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 9
Regression equation, coefficients, model summary, goodness-of-fit tests of the regression, and fits and diagnostics for unusual observations.
SmokeorTobaccoStatus
SmokeDurDrive
Regression equation
NO
NO
NO
YES
YES
NO
YES
YES
Coefficients
Term
Coef
SE coef
VIF
Constant
0.738
0.212
CigUseYr
0.0531
0.0161
1.63
SmokeorTobaccoStatus
YES
−1.183
0.318
1.70
SmokeDurDrive
YES
0.684
0.245
1.28
Model summary
Deviance
Deviance
R-sq
R-sq (adj)
AIC
AICc
BIC
4.69%
4.15%
533.12
533.22
549.32
Goodness-of-fit tests of the regression
Test
DF
Chi-square
value
Deviance
420
525.12
≤0.001
Pearson
420
429.22
0.367
Hosmer–Lemeshow
7
13.31
0.065
Fits and diagnostics for unusual observations
Obs
Observed probability
Fit
Residuals
Std residuals
Type of residuals
49
≤0.001
0.8619
−1.9900
−2.00
R
205
1.0000
0.8427
0.5851
0.60
X
250
1.0000
0.8562
0.5572
0.57
X
274
≤0.001
0.8957
−2.1261
−2.14
R
341
≤0.001
0.8204
−1.8531
−1.88
X
R = large std residuals; X = unusual std residuals.