SAS

Description

The data analysis training with SAS is used in the management of data, analysis, and graphics. It comprises of advanced functions which includes forecasting, survival analysis, data analysis, and time series analysis and survey methods. It can be utilized via graphical interface using very intuitive language.

Caring for your dentures

Course Modules

Module 1: Research Design, Analysis and interpretation

Introduction to Research and the Research Process
Problem Definition
Research Design and Secondary Data Sources
Qualitative Methods
Descriptive Research Design and Observation
Causal Research Design
Measurement, Scaling and Sampling
Data Preparation and Analysis Strategy
Hypothesis testing, Frequencies and Cross-tabulation
Testing for Significant Differences – t-test/ANOVA
Testing for Association – Correlation and Regression

Module 2: Understanding the Workflow

The Workflow
SAS Basics
Data Importing - Instream data and Proc Import
Import Wizard for SAS 9.x
Data Importing in SAS Studio
Bring in Data from Pre-existing SAS Dataset and Create Permanent Dataset
Data importing - excel data

Module 3: Data Manipulation - Naming Convention and IF THEN/ELSE Statement

Naming Convention and Variable Types
IF THEN/ELSE Statement
Keep and Drop Variables
Data Manipulation - SAS Functions and DO Loop
SAS Functions
DO Loop
Dataset Manipulation - Subset and Append

Module 4: Dataset Manipulation - Merge and Transposition

Merge
Merge two datasets into a single dataset
Project part 3: Merge two datasets
Transpose
A comprehensive task using several techniques to subset, transpose data

Module 5: Descriptive Statistics - Frequency and Univariate Analysis

Explore the Data Using PROC PRINT and CONTENTS Procedures
Descriptive Statistics
Calculate the mean of the sample
PROC FREQ

Module 5: Perform descriptive statistical analysis

One, Two Sample T-Test and ANOVA
One Sample T-Tes
Two Sample T-Test
Two Sample T-Test and paired T-Test
Sample ANOVA
Non-parametric Analysis

Module 7: Linear Regression - Predicting the Outcome

Linear Regression
Use Linear Regression model to predict the MSRP
Dummy Variable
Include some categorical variables into the model

Duration: 4 -6 weeks

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