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Regression technique is mostly used in prediction technology, in SAS it is deal with proc regression. It is very useful and easy to finding the relation between dependent and independent variable.
Regression are mostly 3 types—
Linear Regression
Multiple Regression
Logistic Regression
Linear regression finding the relationship between one dependent variable and one or more independent variables, but in case of multiple regression it deals with more than two independent variables. Also, in logistic regression independent variables comes as binary like true or false/yes or no and might be 1/0.
Logistic regression is the most important which are used in machine learning algorithm deal with supervised learning technique. It is used for predicting the dependent categorical data.
Dependent variables are also known as criterion variable which are going to be predict on the basis of independent variables and independent variables is known as predictor variable.
Proc Regression output
by default, regression———it showing so many plot
Diagnostic plot
Residual plot
Fit plot
Partial plot
Ridge plot
procedure——-
if u want to fit a model to the data, u must use the model statement. If u want to use the reg
option no need to use the model statement but use the var statement
Regression output
number of items reads———-number of observations in our input data set
number of items used———-the number of observations used in fitting our model.
parameter estimates
variable name
label
DF—degree of freedom
standard error
t value—t-test
p value—–probability value
root MSE
coefficient variation
r square
adjusted r square
f value
mean square
Trick-
R Square—— 0 to 1
model—————-near to one——very-very good model
R Square——-0.98———-very good
If You are considering more than two variables that most preferably look for adjusted R square
Syntax of proc Reg
Proc reg data= dataset name;
Model variable1=variable2;
Run;
Dependent variable always should write along with model statement.
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