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 2
Coefficients and model summary of the linear regression and fits and diagnostics for unusual observations.
Coef
Standard error (SE) coef
T value
value
Variance inflation factor (VIF)
Coefficients
Term
Constant
3.09
1.53
2.03
0.043
AvgCigPerDay
0.0346
0.0206
1.68
0.094
2.33
NosSmokePerDrivHr
−1.972
0.867
−2.27
0.024
16.00
SmokeDurDrive
YES
0.871
0.341
2.56
0.011
2.33
Foursmokeormore
YES
5.11
2.03
2.52
0.012
1.60
Age
0.0018
0.0141
0.13
0.898
1.54
DrivingHour
−0.0158
0.0389
−0.41
0.684
1.34
CigUseYr
0.0065
0.0201
0.32
0.748
2.85
NosSmokeDurDrive
0.1332
0.0756
1.76
0.079
16.18
SmokeorTobaccoStatus
YES
−1.135
0.731
−1.55
0.121
8.08
SmokeDaily
Not at all
−0.605
0.788
−0.77
0.443
10.55
TypofSmklessTobc
Jarda
−0.80
1.06
−0.75
0.456
7.66
Khoini
−3.38
2.13
−1.58
0.114
1.77
No consumption
−1.42
1.17
−1.22
0.225
13.32
White-pata
−1.29
1.18
−1.09
0.274
3.16
SmkLessTobDailyStat
No consumption
0.694
0.955
0.73
0.467
8.91
Sometimes
−0.488
0.764
−0.64
0.523
1.44
Model summary
S
R-sq
R-sq (adj)
R-sq (pred)
2.26280
8.74%
5.15%
≤0.001%
Fits and diagnostics for unusual observations
Obs
AccidentFreq
Fit
Residuals
Std residuals
Type of residuals
1
2.00
−1.02
3.02
2.36
R
X
20
2.00
1.05
0.95
0.46
X
34
9.00
1.57
7.43
3.31
R
36
12.00
8.98
3.02
2.36
R
X
37
2.00
1.62
0.38
0.19
X
44
9.00
1.96
7.04
3.13
R
54
7.00
2.44
4.56
2.04
R
55
9.00
2.09
6.91
3.09
R
60
9.00
1.36
7.64
3.42
R
68
12.00
1.45
10.55
4.70
R
72
≤0.001
1.15
−1.15
−0.55
X
74
1.00
2.02
−1.02
−0.49
X
94
≤0.001
0.66
−0.66
−0.33
X
97
≤0.001
0.71
−0.71
−0.36
X
100
≤0.001
1.44
−1.44
−0.71
X
102
9.00
2.33
6.67
2.99
R
104
9.00
1.23
7.77
3.48
R
109
≤0.001
1.09
−1.09
−0.54
X
121
7.00
3.90
3.10
1.62
X
124
12.00
2.63
9.37
4.20
R
133
10.00
1.71
8.29
3.69
R
140
7.00
1.71
5.29
2.41
R
141
3.00
2.01
0.99
0.51
X
150
2.00
5.02
−3.02
−2.36
R
X
151
1.00
1.19
−0.19
−0.09
X
169
2.00
3.50
−1.50
−0.75
X
191
≤0.001
1.79
−1.79
−0.85
X
205
1.00
1.44
−0.44
−0.21
X
216
12.00
2.09
9.91
4.42
R
217
8.00
1.63
6.37
2.83
R
218
10.00
1.63
8.37
3.72
R
236
2.00
1.25
0.75
0.35
X
242
8.00
1.95
6.05
2.71
R
246
1.00
2.02
−1.02
−0.52
X
267
≤0.001
0.22
−0.22
−0.10
X
279
≤0.001
−0.06
0.06
0.03
X
284
3.00
3.63
−0.63
−0.31
X
289
≤0.001
−0.63
0.63
0.31
X
294
5.00
1.09
3.91
1.87
X
295
9.00
1.68
7.32
3.25
R
298
2.00
2.86
−0.86
−0.43
X
301
3.00
1.05
1.95
0.96
X
308
7.00
2.35
4.65
2.07
R
313
1.00
2.37
−1.37
−0.70
X
320
1.00
3.01
−2.01
−1.01
X
332
6.00
3.11
2.89
1.43
X
341
≤0.001
0.74
−0.74
−0.36
X
359
≤0.001
−0.05
0.05
0.02
X
370
2.00
1.04
0.96
0.47
X
379
12.00
2.67
9.33
4.17
R
380
9.00
1.69
7.31
3.25
R
388
1.00
1.59
−0.59
−0.28
X
406
9.00
1.93
7.07
3.20
R
408
7.00
2.03
4.97
2.22
R
411
10.00
3.20
6.80
3.08
R
R = large std residuals; X = unusual std residuals.
