5. Cancer is a disease caused by abnormal growth and proliferation of cells in the body. A model is constructed to predict whether a cancer is malignant (1) or benign (0) using uniformity in cell size (X1, score from 1 to 10) and uniformity in cell shape (X2, score from 1 to 10). The biopsy results of two hundred randomly selected patients from a certain hospital were used to construct the following model: = -5.07 +0.60X₁ +0.79X₂ In π(y=1) [1-n(y=1)] The confusion matrix for the constructed model is shown below: Outcome 0 (benign) 1 (malignant) Predicted Probability < 0.5 > 0.5 130 5 9 56 Suppose the test for the overall assessment of the model outputs a p- value of 0.0001, the conclusion at a = 0.05 is A. the predictors are not significant B. there is sufficient evidence to say that the model with predictors fits C. there is insufficient evidence to say that the model with predictors fits D. the model with predictors does not fit significantly

5. Cancer is a disease caused by abnormal growth and proliferation of cells in the body. A model is constructed to predict whether a cancer is malignant (1) or benign (0) using uniformity in cell size (X1, score from 1 to 10) and uniformity in cell shape (X2, score from 1 to 10). The biopsy results of two hundred randomly selected patients from a certain hospital were used to construct the following model: = -5.07 +0.60X₁ +0.79X₂ In π(y=1) [1-n(y=1)] The confusion matrix for the constructed model is shown below: Outcome 0 (benign) 1 (malignant) Predicted Probability < 0.5 > 0.5 130 5 9 56 Suppose the test for the overall assessment of the model outputs a p- value of 0.0001, the conclusion at a = 0.05 is A. the predictors are not significant B. there is sufficient evidence to say that the model with predictors fits C. there is insufficient evidence to say that the model with predictors fits D. the model with predictors does not fit significantly

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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