16 Important SAS/STAT Features You Must Know

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We learned SAS/STAT Tutorial, by what kind of people it is used and the purpose it serves. Today we will be talking a bit more in detail about the different  SAS/STAT features and the variety of tasks that can be performed on it to make our life a little easy.

So, let’s start with SAS/STAT Features.

SAS/STAT Features

16 Important SAS/STAT Features You Must Know

SAS/ STAT Features

These are the important features of SAS/STAT, let’s discuss them one by one:

i. Analysis of Variance

This allows a user to make linear models, compute variance on different measurements as well as multivariate analysis on different parameters.

Let’s discuss SAS/STAT ANOVA (Analysis of Variance)

SAS/STAT Software Features


ii. Bayesian Analysis

Built-in feature for Bayesian modeling and inference for generalized linear models, accelerated failure time model, Wide range of Bayesian models available.

SAS/STAT Software Features

SAS/STAT Features – Bayesian Analysis

iii. Descriptive Statistics

SAS/STAT offers box-and-whisker plots. One can Compute directly and indirectly standardized rates and risks for study populations.

Follow this link to know about SAS/STAT Descriptive Statistics

iv. Discriminant Analysis

Canonical discriminant analysis and Stepwise discriminant analysis can be performed.

 v. Distribution Analysis

Procedures allow Univariate and bivariate kernel density estimation.

vi. High Performance

The 14 SAS/STAT procedures are multithreaded which means a lot of features are packed for use which increases the efficiency of a program.

SAS/STAT Features - High-Performance

SAS/STAT Features – High-Performance

Follow this link to know about SAS/STAT Discriminant Analysis

vii. Market Research

Market research plays an important role in any company. The SAS STAT software provides choice models, as well as multidimensional scaling models to assess parameters.

viii. Missing Data Analysis

Multiple imputations, weighted generalized estimating equations, and imputation for survey data.

ix. Mixed Models

It packs Linear and nonlinear mixed models, generalized linear mixed models and Nested models.

x. Multivariate Analysis

Different analysis like exploratory and confirmatory factor analysis, principal components analysis, canonical correlation and partial canonical correlation.

SAS/STAT Features - Multivariate Analysis

SAS/STAT Features – Multivariate Analysis

xi. Post Processing

This comprises of Hypothesis tests like t-tests, chi-square tests, and Prediction plots.

xii. Predictive Modeling

Predict and reveal insights from data like classification and regression trees, partitioning of data into training, validation and testing roles.

SAS/STAT Features - Predictive Modeling

SAS/STAT Features – Predictive Modeling

Read – SAS/STAT Predictive Modeling

xiii. Regression

Regression is an important feature of statistical analysis. SAS STAT offers least squares regression, principal components regression, quadratic response surface models and accurate estimation for ill-conditioned data.

SAS/STAT Software Features

SAS/STAT Features – Regression

xiv. Standardization

SAS STAT offers around 18 standardization methods and capabilities.

Do you Know Best SAS books to Learn SAS Programming – 2018

xv. Statistical Graphics

There are hundreds of statistical graphs available with analyses, customization is provided and Base SAS “SG” procedures create user-specified statistical graphics.

SAS/STAT Software Features

SAS/STAT Features -Statistical Graphics


SAS/STAT Software Features

Features of SAS/STAT- Statistical Graphics

xvi. Survey Sampling and Analysis

This feature includes sample selection, descriptive statistics, linear and logistic regression, proportional hazards regression and missing value imputation.’

So, this was all about SAS/STAT Features Tutorial. Hope you like our explanation.


Hence, you all came to know a brief idea on different SAS/STAT Features and the amazing things you can do with it. We will be looking at the advantages and limitations in the next lecture. So stay tuned. Furthermore, if you have any query feel free to ask in a comment section.

Related Topic – Robust Regression in SAS/STAT  

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