product depends on how much has been spent on advertising.
Based on the data-material above, a linear regression model is
estimated.(i.e with both linear and square term)
y_i=α+βx_i+γx_i^2+ε_i,i =1,…,5,
- The Following Data Material Describes How Much The Sale Of A Product Depends On How Much Has Been Spent On Advertising 1 (535 Bytes) Viewed 92 times
calculated, I_ β (0.95) = (0.88,5.97) and I_ γ (0.95) = (-
0.055.0.0035).
You further check if the effect that the advertising cost has on
sales is significant, ie you test H_(0,β): β = 0 to H_(1,β): β does
not equal 0 and H_ (0,γ): γ = 0 to H_(1,γ): γ does not equal 0.
Furthermore, two P-values are calculated for the two tests.
What conclusion can be drawn at the 5% significance level?
A: The P-value for the test H_(0,β): β = 0 is less than 0.05, and
the P-value of the test H_(0,γ): γ = 0 is greater than 0.05 and
thus the linear term is significant while the quadratic term is not
significant.
B: The P-value for the test H_(0,β): β = 0 is greater than 0.05,
and the P-value of the test H_(0,γ): γ = 0 is less than 0.05 and
thus the linear term is significant while the quadratic term is not
significant.
C: The P-value for the test H_(0,β): β = 0 is less than 0.05,
and the P-value of the test H_(0,γ): γ = 0 is greater than 0.05 and
thus the linear term is not significant while the quadratic term is
significant.
D: The P-value for the test H_(0,β): β = 0 is greater than 0.05,
and the P-value of the test H_(0,γ): γ = 0 is less than 0.05 and
thus the linear term is not significant while the quadratic term is
not significant.
Yi = sal ekkr
ci advertisingcosts(kkr)