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Table 3 Model 1: negative binomial model for seasonal influences on robbery

From: Testing time-sensitive influences of weather on street robbery

 

IRR

2.5%

97.5%

p-value

Adjusted p-value

(Intercept)

4.359

2.762

6.862

<0.001

0.000

Sequential

1.000

1.000

1.000

<0.001

0.000

Mean temperature (°C)

1.038

1.025

1.052

<0.001

0.000

Mean humidity

0.988

0.983

0.993

<0.001

0.000

Mean wind speed (km per hour)

0.989

0.984

0.994

<0.001

0.000

Proportion of fog

1.002

0.999

1.004

0.196

0.305

Proportion of rain

1.001

1.000

1.003

0.104

0.171

Proportion of snow

0.996

0.987

1.004

0.345

0.508

Spring

0.591

0.336

1.042

0.069

0.138

Summer

0.533

0.271

1.047

0.067

0.138

Autumn

0.820

0.428

1.57

0.549

0.673

Int: mean temperature and spring

0.973

0.957

0.99

0.002

0.008

Int: mean temperature and summer

0.967

0.948

0.986

0.001

0.005

Int: mean temperature and autumn

0.975

0.96

0.992

0.003

0.011

Int: mean humidity and spring

1.006

0.999

1.012

0.076

0.142

Int: mean humidity and summer

1.006

1.000

1.013

0.063

0.138

Int: mean humidity and autumn

1.001

0.994

1.009

0.695

0.759

Int: mean wind speed and spring

1.011

1.003

1.018

0.004

0.012

Int: mean wind speed and summer

1.010

1.002

1.019

0.017

0.043

Int: mean wind speed and autumn

1.010

1.002

1.017

0.013

0.036

Int: proportion of fog and spring

0.994

0.988

1.001

0.084

0.147

Int: proportion of fog and summer

0.999

0.989

1.008

0.790

0.808

Int: proportion of fog and autumn

1.001

0.996

1.005

0.808

0.808

Int: proportion of rain and spring

0.999

0.997

1.001

0.504

0.673

Int: proportion of rain and summer

1.000

0.997

1.002

0.705

0.759

Int: proportion of rain and autumn

0.999

0.997

1.002

0.577

0.673

Int: proportion of snow and spring

1.005

0.988

1.02

0.562

0.673

Int: proportion of snow and autumn

1.008

0.981

1.03

0.540

0.673

  1. N.B. Cragg and Uhler pseudo R2 = 0.068, the 2x log likelihood = −39421.2.