Consider a categorical response Y which takes possible values 0 and 1 as well as a...
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Consider a categorical response Y which takes possible values 0 and 1 as well as a single numerical predictor X. Recall that p(x) = P(Y = 1|X = z). Consider the model and estimated coefficients ● . = 2 8₁-1 log ( P(x)) = = 30 + 31x (a) Provide a classification when x = 2.5 that attempts to minimize the classification error. (b) Calculate an estimate of P(Y = 1|X = 1). (c) Calculate an estimate of P(Y=0|X = 2.5). (d) Find a value c that splits x into regions classified as 1 and 0. Consider a categorical response Y which takes possible values 0 and 1 as well as a single numerical predictor X. Recall that p(x) = P(Y = 1|X = z). Consider the model and estimated coefficients ● . = 2 8₁-1 log ( P(x)) = = 30 + 31x (a) Provide a classification when x = 2.5 that attempts to minimize the classification error. (b) Calculate an estimate of P(Y = 1|X = 1). (c) Calculate an estimate of P(Y=0|X = 2.5). (d) Find a value c that splits x into regions classified as 1 and 0.
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Applied Regression Analysis and Other Multivariable Methods
ISBN: 978-1285051086
5th edition
Authors: David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg
Posted Date:
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