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: π(y=1) In = -6.03 +3.08CELL L1-π(y=1)] - 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 parasite) 1 20 6. The appropriate the graph to see all possible sensitivity and specificity given specific cut-off point is O A. dendogram O B. histogram O C. ROC curve OD. scatter plot
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: π(y=1) In = -6.03 +3.08CELL L1-π(y=1)] - 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 parasite) 1 20 6. The appropriate the graph to see all possible sensitivity and specificity given specific cut-off point is O A. dendogram O B. histogram O C. ROC curve OD. scatter plot
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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![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:
π(y=1)
In
= -6.03 +3.08CELL
L1-π(y=1)]
-
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 parasite)
1
20
6. The appropriate the graph to see all possible sensitivity and specificity given specific cut-off point is
O A. dendogram
O B. histogram
O C. ROC curve
O D. scatter plot](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3478eb2b-d575-460e-8941-9efa289dcb01%2F3b1d22c6-3c2e-4770-9c70-6666feabd212%2Fgov1jo_processed.jpeg&w=3840&q=75)
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:
π(y=1)
In
= -6.03 +3.08CELL
L1-π(y=1)]
-
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 parasite)
1
20
6. The appropriate the graph to see all possible sensitivity and specificity given specific cut-off point is
O A. dendogram
O B. histogram
O C. ROC curve
O D. scatter plot
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