7 research outputs found

    Multiple linear regression analysis of socioeconomic factors.

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    **<p>Significant.</p><p>A multiple linear regression analysis was performed for each procedure (column 1) and three socioeconomic factors (column 2). Individual regression coefficients are identified (column 3), along with their respective 95% confidence intervals (column 4). The goodness of model fit (column 5) is the percent of the variation explained by the model. The P value (column 6) represents the significance of each regression model as a whole, incorporating education, income, and employment as variables. This model was significant in describing the relationship of the three socioeconomic variables and the prevalence of CABG and PTCA. No causal mechanism can be identified with any regression analysis technique.</p

    Relationship between individual socioeconomic variables and the prevalence of different procedures.

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    **<p>Strong correlation, Pearson's correlation coefficient.</p><p>A correlation matrix was established for various procedures (column 1) and socioeconomic factors (columns 2–4). A Pearson's correlation coefficient was established for each relationship. A negative value indicates a negative correlation. Value ranges of 0–0.09, 0.1–0.3, 0.31–0.5, and 0.51–1.0 were considered to have no, small, medium, and strong correlations, respectively. CABG and PTCA had a strong negative correlation with both education and income.</p><p>Socioeconomic Factors Key.</p><p>Employment = unemployed for greater than one year.</p><p>Education = having more than a high school education.</p><p>Income = household income greater than $ 50,000 USD.</p
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