SAS Predictive Modelling


Why this course ?

  • SAS is a statistical software suite developed by SAS Institute for advanced analytics, multivariate analysis, business intelligence, criminal investigation,data management, and predictive analytics.
  • SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and more advanced options through the SAS language.
  • Ready-to-use procedures handle a wide range of statistical techniques including simple descriptive statistics, data visualization, analysis of variance, regression, categorical data analysis, multivariate analysis, cluster analysis, and non parametric analysis are part of this program

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Program Duration
and Fees

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48 Hours




• 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
• Three Approaches towards Probability
• Concept of a Random Variable
• Probability Mass Function
• Probability Density Function
• Expectation of A Random Variable
• Probability Distributions
• Concept of population and sample
• Techniques of Sampling
• Sampling Distributions
• Concept of estimation
• Different types of Estimation
• 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.
• One Way Anova
• Two Way Anova
• 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
• 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
• Concept of Time Series and its Applications
• Assumptions of Time Series Analysis
• Components of Time Series
• Smoothening techniques
• Stationarity
• Random Walk
• ARIMA Forecasting
• 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
• 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
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Course Name: SAS Predictive Modelling