In a regression analysis with multiple independent variables, multicollinearity can be caused by: A strong nonlinear relationship between the dependent variable and one or more independent variables A strong heteroskedastic relationship between the dependent variable and one or more independent variable A strong linear relationship between two or more independent variables None of the above

A strong linear relationship between two or more independent variables

Step-by-step explanation:

Multicolinearity underestimates the statistical significance of the independent variables. It exists when an independent variable is highly correlated with one or many other independent variables giving rise to a large standard error.

f(x) = x^3 -2x^2 -3x

x( x^2 -2x-3)

x( x^2 +x -3x -3)

x( x(x+1) -3(x+1))

x( x-3)( x+1)

so zeroes are 0, 3,-1

its (2) option