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Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex Web Site exist. 058\), which is compared to a chi-square distribution with \(10-5=5\) degrees of freedom to find the p-value = 0. The feature matrix is contained in the
X variable, and the dependent
variable matrix is retained in the Y
variable. stat. After looking at various subsets of the data, we find that a good model is one which only includes the labeling index as a predictor:CoefficientsTerm Coef SE Coef 95% CI Z-Value P-Value VIFConstant -3. \end{equation*}\]The raw residual is the difference between the actual response and the estimated probability from the model.

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9, 103. Least square methods are used to estimate the accuracy.
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