Utilizing Data Below.

Question 1) Would it be legitimate to drop the variables FairPr and FxCost from the regression if you wanted to do so? If the answer is yes, write down the new regression equation.

HINTS...

• Here both FairPr and FxCost have high p-values, however, they also are highly correlated (multicollinearity). Therefore, before deciding to drop them or not, you need to consider their joint significance (test for redundancy).

Question 2) One of your colleagues, Stan, draws your attention to a potential problem in the regression. He claims, "Almost all of the jobs the attorney general classified as rigged took place during the hot summer months. Everyone knows that jobs in hot weather are harder to do, and therefore command a greater premium over estimated costs than jobs done at other times of the year."

a) If Stan is right, what is wrong with the regression?

b) Suppose you could gather data regarding the time of year each job took place and use it to create a new variable, Hot (=1 if the job took place during the hot summer months and = 0 otherwise). If Stan is right, what would change (and how) when you included Hot as an additional independent variable in the regression?

HINTS...

• No calculations required here. Answer based on your understanding of the regression procedure.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.6514154
R Square	0.4243420
Adj. R Square	0.3969298
Std Error	0.1116232
Observations	133
ANOVA
	df	SS	MS	F	Sig. F
Regression	6	1.157260548	0.192877	15.48	3.035E-13
Residual	126	1.569927212	0.01246
Total	132	2.727187761
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 90.0%	Upper 90.0%
Intercept	0.91921096	0.029054027	31.63799	8.5E-62	0.8617139	0.976708	0.87106737	0.9673545
FairPr	-4.811E-05	4.41545E-05	-1.08949	0.278016	-0.000135	3.927E-05	-0.0001213	2.50596E-05
Bidders	-0.007504	0.004000064	-1.87607	0.06296	-0.015420	0.0004116	-0.0141326	-0.0008761
Rigged	0.18322604	0.024960695	7.340583	2.31E-11	0.13382	0.2326225	0.14186525	0.2245868
Length	0.00094679	0.002499299	0.378824	0.705456	-0.003999	0.0058928	-0.0031946	0.0050882
FxCost	5.4998E-05	6.27424E-05	0.876576	0.382385	-6.916E-05	0.0001792	-4.897E-05	0.0001589
Days	0.00020769	0.000121987	1.702599	0.091109	-3.371E-05	0.0004491	5.558E-06	0.00040983