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.

$4034.40

the perimeter of the field is the sum of the lengths of the four sides:

47 yd + 158 yd + 47 yd + 158 yd = 410 yd

the cost of 3 ft (1 yd) of fence is 3×$3.28 = $9.84.

so the cost of jake's wood perimeter fance is

(410 yd) × ($9.84 /yd) = $4034.40

i those same problems in math and dont know how to do them