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 489

step-by-step explanation:

square root of 36/6 = 2.449489743

square root of 16/6 = 1.632993162

square root of 6 = 2.449489743

the answer is square root of 16/6 because the square root of 36/6 is the same answer as square root of 6 = 2.449489743