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SAS Predictive Modeling

  • Course Overview
  • Target Audience
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Descriptive Statistics


Introduction to Statistics
  • Measure of central Tendency

  • Measure of Dispersion

  • Measure of Shape

Data Preparation
  • Data Handling and preparation

  • Missing value analysis and imputation

  • Outlier identification and how handle the outlier problem

  Sampling Methods
  • Simple Random Sampling

  • Systematic Random sampling

  • Stratified Random sampling

  • Cluster Random Sampling

  • Non Random samplings: Quota, Judgment, Convince and Snow ball sampling


II Statistical Inference
Parametric tests
  • One sample t test

  • Independent of two sample tests ( t test & Z tests)

  • Paired t test

  • One Way ANOVA

  • Two ways ANOVA

Non parametric Tests
  • Chi square Test

III. Predictive Modeling technique
Simple Linear Regression (SLR)
  • OLS method

  • MLE

  • Assumption of OLS

  • Checking Assumption of SLR

  • Problem of Homoscatasity

  • Problem of Autocorrelation

  • Problem of Multicolinarity

  • Data Transformation

IV. Multiple Linear regressions
V.  Logistic Regression for Classification and Prediction
VI. Forecasting Technique (Time series Analysis)
  • Trend analysis

  • Smoothening technique (Moving Average and Exponential smoothing

  • Auto regression

  • ARIMA Modeling

  • Exponential Smoothing

  VII. Multivariate Techniques
  • Factor Analysis for Data Reduction

  • Cluster Analysis for Market segmentation

  • CHIAD (Decision tree)

  • CART

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