SAS Predictive Modeling

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SAS Predictive Modeling Course Overview

Learn range of statistical topics and uses SAS software to carry out predictive analysis. Emphasis will be placed on the explanation of the results. 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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Course Fees:Rs.12000

  • Duration : 48 hours/2 months
  • No Cost EMI : INR 6000 x 2

NEFT Payment :Bank account details: Account Name – KLMS Hands-On Systems Private Limited, Account No – 50200042627525, IFSC – HDFC000027

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Course Description!

SAS Predictive Modeling Course Curriculum

This course covers Introduction to Analytics and Basic Statistics, Introduction to Probability Theory, Sampling Theory and Estimation, Theory of Estimation, : Testing of hypothesis, Analysis of variance, Exploratory Factor Analysis, Cluster Analysis, Linear Regression and Multiple Linear Regression, : Logistic Regression, Time Series Analysis

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Course content

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
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