Table 2

comparison of classical regression and GWR results

CHD

Classical regression

GWR


O against E

O = E+GP

O against E

O = E+GP


Residual sum of squares

0.032

0.026

0.026

0.017


Standard deviation

0.010

0.009

0.009

0.007


Akaike Information Criterion

-2260.43

-2336.25

-2285.75

-2393.81


Correlation coefficient

0.299

0.439

0.427

0.637


Adjusted correlation coefficient

0.295

0.434

0.387

0.585


Sum of squares

0.0

0.0

0.0

0.0


Degrees of freedom

2.00

3.00

328.09

306.86


Hypertension

Classical regression

GWR


O against E

O = E+GP

O against E

O = E+GP


Residual sum of squares

0.374

0.250

0.362

0.241


Standard deviation

0.033

0.027

0.032

0.026


Akaike Information Criterion

-1400.41

-1539.57

-1403.50

-1543.67


Correlation coefficient

0.121

0.412

0.150

0.432


Adjusted correlation coefficient

0.116

0.407

0.134

0.421


Sum of squares

0.4

0.2

0.4

0.2


Degrees of freedom

2.00

3.00

344.87

344.04


Stroke

Classical regression

GWR


O against E

O = E+GP

O against E

O = E+GP


Residual sum of squares

0.007

0.006

0.005

0.003


Standard deviation

0.004

0.004

0.004

0.003


Akaike Information Criterion

-2807.25

-2873.16

-2838.92

-2932.93


Correlation coefficient

0.262

0.392

0.422

0.621


Adjusted correlation coefficient

0.258

0.387

0.374

0.561


Classical regression

GWR


O against E

O = E+GP

O against E

O = E+GP


Sum of squares

0.0

0.0

0.0

0.0


Degrees of freedom

2.00

3.00

324.37

302.87


O against E: ratio of observed against expected prevalence

O = E+GP: inclusion of GP supply as an additional independent variable

Soljak et al. BMC Cardiovascular Disorders 2011 11:12   doi:10.1186/1471-2261-11-12

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