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What is the probability of observing leukemia remission when the cellularity of the marrow clot section is 0.8 and the proportion of absolute marrow leukemia cell infiltrate is 0.63? 2. The specificity of MacBee’s model is _____% 3. The accuracy of MacBee’s model is ____% 4. The appropriate the graph to see all possible sensitivity and specificity given specific cut-off point is ____. 5. Suppose in his test for the overall assessment of the model outputs a p-value of 0.0001, the conclusion at α = 0.05 is ____.

What is the probability of observing leukemia remission when the cellularity of the marrow clot section is 0.8 and the proportion of absolute marrow leukemia cell infiltrate is 0.63? 2. The specificity of MacBee’s model is _____% 3. The accuracy of MacBee’s model is ____% 4. The appropriate the graph to see all possible sensitivity and specificity given specific cut-off point is ____. 5. Suppose in his test for the overall assessment of the model outputs a p-value of 0.0001, the conclusion at α = 0.05 is ____.

MATLAB: An Introduction with Applications
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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1. What is the probability of observing leukemia remission when the cellularity of the marrow clot section is 0.8 and the proportion of absolute marrow leukemia cell infiltrate is 0.63?

2. The specificity of MacBee’s model is _____%

3. The accuracy of MacBee’s model is ____%

4. The appropriate the graph to see all possible sensitivity and specificity given specific cut-off point is ____.

5. Suppose in his test for the overall assessment of the model outputs a p-value of 0.0001, the conclusion at α = 0.05 is ____.

For numbers 2-7: A remission is when leukemia cannot be detected in the body and there are no symptoms. A total of 27 random sample of patients from a certain hospital was obtained. The Leukemia Remission data set has a response variable of whether leukemia remission occurred (Y=1). The predictor variables are cellularity of the marrow clot section (CELL) and proportion of absolute marrow leukemia cell infiltrate (INFIL). Regression analysis was done and the model is given by: In(1²(Y-1)) = -2.88 +3.08CELL - 2.5INFIL Macbee wants to validate the model above. He observed 30 random sample of patients from the same hospital. He came up with a confusion matrix below. Predicted Probability Outcome < 0.5 > 0.5 0 (with parasite) 5 2 1 (without 1 20 parasite)
Transcribed Image Text:For numbers 2-7: A remission is when leukemia cannot be detected in the body and there are no symptoms. A total of 27 random sample of patients from a certain hospital was obtained. The Leukemia Remission data set has a response variable of whether leukemia remission occurred (Y=1). The predictor variables are cellularity of the marrow clot section (CELL) and proportion of absolute marrow leukemia cell infiltrate (INFIL). Regression analysis was done and the model is given by: In(1²(Y-1)) = -2.88 +3.08CELL - 2.5INFIL Macbee wants to validate the model above. He observed 30 random sample of patients from the same hospital. He came up with a confusion matrix below. Predicted Probability Outcome < 0.5 > 0.5 0 (with parasite) 5 2 1 (without 1 20 parasite)
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