The regression analysis below rotates the value of new car sales (In millions of dollars) and the independent variables "compensation" (in billions of dollars) and "employment level In the non-agricultural sector" (In thousands) for 44 consecutive quarters. Compare this multiple regression to the simple regressions with compensation and employment level of the respective Independent variables. Which of the following Is the likely culprit of the dramatic Increase In the p-value for employment level in the multiple regression? 1. Nonlinearity.
2. Heteroskedasticity.
3. Multicollinearity
4. None of the above.

The answer is option (3) Multicollinearity

Kindly find an attached copy of a diagram to the given question

Step-by-step explanation:

Solution

The method that would be responsible for causing the p-value to increase or go higher is Multicollinearity.

Multicollinearity: Refers to a method where variables that are independent in a regression model are associated.

This correlation or association of variables tends to be a problem, because variables that are independent should remain independent.

the temperature will be -18 just do 62 + (-40) + (-40)

step-by-step explanation:

median is the value separating the higher half of a data sample, a population or probablity distribution from the lower half.

step-by-step explanation:

say you have 3,8,7,3,1,9,8 order them from lowest to highest

if orderd it would be 1,3,3,7,8,8,9 now you take away from the lowest and the highest until you end up in the middle.

so you would remove the 1 and 9 leaving you with 5 digits remaining

when you've reached the middle that would be your answer so in this case the answer is 7

if there were two numbers left say 7 and 5 you'd and them together so 12 then divide by 2 witch would equal 6 so the answer would be 6

good luck on that project hoped i you at all