The following estimated regression equation was developed for a model involving two independent variables.

After x2 was dropped from the model, the least squares method was used to obtain an estimated regression equation involving only x1 as an independent variable.

a. In the two independent variable case, the coefficient x1 represents the expected change in (Select your answer : y, x1, x2) corresponding to a one unit increase in (Select your answer: y, x1, x2) when (Select your answer: y, x1, x2) is held constant.
In the single independent variable case, the coefficient x1 represents the expected change in (Select your answer: y, x1, x2) corresponding to a one unit increase in (Select your answer: y, x1, x2) .
b. Could multi-collinearity explain why the coefficient of x1 differs in the two models? Assume that x1 and x2 are correlated.
Yes, because a change in x1 would be accompanied by a change in x2
Yes, because a change in x1 would not be accompanied by a change in x2

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S Amount Est % Uncollectible Days Outstanding 0 -30 2,350,000 3% 31-60 1,110,000 7% 8% 61-90 750,000 91-120 300,000 10% 120+ 62,000 100% Days Outstanding S Amount Est % Uncollectible 0 -30 2,350,000 3% 31-60 1,110,000 7% 61-90 750,000 8% 91-120 300,000 10% 120+ 62,000 100%