Statistical Analysis System (SAS) is one of the most advanced software systems for report writing and data analysis. In simple terms, SAS is a group of programs designed to work together and store data values. You can retrieve these data values, modify them, compute complex or straightforward statistical analysis, and create reports.
SAS comes with a Linear Regression model, simply a method to analyze the relationship between two quantitative variables. Let's say that there are two variables, X and Y. If you represent the relationship between these two variables using the linear function, then the strength of impact is the function's slope. A test made on slopes is known as a linear influence test.
In this article, we will talk about SAS linear regression in detail.
Use of SAS Linear Regression
Linear Regression in SAS is the best way to identify the relationship between one or more independent variables or a dependent variable. The model of relationship is first proposed, and then the estimation of the parameter values is made to develop a regression equation (estimated).
After this process, a set of tests are performed to determine if the model proposed is correct or not. If the model is as per the requirement, you can use the estimated regression equation to predict the value of the dependent variable in accordance with the values for the independent variables.
The syntax for SAS Linear Regression is:
PROC REG DATA = dset;
MODEL var_1 = var_2;
- dset is the name of the dataset.
- var_1 & var_2 are the names of the variables of the dataset.
Here’s an example that explains the use of SAS Linear Regression:
create table Shampoo1 as
SELECT type, name, weight, invoice
WHERE make in ('Oily Hair', 'Lavender Shampoo')
proc reg data = Shampoo1;
Model name = type;
How to Perform SAS Linear Regression
To perform or run SAS Linear Regression, run a SAS Linear Regression with the PROC REG procedure. This procedure is generally referred to as a general-purpose procedure for regression in SAS.
PROC REG procedure returns the most crucial parameters and statistics, and for this, it becomes the most straightforward method to program.
Here are the steps you must follow to perform SAS linear regression with PROC REG:
- Start your PROC REG Procedure: First, you need to start the procedure. For this, you have to use the PROC REG statement.
- Define the input dataset: Enumerate the name of the input dataset using the DATA=option. You can use a dataset from the work or a permanent library.
- Specify the relationship between your variables: Define the relationship between your variables with the MODEL statement. The statement starts with the MODEL keyword, the dependent variable, equal sign, and independent variable.
- Finish and implement the PROC REG procedure: Use the RUN statement to finish and execute your code.
When you utilize PROC REG to create a linear model, SAS makes a report containing the analysis of variance, parameter estimates, and scatter plots and histograms.
How to Run a Simple Linear Regression With SAS Studio?
If you don't like writing code to run a simple linear regression, make use of the SAS Studio instead. The SAS Studio offers a point-and-click interface that guides you through building a simple linear regression model with absolutely no coding.
Following are the steps to run a simple linear regression with SAS Studio:
1. Open The Linear Regression Task
For running a simple linear regression in SAS Studio, utilize the "Linear Regression" task. In the "Tasks and Utilities" pane, you find this task under Tasks > Linear Models. Right-click the Linear Regression task and tap Open to create a linear regression.
2. Select The Input Dataset
After you've opened the Linear Regression task, you can easily build a Simple Linear Regression. To begin with, choose the input dataset.
Pick the input dataset in the Data tab from the Data option. You can jot the name of your dataset or browse by selecting the table icon. Post selecting the input dataset, you can add filters to the data.
3. Select The Dependent Variable
After selecting the input dataset, you can easily define the roles of your regression model, i.e., the dependent and independent variables. First, select the dependent variable.
You can select the dependent variable by tapping on the plus icon. A new window will offer you a list of all numeric variables available in your dataset. Select any one variable and click the OK button to give this variable the role of the dependent variable.
4. Select The Independent Variable
You have to choose the independent variable once you define the dependent variable. Firstly, you must select numeric variables except for the dependent variable, which enters the model. Hence, it's clear that it's a two-step procedure.
Scroll down the tab until you find the variable section, which continues. This is a practical step to choose the numeric variables. It opens with a pop-up window after clicking on the plus icon. The pop-up window consists of numeric variables in a dataset. After selecting the appropriate numeric variable for the model, click on the OK button.
Note: You could have selected more than one variable, but as we are heading to a simple linear regression, hence, we will choose less than two variables.
5. Run The Simple Linear Regression
Since we are done with selecting independent and dependent variables, you can run or operate the respective model. You perform the above step by pressing the F3 Or Run button.
6. Check The Results
To check the result of linear regression, click on the Results/Code section and then select the Results tab to get the actual result of linear regression.
The Results tab shows the regression result of the model and shows several graphs and tables. These graphs and tables are used to calculate the model's parameters. And to cross-check all resumption of the linear model.
7. SAS Code Examination
As earlier stated, you are supposed to write a single code so that it can run the simple linear regression. Check the Code tab if you are interested in SAS code. Here, you will find the code being created on its own, i.e., automatically.
Other Statistical Procedure (make this a listicle where you can discuss arithmetic mean, standard deviation, etc.)
Other Statistical Procedures
Arithmetic means - The most frequent and straightforward central tendency is the mean. The value generated by reducing the total of observations by the number of comments information in the data set is the mean, and the symbol x symbolizes it. The arithmetic mean, often known as the mean or average value, is the result of adding two numbers or factors and dividing them by the total of figures or elements afterward.
Standard deviation - A standard deviation (or ) is a measurement of data dispersion in proportion to the mean. Data are grouped around the mean when the standard deviation is low, while data are much more scattered when the standard deviation is significant.
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