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Explanation: Logistic regression is a statistical method that models the relationship between a dependent variable and one or more independent variables using a logistic function. In healthcare, it is particularly valuable because it is used for binary classification tasks , such as predicting whether a patient has a certain disease (yes/no) or if a recovery will occur (yes/no). This is ideal for healthcare applications like diagnosis prediction or determining whether patients will need intensive care. The output of logistic regression is a probability score between 0 and 1, which makes it highly interpretable and suitable for binary outcomes, like disease diagnosis or treatment response. Option A: Linear regression is used for predicting continuous outcomes (e.g., price, temperature), not binary outcomes like disease presence. Option C: Time series analysis is typically used for forecasting data over time and would not directly be applied to binary outcome predictions like a diagnosis. Option D: SVM is a robust classifier, but logistic regression is often preferred for binary outcomes in medical fields due to its simplicity and interpretability. Option E: Decision Trees could also be used for classification, but logistic regression generally gives better probability outputs that are more interpretable in healthcare predictions.
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...Statements: S @ O, O & E, E $ K, K # C
Conclusions: I. S @ K II. K @ O III. C @ E
...Statements: P > Q = R ≤ S; R > T > U; U = Z ≥ O
Conclusions:
I. P > U
II. O < S
III. R > P
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Statements: H @ B, B * E, V © E, W $ V
 Conclusions:     I.W $ E                 II.H @ E               Â...
Statements: H # I, I @ J, J $ P
 Conclusions: a) H # J     b) H # P
Statement: A ≤ B ≤ C > D; E < D; F > E
Conclusions: I. D > A II. E < C
...Statement: M > N ≥ O; M ≤ P = Q; R > N
Conclusion: I. N < QÂ Â Â Â Â II. R > M
Statements: B ≥ C > D; B < E > J; G > A ≥ H > J
Conclusion:
I. D ≤ A
II. G > C