Which of the following is not a reason why multicollinearity is a problem in regression?
a. It creates heteroscedasticity (heterogeneity of variances) in the data.
b. It leads to unstable regression coefficients.
c. It makes it difficult to assess the individual importance of a predictor
d. It limits the size of R

step-by-step explanation:

there is a formula i can give you:

it is r^2*n*sin(360/n) divided by 2 where r represents the "radius" and n is the number of sides.

plug them in:

(10sqrt(3))^2*6*sin(360/6) divided by 2

100*3*6*sin(60) divided by 2

900*sin(60)

900*sqrt(3)/2

450*sqrt(3)

approx is 779.4 cm^2

d. it is a convex pentagon because it has five sides and none of the sides would extend into the inside of the polygon.

step-by-step explanation:

answer

you start with $5 and save $10 a week until you have at least $45wer

step-by-step explanation: