Question

Evaluate the following statement: In order to run most multivariate analyses, it is not necessary to meet all the assumptions of normality, linearity, homoscedasticity, and independence.

Answer 1

Linearity:

If the relationship is nonlinear then use a transformation to make it linear. The choice that works often is a log transformation. If the transformation works, then all the standard assumptions apply. If the relationship cannot be transformed to be linear then a nonlinear analysis is needed. Nonlinear analysis is a different course, and you probably should consult a statistician.

A good transformation can sometimes be used to solve the other problems. However, nonparametric tests may still be needed.

dependancy :

you must have dependency between dependant variable and independent variable... butdont have dependant in independent variable beacouse multicolinearity affected negativily on model, like overfitting, so that  time we use PCA for reduce multicolinearity ,,

check: in continuous variable we use correlation or scatter plot and in catagorical variable we use ch-square test...

notmality:

o saw real life problem is not normally follows.. so 90%real data not follow normal.. so then

my side

The problem is that, I continued the analysis, and ignored the non normality based on recommended from some friend. But now I need literature to support that.

I need literature to support that " In case of multivariate normality if the data not normal the researcher can ignore the normality and continue analysis .

homoscedasticity:

The impact of violating the assumption of homoscedasticity is a matter of degree, increasing as heteroscedasticity increases..The assumption of homoscedasticity (meaning “same variance”) is central to linear regressionmodels. Homoscedasticity describes a situation in which the error term (that is, the “noise” or random disturbance in the relationship between the independent variables and the dependent variable) is the same across all values of the independent variables. Heteroscedasticity (the violation of homoscedasticity) is present when the size of the error term differs across values of an independent variable.

Thanks