Learn SAS Base And Advanced From Industry Experts On Real Time Data

School Of Analytics
In New Delhi

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Important information

  • Training
  • New delhi
  • Duration:
    2 Months
Description


Suitable for: Any graduate or working professionals

Important information
Venues

Where and when

Starts Location
On request
New Delhi
65,1st floor mall road,kalkaji,new delhi-19, 110019, Delhi, India
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Course programme

DATA ANALYSIS BY SAS SOFTWARE

Course contents

1. Introduction to the SAS Language

1.1 Introduction

1.2 Basic Language: Rules and Syntax

1.3 Creating SAS Data Sets

1.4 The INPUT Statement

1.5 SAS Data Step Programming Statements Their Uses

1.6 Data Step Processing

1.7 More on INPUT Statement

1.7.1 Use of Pointer controls

1.7.2 The trailing@ line-hold specifier

1.7.3 The trailing@ @ line-hold specifier

1.7.4 Uses of RETAIN statement

1.7.5 The use of line pointer controls

1.8 Using SAS Procedures

2. More on SAS Programming and some Applications

2.1 More on the DATA and PROC STEPS

2.1.1 Reading data from files

2.1.2 Combing SAS data sets

2.1.3 Saving and retrieving permanent SAS data Sets

2.1.4 User- defined in formats and formats

2.1.5 Creating SAS data sets in procedure steps

2.2 SAS Procedures for Computing Statistics

2.2.1 The UNIVARIATE procedures

2.2.2 The FREQ procedure

2.3 Some Useful Base SAS Procedures

2.3.1 The PLOT procedures

2.3.2 The CHART procedures

2.3.3 The TABULATE procedure

3. Statistical Graphics Using SAS / GRAPH

3.1 INTRODUCTINS

3.2 An INTRODUCTINS to SAS / GRAPH

3.2.1 Useful SAS / GRAPH procedures

GPLOT procedure

GCHART procedure

3.2.2 Writing SAS/Graph programs

3.3 Quantile Plots

3.4 Empirical Quantile- Quantile Plots

3.5 Theoretical Quantile- Quantile Plots or probability Plots

3.6 Profile Plots of Means or Interaction Plots

3.7 Two Dimensional Scatter Plots and Scatter Plot Matrices

3.7.1 Two –Dimensional Scatter Plots

3.7.2 Scatter plots Matrices

3.8 Histograms Bar Charts and Pie Charts

3.9 Other SAS Procedures for High- resolutions Graphics

4. Statistical Analysis of Regression Models

4.1.1 Simple linear regression using PROC REG

4.1.2 Lack of fit test using PROC ANOVA

4.1.3 Diagnostics use of case statistics

4.1.4 Predictions of new y values using regressions

4.2 An Introduction to Multiple Regression Analysis

4.2.1 Multiple regression analysis using PROC REG

4.2.2 Case Statistics and residual analysis

4.2.3 Residual Plots

4.2.4 Examining relationships among regression variables

4.3 Types of Sums of Squares Computed in PROC REG and PROC GLM

4.3.1 Model comparison technique and extra sum of squares

4.3.2 Types of sums of squares in SAS

4.4 Subset selection using PROC REG for Model selection

4.4.1 Subset selection using PROC REG

4.4.2 Other options available in PROC REG for model selection

4.5 Inclusion of squared Terms Product terms in Regression Models

4.5.1 Including interaction terms in the model

4.5.2 Comparing slopes of regression lines using interaction

4.5.3 Analysis of models with higher-order terms with PROC REG

5. Analysis of Variance Model

5.1 Introduction

5.1.1 Treatment Structure

5.1.2 Experimental Designs

5.1.3 Linear Models

5.2 One-way Classification

5.2.1 Using PROC ANOVA to analyze one-way Classifications

5.2.2 Making preplanned (or a priori) comparisons using PROC GLM

5.2.3 Testing orthogonal polynomials using contrasts

5.3 One-Way Analysis of Covariance

5.3.1 Using PROC GLM to perform one-way covariance analysis

5.3.2 One-way covariance analysis: Testing for equal slopes

5.4 A two Factorial in a Completely Randomized Design

5.4.1 Analysis of a two-way factorial using PROC GLM

5.4.2 Residual Analysis of Interaction

5.5 Two-Way Factorial: Analysis of Interaction

5.6 Two-Way Factorial: Unequal Sample sizes

5.7 Two way Classification: Randomized Complete Block Design

5.7.1 Using PROC GLM to analyze a RCBD

5.7.2 Using PROC GLM to test for nonadditivity

6. Analysis of Variance: Random and Mixed Effects Models

6.1 Introduction

6.2 One-way Random Effects Model

6.2.1 Using PROC GLM to analyze one-way Random Effects Models

6.2.2 Using PROC MIXED to analyze one-way Random Effects Models

6.3 Two –way Crossed Random Effects Model

6.3.1 Using PROC GLM and PROC MIXED to analyze two –way Crossed Random Effects Model

6.3.2 Randomized complete block design: Blocking when treatment factors are random

6.4 Two-Way Nested Random Effects Model

6.4.1 Using PROC GLM to analyze two-way nested random effects models

6.4.2 Using PROC MIXED to analyze two-way Nested Random Effects Models

6.5 Two-way Mixed Effects Models

6.5.1 Two-way Mixed Effects Models: Randomized Complete Blocks Design

6.5.2 Two-way Mixed Effects Models: Crossed Classification

6.5.3 Two-way Mixed Effects Models: Nested Classification

6.6 Models with Random and Nested Effects for More Complex Experiments

6.6.1 Models for nested factorials

6.6.2 Models for split-plot experiments

6.6.3 Analysis of split-plot experiments using PROC GLM

6.6.4 Analysis of split-plot experiments using PROC MIXED


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