PMC:5664696 / 13772-15097 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/5664696","sourcedb":"PMC","sourceid":"5664696","source_url":"https://www.ncbi.nlm.nih.gov/pmc/5664696","text":"After modeling, the classical multiple regression model fit was always lower than 0.6, and the Durbin-Watson value was 1.268. This showed that the residual of ordinary least square (OLS) has a positive autocorrelation structure and was not suitable for establishing models with OLS. The regression parameters in this study varied relative to geographic location. Regression parameter estimation of a global regression model is the average value of the regression parameters in a study area. This fails to reflect the spatial character of all the regression parameters. Factors in this study such as Se, temperature, humidity, altitude and fish consumption vary according to regional characteristics and spatial effects. In this case, we adopted a geographically weighted regression (GWR) model. In GWR, geographic location was embedded in the regression parameters. Consequently, observational data close to the position has more influence than data far from the position, resulting in adjacent locations having similar regression parameters. We tested all the factor groups and found that the correlation coefficient of the group consisting of humidity, altitude, and per capita sea fish consumption was highest, both in 2000 and 2010. In addition, the top four groups with highest R2 are illustrated in Table 6 and Table 7.","tracks":[]}