Question

Explain why two perfectly multicollinear regressors cannot be included in a linear multiple regression. If those same two regressors were not perfectly collinear but highly collinear what difference, or differences, would that make? Give an example of a pair of perfectly multicollinear regressors.

Answer 1

MULTICOLLINEAR REGRESSION:

• It is a regression analysis.
• and it is the relationship between INDEPENDENT and DEPENDENT VARIABLE.
• The value of independent variable alone changed,not the others.
• There are two perfect multicollinear regression.they were:1)data multicollinearity
• 2)structural multicollinearity.
• these two will face the two major problems,they are COEFFICIENT ESTIMATES.
• This is known ad MULTICOLLINEAR REGRESSION.

LINEAR MULTIPLE REGRESSION:

• It is known as linear regression in simple way.
• This is the relationship between two variables using the straight lines.
• It is used when we need to predict the value of the products.
• Here we mostly predict DEPENDENT VARIABLE.
• This have few assumptions,like HOMOSCEDASITY,LINEAR RELATIONSHIP,NO AUTO CORRELATION ETC..
• this is known as LINEAR MULTIPLE REGRESSION.

((THE MAIN FACTOR IS THAT IN MULTICOLLINEAR REGRESSION WE USE independent variable BUT IN LINEAR REGRESSION WE USE depenednt variable.

THEN LINEAR EXPRESSION HAD scalar response,MULTICOLLINEAR REGRESSION HAD variable response.))

EXAMPLE FOR MULTICOLLINEAR REGRESSION:

THIS WILL BE OCCUR IN WHERE HIGH CORRELATIONS OCCUR.

The casual example is :A Person's height and weight.

female and male equalities.

performaces between many students.

SO THESE ARE SOME OF THE EXAMLES OF MULTICOLLINEAR REGRESSION.