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2943415207Lv1
19 Nov 2021
- Use the logistic model, fitted to the training data, to classify the test data.
(a)Fit a logistic model to the training data, using the variables Sepal.Length and Sepal.Width as predictors (3 points).
(i)Obtain the estimates and their standard errors for the model parameters (1.5 points).
(ii)Compute the confusion matrix for the test data and the misclassification error rate (1.5 points).
(iii)Are both of the predictor variables necessary for the purpose of classification? Explain your answer (3 points).
(b) Fit a logistic regression model to the training data, using the variable Sepal.Length as a one-dimensional predictor (3 points).
(i)Obtain the estimates and their standard errors for the model parameters (1.5 points).
(ii)Compute the confusion matrix for the test data, and the misclassification error rate (1.5 points).
(iii)Compare the results with those in 3(a). Does your result in 3(b)(ii) support the answer to 3(a)(iii) (3 points)?
- Use the logistic model, fitted to the training data, to classify the test data.
(a)Fit a logistic model to the training data, using the variables Sepal.Length and Sepal.Width as predictors (3 points).
(i)Obtain the estimates and their standard errors for the model parameters (1.5 points).
(ii)Compute the confusion matrix for the test data and the misclassification error rate (1.5 points).
(iii)Are both of the predictor variables necessary for the purpose of classification? Explain your answer (3 points).
(b) Fit a logistic regression model to the training data, using the variable Sepal.Length as a one-dimensional predictor (3 points).
(i)Obtain the estimates and their standard errors for the model parameters (1.5 points).
(ii)Compute the confusion matrix for the test data, and the misclassification error rate (1.5 points).
(iii)Compare the results with those in 3(a). Does your result in 3(b)(ii) support the answer to 3(a)(iii) (3 points)?
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rahulv336699Lv10
20 Nov 2021
