SAS Analytics Training Institutes in Hyderabad

SAS Advanced Analytics

Predictive Modelling:

I. 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
Discrete Distributions
Binomial, Poisson, Negative Binomial, Geometric and Hyper-Geometric Distributions.
Continuous Distributions
Normal, Uniform, Gamma, Beta of I and II kinds, Exponential, Cauchy Distributions.
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
Principle Component Analysis
Cluster Analysis for Market segmentation
Discriminate Analysis for classification and Prediction
Conjoint analysis for Product design
Canonical correlation