Question

please a, b

a)which of the slope parameters are significantly different from zero?  Give hypotheses for each slope parameter, use alpha=0.05 to make decisions about each null hypothesis,

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2870476 0.1073076 11.994 <2e-16 ***
additive 0.0290057 0.0015435 18.792 <2e-16 ***
copper 0.0002050 0.0002636 0.778 0.44

b)Should we be concerned with multicollinearity for Model? Justify your answer.

> VIF(model)

  additive      additiveSq     copper

  12.71875   12.71875      1.00000

Answer 1

a) The parameter additive is significantly different from zero

Here p = 0.0000 <0.05

Hence additive was significant predictor.

The following is the hypothesis for the slope parameter additive

Ho:; H1:

The parameter copper is not significantly different from zero

Here p = 0.44 > 0.05

Hence copper was not a significant predictor.

The following is the hypothesis for the slope parameter copper

Ho: ; H1:

b) YES

Here there are three variables.

Multicollinearity is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.