Question

Parameter Estimates
Parameter	DF	Estimate	Standard Error	t Value	Pr > |t|
Intercept	1	-31.166890	2.880284	-10.82	<.0001
HouseholdInc	1	0.000097845	0.000027084	3.61	0.0003
DailyPM25	1	2.869205	0.147155	19.50	<.0001
PctSmokers	1	0.671836	0.048104	13.97	<.0001
PctObese	1	0.616837	0.080844	7.63	<.0001
PM25days	1	-0.244056	0.063909	-3.82	0.0001
OzoneDays	1	0.157997	0.035823	4.41	<.0001
PctDiabetic	1	1.164233	0.182226	6.39	<.0001

Regardless of the biological basis of disease or hypotheses, statistically speaking, are any of your variables in your final model presented from table above, associated with a decrease or less heart disease? If yes, provide the name of the variable or variables, and an explanation of why you made that determination based upon the model coefficient(s) (also known as parameter estimates)

Answer 1

Ans: From the above output we can form the model

heart disease = -31.1668 +0.0001  HouseholdInc + 2.869205 DailyPM25 + 0.671836  PctSmokers + 0.616837 PctObese - 0.244056  PM25days + 0.157997 OzoneDays +1.164233 PctDiabetic

In this model we clearly see that the association b/w the variable heart diseaese (dependent variable ) and  PM25days (independent variable) is negative as indicate by the (-) sign, since we can say with the increase in PM25days the heart disease become less or decreases.

other variable are postively associated since the increase in the other independent variable except PM25days Heart disease become increases.