SAS Programming 1: Essentials

Learn how to

navigate the SAS windowing environment

navigate the SAS Enterprise Guide programming

environment

read various types of data into SAS data sets

create SAS variables and subset data

combine SAS data sets

create and enhance listing and summary reports

validate SAS data sets.

Course Contents

Introduction

an overview of SAS foundation

course logistics

course data files

SAS Programs

o introduction to SAS programs

o submitting a SAS program

o working with SAS program syntax

Accessing Data

Producing Detail Reports

o submitting report data

o sorting and grouping report data

o enhancing reports

Formatting Data Values

o using SAS formats

o creating user-defined formats

Reading SAS Data Sets

o reading a SAS data set

o customizing a SAS data set

Reading Spreadsheet and Database Data

o reading spreadsheet data

o reading database data

Reading Raw Data Files

o introduction to reading raw data files

o reading standard delimited data

o reading nonstandard delimited data

o handling missing data

Manipulating Data

o using SAS functions

o conditional processing

Combining SAS Data Sets

o concatenating data sets

o merging data sets one-to-one

o merging data sets one-to-many

o merging data sets with nonmatches

Creating Summary Reports

o using the FREQ procedure

o using the MEANS and UNIVARIATE

procedures

o using the Output Delivery System

Learn how to

o control SAS data set input and output

o combine SAS data sets

o summarize, read, and write different types

of data perform DO loop and SAS array

processing

o transform character, numeric, and date

variables

Course Contents

Introduction

o an overview of SAS foundation

o course logistics

o course data files

Controlling Input and Output

o writing observations explicitly

o writing to multiple SAS data sets

o selecting variables and observations

Summarizing Data

o creating an accumulating total variable

o accumulating totals for a group of data

Reading Raw Data Files

o reading raw data files with formatted input

o controlling when a record loads

Data Transformations

o manipulating character values

o manipulating numeric values

o converting variable type

Debugging Techniques

o using the PUTLOG statement

Processing Data Iteratively

o DO loop processing

o conditional DO loop processing

o SAS array processing

o using SAS arrays

Data Transformations

o manipulating character values

o manipulating numeric values

o converting variable type

Debugging Techniques

o using the PUTLOG statement

Processing Data Iteratively

o DO loop processing

o conditional DO loop processing

o SAS array processing

o using SAS arrays

Restructuring a Data Set

o rotating with the DATA step

Combining SAS Data Sets

o using data manipulation techniques with matchmerging

Creating and Maintaining Permanent Formats

o creating permanent formats

1. Introduction To Analytics and Basic Statistics

Types of Analytics

Properties of Measurements

Scales of Measurement

Types of Data

Measures of Central Tendency

Measures of Dispersion

Measures of Location

Presentation of Data

Skewness and Kurtosis

2. Introduction to Probability Theory

Three Approaches towards Probability

Concept of a Random Variable

Probability Mass Function

Probability Density Function

Expectation of A Random Variable

Probability Distributions

3. Sampling Theory And Estimation

Concept of population and sample

Techniques of Sampling

Sampling Distributions

4. Theory of Estimation

Concept of estimation

Different types of Estimation

5. Testing of hypothesis

Concept of hypothesis

Null hypothesis

Alternative hypothesis

Type-I error

Type-II error

Level of Significance

Confidence Interval

Parametric Tests and Non Parametric Tests

One Sample T test

Two independent sample T test

Paired Sample T test

Chi square Test for Independence of Attributes.

6. Analysis of variance

One Way Anova

Two Way Anova

7. Exploratory Factor Analysis

Principal Component Analysis

Estimating the Initial Communalities

Eigen Values and Eigen Vectors

Correlation Matrix check and KMO-MSA check

Factor loading Matrix

Diagrammatic Representation of Factors

Problems of Factor Loadings and Solutions

8. Cluster Analysis

Types of Clusters

Metric and linkage

Ward’s Minimum Variance Criteria

Semi-Partial R-Square and R-Square

Diagrammatic Representation of clusters

Problems of Cluster Analysis

9. Linear Regression and Multiple Linear Regression

Concept of Regression and features of Linear line.

Assumptions of Classical Linear Model

Method of Least Squares

Understanding the Goodness of Fit

Test of Significance of The Estimated Parameters

Multiple linear Regression with their Assumptions

Concept of Multocollinearity

Signs of Multicollinearity

The Idea Of Autocorrelation

10. Logistic Regression

Concept and Applications of Logistic Regression

Principles Behind Logistic Regression

Comparison between Linear probability Model and Logistic Regression

Mathematical Concepts related to Logistic Regression

Concordant Pairs, Discordant Pairs and Tied Pairs

Classification Table

Graphical Representation Related to logistic Regression.

11. Time Series Analysis

Concept of Time Series and its Applications

Assumptions of Time Series Analysis

Components of Time Series

Smoothening techniques

Stationarity

Random Walk

ARIMA Forecasting

Box Jenkins Technology

Merits and Demerits of BJ Technology