Selected article for: "linear regression and maximum value"
Author: Cai, Weiqin; Li, Chengyue; Sun, Mei; Hao, Mo
Title: Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China
  • Document date: 2019_11_21
    • ID: 0yiek4bg_16
    Snippet: A regression analysis was conducted to understand the contextual factors that might affect the within-group and between-group inequality of PHWs. This regression analysis was only performed for the municipal level. All 333 municipal-level CDCs were included in the fitted linear regression model. In the model, the inequality values between and within the three PHWs were taken as dependent variables. Since the decomposition value of the Theil index.....
    Document: A regression analysis was conducted to understand the contextual factors that might affect the within-group and between-group inequality of PHWs. This regression analysis was only performed for the municipal level. All 333 municipal-level CDCs were included in the fitted linear regression model. In the model, the inequality values between and within the three PHWs were taken as dependent variables. Since the decomposition value of the Theil index can have both negative and positive values, we transformed the between-municipality and within-municipality inequality values to only positive values from 0 to 1. The higher the value of the transformed variable, the higher the share of PHWs. We used the following formula used in similar studies to make this transition: Theil Transformed = (actual value−minimum value)/ (maximum value−minimum value) [10] .

    Search related documents:
    Co phrase search for related documents
    • contextual factor and municipality municipality: 1
    • contextual factor and phw inequality: 1
    • dependent variable and fit linear regression model: 1
    • dependent variable and linear regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • dependent variable and linear regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
    • fit linear regression model and linear regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
    • fit linear regression model and linear regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
    • group affect and linear regression: 1
    • linear regression and maximum value: 1, 2
    • linear regression and municipal level: 1, 2, 3
    • linear regression and municipality municipality: 1
    • linear regression model and municipal level: 1, 2
    • linear regression model and municipality municipality: 1