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.