Basic and Advanced Programming in SAS with Statistics 223111-D
Laboratorium (LAB) Semestr zimowy 2020/21

Data processing in SAS 4GL:

Data- and Proc-steps, SAS statements and functions

Sequence of SAS code running, variable attributes and PDV.

Important procedures for data processing:

Sorting and transposition of row data

Data aggregations.

Valuable processing techniques:

Processing by groups

Arrays of data

Tables joining including SAS SQL.

Tabular reports and basic of ODS:

Data listing, tabular reports

ODS: HTML, PDF, RTF and OUTPUT.

Processing of text data:

Text functions in SAS

Data cleaning elements

Regular expressions and Levenshtein distance

Outlier detection.

Data visualization:

Bar, line and pie graphs

ODS GRAPHICS and ODS MARKUP

Vector graphics and LaTeX interchange.

Macro-programming:

SAS code parameterization by macro-variables

Macro-loop and macro-if-then statements

Array of macro-variables.

Automatizing of data processing and reporting:

Interactive reports

SAS - Excel - interchange - DDE tool.

Advance programming elements:

Access to various data sources (ODBC, Oracle etc.)

SQL Pass Through Facility

Complicated text files and PIPE source

SAS/IML and connection to R software

Data processing optimization.

Structure analyse:

Descriptive statistics calculation

Box and probability plots

Goodness-of-Fit tests for distributions

Distribution analyses.

Statistical estimation:

Confidence intervals for a mean

Test for location for population mean

T tests for one sample and two samples

One-side t tests

Random generators and approximately distributions in practice.

Variable dependency:

Correlation analyses and scatter plots

Dependency for categorical variables

Visualisation of dependency

Regression analyse elements.

Automatizing of statistical analyses:

Box-cox transformation

Partial correlation

Outlier detection, robust means

Multidimensional reduction, Principle Component Analyse.

Advance usage of SAS/IML:

Linear programming

Non-linear optimization

Genetic algorithms.

User friendly graphical interface - simple code making in SAS:

SAS Enterprise Guide

Review of SAS tools useful for BIG DATA.

Examples of analyses in R.