Question

If we are interested in the impact of Chinese import competition (D_Imports_Per_Worker), why do we include other variables in the regression?

You should discuss the following topics in a cohesive reports, not an itemized list:

When aplha is .01 wha can we say that D_Imports_Per_Worker has an impact on each y-variable?

Pick one of the regressions and discuss what does the coefficient on years2000_2010 tell you?

How much would the unemployment rate go up for those without a college education if a region had a value of D_Imports_Per_Worker = $1000.

Discuss who seemed to be hurt the most by impacted by Chinese import competition.

If we are interested in the impact of Chinese import competition (D_Imports_Per_Worker), why do we include other variables in the regression?

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	-0.37390951	1.091669752	-0.34251156	0.732016106	-2.515349098	1.767530079
D_Imports_Per_Worker	-0.107697625	0.0233622	-4.609909344	4.38643E-06	-0.153525349	-0.061869901
Manufact_Share	-0.045107714	0.006163296	-7.318765331	4.15064E-13	-0.057197748	-0.033017679
College_Educated	0.022201975	0.009352853	2.373818404	0.017736193	0.003855245	0.040548706
Foreign_Born	0.028041113	0.011026008	2.543179142	0.01108911	0.006412292	0.049669933
years2000_2010	-1.899658168	0.143217809	-13.26411973	5.81123E-38	-2.180596874	-1.618719463

Answer 1

D_Imports_Per_Worker (x1) is significant ( p-value is less than alpha=0.01) in the regression model to know the Chinese_import_competition (y) at alpha=0.01, so we can include other variable to look the wether the included variable is significant.

years2000_2010 (x5) is singificant predictor for response variable y, as the p-value is less than alpha=0.01.

since the coefficent of x1 = -0.107697625, if x1=1000 then change in unemployment (y)when College_Educated (x3)=0

so requiered change in y=-0.107697625*1000+0*0.022201975=107,69

years2000_2010 is most hurt as its its t-value is negative and highest.