SAS Base & Predictive modelling


Why this course ?

  • SAS is a flexible, extensible 4GL and web-based interface for data access, transformation and reporting.
  • This course is for users who want to learn how to write SAS programs. It is the entry point to learning SAS programming and is a prerequisite for many other SAS courses.
  • This course would make use of SAS to teach you all the basics of statistics, hypothesis testing, t-test, ANOVA, Linear and Logistic Regression
  • SAS Analytics skills are the most valuable skills to have in today's job market.

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  • Led by an expert instructor
    Instructors are best at the field of Statistics and also having industry exposure.
    Advantages of attending this course
    No programming knowledge is required for this course.
    Delivered online and classroom
    All coursework is available 24/7 online or offline.


• Concatenating SAS Data Sets
• Merging SAS Data Sets
• Combining SAS Data Sets : Additional Features
• 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
• Reading SAS Data Sets and Creating Variables
• Conditional Processing
• Dropping and Keeping Variables
• Reading Excel Spreadsheets Containing Date Fields
• Concept of population and sample
• Techniques of Sampling
• Sampling Distributions
• Producing Bar and pie Chart
• Enhancing output
• Producing Plots
• Concept of Regression and features of Linear line.
• Assumptions of Classical Linear Model
• Method of Least Squares
• Understanding the Goodness of Fit
• Multiple linear Regression with their Assumptions
• Concept of Multocollinearity
• Signs of Multicollinearity
• The Idea Of Autocorrelation
• An Overview of the SAS System
• Getting Started With the PRINT Procedure
• Sequencing and Grouping Observations
• Identifying Observations
• Special WHERE Statement Operators
• 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
• Introduction of Summary Reports.
• Basic Summary Reports
• The Report Procedure
• The Tabulate Procedure
• Three Approaches towards Probability
• Concept of a Random Variable
• Probability Mass Function
• Probability Density Function
• Expectation of A Random Variable
• Probability Distributions
ypes 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
• Overview
• Review of SAS basics
• Review of DATA Step Processing
• Review of Displaying SAS Data Sets
• Working with Existing SAS Data Sets
• Do Loop Processing
• SAS Array Processing
• Using SAS Arrays
• 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
• Introduction
• Manipulating Character values
• Manipulating Numeric values
• Manipulating Numeric values based on Dates
• Converting variable Type
• Customizing Report Appearance
• Formatting Data Values
• Creating HTML Reports
• Reading Raw Data Files: Column Input
• Reading Raw Data Files: Formatted Input
• Examining Data Errors
• Assigning Variable Attributes
• Changing Variable Attributes
• Reading Excel Spreadsheets
• Match-merging Two or more SAS Data Sets
• Simple Joins Using the SQL Procedure
• Creating an Accumulating Total variable
• Accumulating Totals for a Group of Data
• One Way Anova
• Two Way Anova
• Outputting Multiple Observations
• Writing to Multiple SAS Data Sets
• Selecting Variables and Observations
• Writing to an External File
• Introduction to SAS Programs
• Running SAS Programs
• Mastering Fundamental Concepts
• Diagnosing and Correcting Syntax Errors
• Exploring Your SAS Environment
• Reading Delimited Raw Data Files
• Controlling When a Record Loads
• Reading Hierarchical Raw data Files
• 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.
• Using the PUT Statement
• Using the DEBUG Option
• Concept of Time Series and its Applications
• Assumptions of Time Series Analysis
• Components of Time Series
• Smoothening techniques
• Stationarity
• Random Walk
• ARIMA Forecasting

Base SAS





Course Name: SAS Base & Predictive modelling