POISSON REGRESSION We will use the dataset “poisson_data” fo…
POISSON REGRESSION We will use the dataset “poisson_data” for this question ## Features: **Transaction_Hour (Numerical):** Hour of the day when the transaction occurred (0-23) **Previous_Frauds (Numerical):** Number of previous fraudulent transactions by the user (0-5) **Account_Age_Days (Numerical):** Age of the account in days (1-5000) **Fraud_Count (Numerical):** Number of frauds (Response variable) Q6 Poisson Regression (Use poisson_data for this question) (5 points) a. i) (2 points) Fit a poisson regression model using all the predictors from the “poisson_data” and “Fraud_Count” as the response variable. Call it ’pois_model1 and display the model summary. ii) (1 point) Interpret the coefficient of “Previous_Frauds” in pois_model1 with respect to the log expected “Fraud_Count”. b. (2 points) Calculate the estimated dispersion parameter for “pois_model1” using both the deviance and Pearson residuals. Is this an overdispersed model using a threshold of 2.0? Justify your answer.
Read DetailsQ3. Goodness of fit tests (Use trainData for this question)…
Q3. Goodness of fit tests (Use trainData for this question) (9 points) a.i (1 point) Can we perform goodness-of-fit tests for model2? Why or why not? ii. (1 point) If not, how can we modify the dataset to enable goodness-of-fit testing?iii. (5 points) Convert the dataset accordingly and fit a logistic regression model. Name it model3, and display its summary. b. (2 points) Use the Deviance residuals to form goodness-of-fit hypothesis tests on model3. What do you conclude from the result of the test?
Read DetailsWe apply a classification model and obtain the estimated cla…
We apply a classification model and obtain the estimated classification provided in the table, which also provides the observed classification. Use the following table contrasting the estimated versus the observed classification for questions 15-20: Estimated Classification YES NO Observed Classification YES 105 228 NO 40 9627
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