Logistic Regression Explained
<p>Linear regression models the relationship between a dependent variable and one or more independent variables <strong><em>by fitting a linear equation</em></strong> to observed data. The goal is to minimize <strong><em>the sum of squared errors</em></strong> — the squared differences between observed values and the values predicted by the model. The R-squared value represents the proportion of variance in the dependent variable that’s explained by the independent variables. A higher R-squared generally suggests a better fit, but it’s essential to check the residuals to ensure the model adequately captures underlying data patterns. Once the model is established, it can be used to predict <strong><em>continuous outcomes</em></strong> based on new input values for the independent variables.</p>
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