Suppose you've assumed the following data-generating process:

Yi = + 1 X1i + 2 X2i + 3 X3i + 4 X4i + Ui

You have collected a dataset, and you are also willing to assume that you have a random sample and

that the errors are not correlated with the Xs. Below are the regression results when regressing Y on

the Xs: (LO5)

COEFFICIENTS STANDARD ERROR t STAT P-VALUE LOWER 95% UPPER 95% VIF

Intercept 415.59 212.44 1.96 0.051 2.66 833.83 N/A

X1 77.03 30.53 2.52 0.012 16.93 137.13 412.02

X2 67.57 60.78 1.11 0.267 187.23 52.10 196.15

X3 71.76 108.35 0.66 0.508 285.07 141.55 149.46

X4 58.18 46.03 1.26 0.207 32.45 148.81 66.37

a. According to these results, is there evidence that any of the Xs affects Y?

b. Why might collection of more data be particularly useful for this analysis?