SK04
Assessment criteria
Thes assesment will be based on three pillars: class presence (10%), in-class activity, homeworks (30%) and solving a take-home exam exercises - 60%). The take home exam will require preparing 4GL code for the exercises and the description and interpretation of the results.
Bibliography
SAS, OnlineDoc
Der, G. and Everitt, B. S. (2002), A Handbook of Statistical Analyses using SAS, Chapman & Hall/CRC, 2nd edition.
Jóźwiak, J. and Podgórski. J. (1995), Statystyka od podstaw, PWE.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: