ANOVA stands for Analysis of Variance. In SAS it is done using PROC ANOVA. It performs analysis of data from a wide variety of experimental designs. In this process, a continuous response variable, known as a dependent variable, is measured under experimental conditions identified by classification variables, known as independent variables. The variation in the response is assumed to be due to effects in the classification, with random error accounting for the remaining variation.

Syntax

The basic syntax for applying PROC ANOVA in SAS is −

PROC ANOVA dataset ;
CLASS Variable;
MODEL Variable1 = variable2 ;
MEANS ;

Following is the description of the parameters used −

dataset is the name of the dataset.
CLASS gives the variables the variable used as classification variable.
MODEL defines the model to be fit using certain variables from the dataset.
Variable_1 and Variable_2 are the variable names of the dataset used in analysis.
MEANS defines the type of computation and comparison of means.

Applying ANOVA

Let us now understand the concept of applying ANOVA in SAS.

Example

Lets consider the dataset SASHELP.CARS. Here we study the dependence between the variables car type and their horsepower. As the car type is a variable with categorical values, we take it as class variable and use both these variables in the MODEL.

PROC ANOVA DATA = SASHELPS.CARS;
CLASS type;
MODEL horsepower = type;
RUN;

When the above code is executed, we get the following result −

Applying ANOVA with MEANS

Let us now understand the concept of applying ANOVA with MEANS in SAS.

Example

We can also extend the model by applying the MEANS statement in which we use Turkey’s Studentized method to compare the mean values of various car types.The category of car types are listed with the mean value of horsepower in each category along with some additional values like error mean square etc.

PROC ANOVA DATA = SASHELPS.CARS;
CLASS type;
MODEL horsepower = type;
MEANS type / tukey lines;
RUN;

When the above code is executed, we get the following result −

SAS – Hypothesis Testing

Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. The usual process of hypothesis testing consists of four steps as shown below.

Step-1

Formulate the null hypothesis H0 (commonly, that the observations are the result of pure chance) and the alternative hypothesis H1 (commonly, that the observations show a real effect combined with a component of chance variation).

Step-2

Identify a test statistic that can be used to assess the truth of the null hypothesis.

Step-3

Compute the P-value, which is the probability that a test statistic at least as significant as the one observed would be obtained assuming that the null hypothesis were true. The smaller the P-value, the stronger the evidence against the null hypothesis.

Step-4

Compare the p-value to an acceptable significance value alpha (sometimes called an alpha value). If p <=alpha, that the observed effect is statistically significant, the null hypothesis is ruled out, and the alternative hypothesis is valid.

SAS programming language has features to carry out various types of hypothesis testing as shown below.

Test	Description	SAS PROC
T-Test	A t-tests is used to test whether the mean of one variable is significantly different than a hypothesized value. We also determine whether means for two independent groups are significantly different and whether means for dependent or paired groups are significantly different.	PROC TTEST
ANOVA	It is also used to compare means when there is one independent categorical variable. We want to use one-way ANOVA when testing to see if the means of the interval dependent variable are different according to the independent categorical variable.	PROC ANOVA
Chi-Square	We use chi square goodness of fit to assess if frequencies of a categorical variable were likely to happen due to chance. Use of a chi square test is necessary whether proportions of a categorical variable are a hypothesized value.	PROC FREQ
Linear Regression	Simple linear regression is used when one wants to test how well a variable predicts another variable. Multiple linear regression allows one to test how well multiple variables predict a variable of interest. When using multiple linear regression, we additionally assume the predictor variables are independent.	PROC REG

So, this brings us to the end of blog. This Tecklearn ‘One Way Anova and Hypothesis Testing in SAS’ blog helps you with commonly asked questions if you are looking out for a job in SAS. If you wish to learn SAS and build a career in Data Analytics domain, then check out our interactive, SAS Training for SAS BASE Certification Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/sas-training-for-sas-base-certification/

SAS Training for SAS BASE Certification Training

About the Course

SAS Certification Training is intended to make you an expert in SAS programming and Analytics. You will be able to analyse and write SAS code for real problems, learn to use SAS to work with datasets, perform advanced statistical techniques to obtain optimized results with Advanced SAS programming. In this SAS online training course, you will also learn SAS macros, Machine Learning, PROC SQL, procedure, statistical analysis and decision trees. You will also work on real-life projects and prepare for the SAS Certified Base Programmer certification exam. Upon the completion of this SAS online training, you will have enough proficiency in reading spreadsheets, databases, using SAS functions for manipulating this data and debugging it.

Why Should you take SAS Training?

The average salary for a Business Intelligence Developer skilled in SAS is $100k (PayScale salary data)
SAS, Google, Facebook, Twitter, Netflix, Accenture & other MNCs worldwide are using SAS for their Data analysis activities and advance their existing systems.
SAS is a Leader in 2017 Gartner Magic Quadrant for Data Science Platform.

What you will Learn in this Course?

Introduction to SAS

Introduction to SAS
Installation of SAS
SAS windows
Working with data sets
Walk through of SAS windows like output, search, editor etc

SAS Enterprise Guide

How to read and subset the data sets
SET Statement
Infile and Infile Options
SAS Format -Format Vs Informat

SAS Operators and Functions

Using Variables
Defining and using KEEP and DROP statements
Output Statement
Retain Statement
SUM Statement

Advanced SAS Procedures

PROC Import
PROC Print
Data Step Vs Proc
Deep Dive into Proc

Customizing Datasets

SAS Arrays
Useful SAS Functions
PUT/INPUT Functions
Date/Time Functions
Numeric Functions
Character Functions

SAS Format and SAS Graphs

SAS Format statements
Understanding PROC GCHART, various graphs, bar charts: pie, bar

Sorting Techniques

NODUP
NODUKEY
NODUP Vs NODUKEY

Data Transformation Function

Character functions, numeric functions and converting variable type
Use functions in data transformation

Deep Dive into SAS Procedures, Functions and Statements

Find Function
Scan Function
MERGE Statement
BY Statement
Joins
Procedures Vs Function
Where Vs If
What is Missover
NMISS
CMISS

PROC SQL

SELECT statement
Sorting of Data
CASE expression
Other SELECT statement clauses
JOINS and UNIONS

Using SAS Macros

Benefits of SAS Macros
Macro Variables
Macro Code Constituents and Macro Step
Positional Parameters to Macros

Got a question for us? Please mention it in the comments section and we will get back to you.

455