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SAS Business Analytics (Statistics & Predictive Modeling) Course Online at Professional Sas Tutor

#703, 30th Main, 1st Phase, 2nd Stage,
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About The Course:-

Best SAS Online Training Institute. We provide online SAS training with Real-time, Experienced Faculties for Individuals and Corporates.

Who Should Take this?

Systems administrators, linux administrators, windows administrators, Infrastructure engineers, Big Data Architects, DB Administrators, IT managers and Mainframe Professionals.

Pre-requisites for this training
This course requires  prior knowledge of Java

Course Objectives

    • Basic Statistical Concepts
      Descriptive and Inferential Statistics, Populations and Samples, Parameters and Statistics, Use of variables dependent and Independent, Types of Variables Quantitative and Categorical, Scales of measurement Nominal ordinal interval ratio, Statistical Methods, Exploring your data

Descriptive Statistics
Describing your data, Measures of Location, Percentiles, Measures of Variability, Using descriptive statistics to answer data questions, Means procedure, Using Proc means to generate statistics

Picturing your data
Histogram, Normal Distribution, Assessing Normality, Measures of Shape Skewness, Measures of Shape Kurtosis, Normal probability plots, ,Box Plots, Comparing Distributions Summary, Assessing Normality with examples, Univariate Procedure, Statistical Graphics procedure in SAS, The SG Plot procedure in SAS, ODS Graphics output, Using SAS to picture your data

Confidence Intervals
Point Estimators,Variability and Standard Error, Distribution of Sample Means, Interval Estimators, Confidence Intervals, Normality and Central Limit Theorem, Calculating confidence interval for the Mean, Using Proc Means to generate confidence interval, Calculating 95 percent confidence interval

Hypothesis Testing
Decision Making Process, Steps in hypothesis testing, Types of Errors and Power, The P value Effect Size Sample Size, Statistical Hypothesis Test, The T Statistic and the T Distribution, Comparing Sample and Hypothesized Means, Using Proc univariate to generate statistics, Using Proc Univariate to perform a hypothesis test

Analysis of Variance – Anova

Two sample T test
Assumptions for Two sample T Test, F test for Equality of Variance, Comparing Group Means, Identifying your data, The T test procedure, Running Proc T test in SAS, Examining the equal variance t- test and p- value, Examining the unequal variance t- test and p value, Interpreting two sample t test result, One Sided Test, Test for difference on one side, The T test procedure and Side option, Performing a one sided test.

One Way Anova-Introduction
Anova Overview, The ANOVA hypothesis, The ANOVA Model, Sum of Squares, Assumptions for Anova, Predicted and Residual Values, Comparing group means with one way Anova, Examining the descriptive statistics across means, The GLM procedure, using the GLM procedure.

Anova with data from a Randomized block design
Observational Studies Vs Controlled Experiments, Nuisance Factors, Including a blocking variable in the model, More Anova Assumptions, Creating a Randomized block design, Performing Anova with Blocking,

Anova -Post Hoc Test
Multiple Comparison Methods, Tuckey’s multiple comparison method, Dunnet’s multiple comparison method, determining which mean is different, Diffogram and Tuckey’s Method, Control Plots and Dunnet’s Method, Proc GLM with LS means, performing post hoc pairwise comparison

Two Way Anova with Interactions
N Way Anova, Interactions, Two way Anova Model, Using Two way Anova, Identifying your data, Applying the two way Anova Model, Examining your data with Proc Means, Examining your data with Proc SG plot, Performing two way Anova with Interactions, Performing Post Hoc Pairwise comparison


Exploratory data analysis
Using scatter plots to describe relationship between continuous variables, Using Correlation to measure relationship between two continuous variables, Hypothesis testing for a correlation, Avoiding common errors in interpreting correlations, Avoiding common errors Causal and Effect, Avoiding common Errors: Types of relationships, Avoiding common Errors: Outliers, Exploring data using correlation and scatter plots, Producing correlation statistics and scatter plots using Proc Corr, Using PROC CORR to produce correlation matrix and scatter plots, Examining correlations between predictor variables.

Simple Linear Regression
Objectives of simple linear regression, performing simple linear regression, The simple linear regression model, How SAS performs simple linear regression model, Measuring how well the model fits the data, Comparing regression model to a baseline model, Hypothesis testing for linear regression, Assumption of simple linear regression, The REG procedure: Performing Simple linear regression, Confidence and prediction intervals, Specifying Confidence and Prediction Intervals using SAS, Viewing and printing confidence intervals, The REG procedure producing predicted values, Producing predicted value of the response variable, Scoring predicted values using parameter estimates, Storing parameter estimates using Proc Reg and score using Proc score.

Multiple Linear Regression
Advantages and Disadvantages of Multiple Regression, Common Applications for Multiple linear regression, Picturing the model for Multiple Regression, Analysis versus prediction in multiple regression, Hypothesis testing for multiple regression, Assumptions for multiple regression, The Reg Procedure performing multiple linear regression

Model Building and interpretation
Approaches to selecting model, SAS and automated approaches to modeling, All possible regressions approach to model building, SAS and all possible regressions approach, Evaluating the model using Mallow’s Cp stat, Viewing Mallow’s Cp stat in Proc Reg, The REG procedure using all techniques, The REG procedure using automatic selection, The REG procedure Estimating and testing coefficients for selected models, The Stepwise selection approach to model building, Specifying Stepwise selection in SAS, The REG procedure performing stepwise regression, Using alternate significance criteria for stepwise models

Regression Diagnostics

Examining Residuals
Assumption for regression, the importance of plotting data and checking Assumptions, Verify Assumptions using residual plots, Detecting outlies using residual plots, The REG procedure producing default diagnostics, The REG procedure specific diagnostics

Identifying influential observations introduction
Using diagnostic statistic, Using Diagnostic Statistic STUDENT, Using Diagnostic Statistic COOK, Using Diagnostic Statistic RSTUDENT, Using Diagnostic Statistic DFFITS, Using Diagnostic Statistic DFBETAS, The REG procedure Requesting diagnostic plots, using diagnostic plots to identify influential observation, The REG procedure generating and saving diagnostic statistics, The REG procedure writing diagnostic statistics to an output dataset, Using cut off values for diagnostic criteria, Detecting influential observation programmatically, Handling influential

Detecting Collinearity
Understanding Collinearity, The REG procedure detecting Collinearity, Using Diagnostic statistic to detect Collinearity, The REG procedure Calculating diagnostics for Collinearity, The REG Procedure Dealing with Collinearity, Using an effective modeling cycle
Categorical data analysis

Describing categorical data
One Way Frequency tables, Association between categorical Variables, Cross Tabulation tables, Testing for Association and fitting a logistic model, The Tables statement in Proc Freq, Examining the distribution of categorical variables, Ordering the values of an Ordinal variable, Ordering a variable in cross tab table

Test of Association
The Pearson Chi Square Test, Cramer’s V Statistics, Odds Ratio, Performing Chi Square Test, The Mentel Haenszel Chi Square Test, The Spearman Correlation Statistic, Performing a Mentel Haenszel test for ordinal Association

Introduction to Logistic Regression
Logistic Regression, Modeling a binary response, The Logistic Procedure, Specifying a parameterized method in class statement, Effect Coding, Reference Cell coding, Fitting a binary logistic regression model, Interpreting odds ratio for a categorical predictor, Interpreting odds ratio for a continuous predictor, Comparing pairs to assess the fit of a model.

Multiple Logistic Regression
Multiple logistic regression, The backward elimination method for variable selection, Adjusted Odds ratio, Specifying the variable selection method in model statement, The Units Statement, Fitting a multiple logistic regression model, Comparing the binary and the logistic regression model, Specifying the formatted value as a reference, Interaction between variables, The backward elimination method with interactions, Specifying interaction in the model statement, Fitting a multiple logistic regression with interaction, The Odds ratio statement, Fitting a multiple logistic regression with all Odds ratio, Comparing multiple logistic regression models, Interaction plots

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