Identifying the Smoking and Smokeless Tobacco-Related Predictors on Frequencies of Heavy Vehicle Traffic Accidents in Bangladesh: Linear and Binary Logistic Regression-Based Approach

Table 6

Coefficients, model summary, goodness-of-fit tests of the regression, and fits and diagnostics for unusual observations.

		Coef		SE coef		VIF
Coefficients
Term
Constant		12		125
Age		0.0182		0.0141		1.41
DrivingHour		0.0068		0.0372		1.29
AvgCigPerDay		0.0065		0.0200		2.13
CigUseYr		0.0350		0.0208		2.63
NosSmokeDurDrive		0.0633		0.0743		15.38
NosSmokePerDrivHr		−1.256		0.848		16.08
SmokeorTobaccoStatus
YES		−1.612		0.741		9.10
TypeofSmoke		0.174		0.699		10.50
SmokeDaily
Not at all		−0.47		1.10		22.13
TypofSmklessTobc
Jarda		−12		125		111918.84
Khoini		−0		261		1.30
No consumption		−13		125		141242.34
White-pata		−12		125		35901.36
SmkLessTobDailyStat
No consumption		1.105		0.946		8.34
Sometimes		−0.432		0.784		1.48
SmokeDurDrive
YES		1.105		0.337		2.31
Model summary	Deviance			Deviance
	R-Sq			R-sq (adj)	AIC	AICc	BIC
	7.98%			5.08%	541.01	542.52	609.86
Goodness-of-fit tests	Test			DF	Chi-square	P-value
	Deviance			407	507.01	0.001
	Pearson			407	425.55	0.253
	Hosmer–Lemeshow			8	10.22	0.250
Fits and diagnostics for unusual observations	Obs	Observed probability	Fit	Residuals	Std residuals	Type of residuals
	1	1.000	1.000	0.003	≤0.001		X
	20	1.000	0.726	0.801	0.87		X
	36	1.000	0.869	0.530	0.70		X
	37	1.000	0.698	0.848	0.95		X
	49	≤0.001	0.869	−2.018	−2.04	R
	72	≤0.001	0.485	−1.152	−1.27		X
	74	1.000	0.645	0.937	1.08		X
	94	≤0.001	0.399	−1.010	−1.16		X
	97	≤0.001	0.499	−1.175	−1.41		X
	100	≤0.001	0.481	−1.145	−1.30		X
	107	≤0.001	0.493	−1.166	−1.25		X
	109	≤0.001	0.530	−1.230	−1.40		X
	111	1.000	0.377	1.396	1.49		X
	121	1.000	0.713	0.823	0.99		X
	141	1.000	1.000	0.003	≤0.001		X
	150	1.000	1.000	0.002	≤0.001		X
	151	1.000	0.577	1.048	1.18		X
	161	≤0.001	0.692	−1.534	−1.65		X
	169	1.000	0.859	0.552	0.65		X
	191	≤0.001	0.766	−1.705	−1.82		X
	236	1.000	0.711	0.826	0.89		X
	246	1.000	1.000	0.003	≤0.001		X
	267	≤0.001	0.295	−0.836	−0.90		X
	274	≤0.001	0.921	−2.251	−2.27	R
	277	1.000	0.697	0.850	0.92		X
	289	≤0.001	0.234	−0.730	−0.81		X
	294	1.000	0.616	0.985	1.08		X
	301	1.000	0.402	1.351	1.54		X
	313	1.000	1.000	0.004	≤0.001		X
	314	≤0.001	0.908	−2.182	−2.21	R
	320	1.000	0.739	0.777	0.89		X
	336	≤0.001	0.863	−1.995	−2.02	R
	341	≤0.001	0.729	−1.616	−1.76		X
	370	1.000	0.481	1.210	1.40		X
	388	1.000	0.664	0.905	0.99		X

R = large std residuals; X = unusual std residuals.