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[-2 π(y=1) [1-π(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 parasite) 1 20 7. Suppose in his test for the overall assessment of the model outputs a p-value of 0.0001, the conclusion at a = 0.05 is ________ OA. the predictors are not significant O B. there is sufficient evidence to say that the model with predictors fits OC. there is insufficient evidence to say that the model with predictors fits O D. the model with predictors does not fit significantly

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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
= -2.88 +3.08CELL - 2.5INFIL
[1-π(y=1)]
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
7. Suppose in his test for the overall assessment of the model outputs a p-value of 0.0001, the conclusion at a = 0.05 is
O A. the predictors are not significant
O B. there is sufficient evidence to say that the model with predictors fits
O C. there is insufficient evidence to say that the model with predictors fits
O D. the model with predictors does not fit significantly
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 = -2.88 +3.08CELL - 2.5INFIL [1-π(y=1)] 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 7. Suppose in his test for the overall assessment of the model outputs a p-value of 0.0001, the conclusion at a = 0.05 is O A. the predictors are not significant O B. there is sufficient evidence to say that the model with predictors fits O C. there is insufficient evidence to say that the model with predictors fits O D. the model with predictors does not fit significantly
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