# SAS Analytics

Currency:

## Why this course ?

• Learn how to generate descriptive statistics and explore data with graphs and perform linear regression and assess the assumptions
• Learn how to use diagnostic statistics to identify potential outliers in multiple regression and fit a multiple logistic regression model
• Modify data for better analysis results, build and understand predictive models such as regression models and compare and explain complex models generate and use score code

This course covers a range of introductory statistical topics and uses SAS software to carry out analysis. Emphasis placed on the interpretation of the results. It covers the skills required to assemble analysis flow diagrams using the rich tool set and predictive modeling. Ready-to-use procedures handle a wide range of statistical techniques.

Program Duration
and Fees

N/A

# Price

12800

#### Installation of SAS

Installation of SAS

#### Importing CSV file in SAS

Importing CSV file in SAS

#### Importing CSV file in SAS

Concept of Linear Regression
Assumptions of Classical Linear Regression Model
Concept of Multi Collinearinty and Auto Correlation

#### Linear Regression-Case Study & Practical session

Case study discussion and Dataset descriptions
Linear Regression Practical Session - Part - 1
Linear Regression Practical Session - Part - 2
Linear Regression Practical Session - Part - 3
Linear Regression Practical Session - Part - 4
Linear Regression Practical Session - Part - 5

#### Logistic Regression

Logistic Regression
Assumptions of Logistic Regression
ODDS AND ODDS Ratio
Concept of Concordant, Discordant and Tied Pairs
Setting the probability level
Detail description of Confusion Matrix

#### Logistic Regression- Case Study & Practical

Logistic Regression Practical part- 1
Logistic Regression Practical part- 2
Logistic Regression Practical part- 3
Logistic Regression Practical part- 4
Logistic Regression Practical part- 5

#### Time Series Forecasting

Concept of time series
Components of Time Series
Smoothing Techniques
Stationarity and non-stationarity of Time Series Data
ARIMA Modelling
BOX JENKINS Technology

#### Time Series - Case Study & Practical Session

Case Study and Data set discussion of Time Series
Time Series Practical Session Part- 1
Time Series Practical Session Part- 2
Time Series Practical Session Part- 3

Learners

Learners

Learners

Learners

Learners

Learners

# Testimonials

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Course Name: SAS Analytics

12800