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 answer is 200

step-by-step explanation:

the way the question is worded, it seems to imply the $264 interest is simple interest, not compound interest.

the simple interest for 2 years is $264; the simple interest for 1 year is $132.

$132 interest on a $600 investment means an interest rate of (132/600)*100 = 22%.

the value of $200 at 22% interest compounded annually for three years is

200%281.22%29%5e3

the amount of interest is that value, minus the original $200.

it's both, now can you answer my most recent question?