SAS Programming: Applied Statistical Methods
- Offered byEduonix
SAS Programming: Applied Statistical Methods at Eduonix Overview
Duration | 2 hours |
Total fee | ₹199 |
Mode of learning | Online |
Schedule type | Self paced |
Credential | Certificate |
SAS Programming: Applied Statistical Methods at Eduonix Highlights
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SAS Programming: Applied Statistical Methods at Eduonix Course details
- You will learn Detecting multicollinearity using VIF
- You will learn Detecting multicollinearity using Condition Index
- You will Learn Hypothesis Testing
- You will learn Validate and Interpret Normality Test
- Learn how to use SAS programming from the beginners to validating machine learning algorithms assumptions
- It provides easy way to access multiple applications
- It has numerous procedures for descriptive, inferential, and forecasting types of statistical analyses
- By the end of this course you will be able to Use numbered range list to name SAS variables, understand SAS libraries and how to access data in SAS using a library
SAS Programming: Applied Statistical Methods at Eduonix Curriculum
Course Introduction
Introduction to the course
SAS OnDemand for Academics
SAS OnDemand for Academics
Understanding SAS syntax
DATA step
Procedure (PROC) step
Defining SAS Variables
Ways to define SAS variables
Assignment statement
INPUT statement
Variable Numbered Range List
Variable Attributes: PROC CONTENTS
SAS Operators
Arithmetic Operators
Example: Arithmetic Operators
Comparison Operators
Logical Operators
Accessing Data in SAS
Access using SAS Library
Using SAS Library demonstration
Using Library refernce
IMPORT procedure (PROC IMPORT)
IMPORT AN EXCEL FILE
IMPORTING A CSV FILE
RAW INSTREAM DATA
Control Output of Variables
Control Output of Variables
RENAME & KEEP statements
DROP statement
WHERE statement
Data Manipulation
IF-THEN/ELSE
Example: IF-THEN/ELSE
IF-THEN/DO
Example: IF-THEN/DO
Exercise: IF-THEN/DO
DO loops
Example: Iterative Do loops
DO WHILE & DO UNTIL
Example: DO WHILE
Example: DO UNTIL
Exercise: Do loops
Exercise continue: Do loops
Data Processing - Validating Model Assumptions
MISSING VALUES & DUPLICATES
DEALING WITH MISSING VALUES: MISSING() FUNCTION
DUPLICATES -- noduprecs
Multicollinearity or Collinearity Diagnostics
Detecting Multicollinearity
Linearity Assumption
Validate Linearity: Pearson Correlation Test
Validate Linearity: Residual Diagnostics
Normality Test
Shapiro Wiks Test for Normality
Outliers & Influential Observations
Outliers Detection
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