Question

A and B for each one five reasons please do not mix the answer .

A-for multiple regression why the underlying statistical model is important ? five reasons .

B-for linear regression why the underlying statistical model is important? five reasons ?

Answer 1

Multiple Regression:

The underlying statistical model is important because:

(1) To predict what is the output for a set of input data. How a minor change in a particular type of input data may effect on the output information. Kindly note that in this model there will be multiple variables... For example how is lifestyle affected by income, family size, expenditure and many more... Here, lifestyle will be dependent and the others will be independent...

(2) To predict which among the variables are statistically more significant with the output.

(3) To check whether the model fits the data.

(4) To check whether we can add more variables or reduce some in order increase the goodness of fit.

(5) To check how much variations in the dependent variable is explained by the independent variables. Generally it should be more than 1.

Linear Regression:

Generally, Multiple regression is a part of linear regression... hence the importance is same for both the cases... But the difference is that in multiple regression, there should be more than one independent variables and in the case of linear, it can be 1 to infinity...

Here, the importance is same for both the cases...