Use the dataframe Q6. Fit a model predicting 'Donation.Amount' from LIFETIME_MIN_GIFT_AMT and OVERLAY_SOURCE significance of the...

 

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Use the dataframe Q6. Fit a model predicting 'Donation.Amount' from LIFETIME_MIN_GIFT_AMT and OVERLAY_SOURCE significance of the predictor OVERLAY_SOURCE Note: The predictor OVERLAY_SOURCE be done by using drop1(). Check the previous activity and make sure to specify the correct test when using drop1(). (no interaction). Copy/paste the p-value of the test for If it is <2.2e-16 enter 0. is a categorical variable so the test should `{r} data=(Q6) M6<-1m (Donation.Amount~LIFETIME_MIN_GIFT_AMT+OVERLAY_SOURCE, data-Q6) visualize_model (M6) drop1 (M6, test="chisq") summary (M6) 公 X #12: Use dataframe Q12. First set the seed number to be 320. Split the dataframe Q12 into a training sample (75%) and holdout sample (25%). Build a predictive logistic regression model predicting 'Buy' from all remaining predictors in Q12 (no interactions), choosing as your final model the one suggested by the one standard deviation rule (make sure to use 320 for the seed number). Fit the model on the training sample, then report its misclassification rate on your holdout sample. The answer should be a number between 0 and 100. Note: Make sure to set the random number seed to 320 when splitting the data into traning, holdout sample and building a predictive logistic regression model. #11: Use the Q11 dataframe. You want a 'good' model predicting 'wins' from all remaining variables in Q11 (without interactions). Perform the 'all possible' approach, which produces a list of models with whose AICS are within 4 of the overall lowest AIc of all considered models. From this list of models, identify the predictors in the model with the FEWEST predictors (it could be the case your list has only one model). Redo the 'al1 possible' approach considering just those predictors ALONG WITH all two-way interactions between them to produce yet another list of models (your list might only have one). Report the AIC of the model at the top of this list, i.e., the one with the LOWEST AIC. Note: do not add ANY extra arguments (e.g., nbest or nvmax) to any of the commands. ` {r} data=(Q11) #10: Use dataframe Q10. Fit a logistic regression model predicting Smoking from Weight and SpouseRace with an interaction. Copy/paste the p-value of the interaction. If it is <2.2e-16 enter 0. Note: The predictor SpouseRace is a categorical variable so the test should be done by using drop1(). Check the previous activity and make sure to specify the correct test when using drop1(). `{r} data=(Q10) M10<-glm(Smoking-Weight*SpouseRace, data=Q10,family="binomial") visualize_model (Q10) drop1 (M10, test="chisq") Single term deletions Model: Smoking - Weight * SpouseRace Df Deviance AIC LRT Pr(>Chi) <none> 401.33 421.33 Weight:SpouseRace 4 406.96 418.96 5.632 0.2284 Use the dataframe Q6. Fit a model predicting 'Donation.Amount' from LIFETIME_MIN_GIFT_AMT and OVERLAY_SOURCE significance of the predictor OVERLAY_SOURCE Note: The predictor OVERLAY_SOURCE be done by using drop1(). Check the previous activity and make sure to specify the correct test when using drop1(). (no interaction). Copy/paste the p-value of the test for If it is <2.2e-16 enter 0. is a categorical variable so the test should `{r} data=(Q6) M6<-1m (Donation.Amount~LIFETIME_MIN_GIFT_AMT+OVERLAY_SOURCE, data-Q6) visualize_model (M6) drop1 (M6, test="chisq") summary (M6) 公 X #12: Use dataframe Q12. First set the seed number to be 320. Split the dataframe Q12 into a training sample (75%) and holdout sample (25%). Build a predictive logistic regression model predicting 'Buy' from all remaining predictors in Q12 (no interactions), choosing as your final model the one suggested by the one standard deviation rule (make sure to use 320 for the seed number). Fit the model on the training sample, then report its misclassification rate on your holdout sample. The answer should be a number between 0 and 100. Note: Make sure to set the random number seed to 320 when splitting the data into traning, holdout sample and building a predictive logistic regression model. #11: Use the Q11 dataframe. You want a 'good' model predicting 'wins' from all remaining variables in Q11 (without interactions). Perform the 'all possible' approach, which produces a list of models with whose AICS are within 4 of the overall lowest AIc of all considered models. From this list of models, identify the predictors in the model with the FEWEST predictors (it could be the case your list has only one model). Redo the 'al1 possible' approach considering just those predictors ALONG WITH all two-way interactions between them to produce yet another list of models (your list might only have one). Report the AIC of the model at the top of this list, i.e., the one with the LOWEST AIC. Note: do not add ANY extra arguments (e.g., nbest or nvmax) to any of the commands. ` {r} data=(Q11) #10: Use dataframe Q10. Fit a logistic regression model predicting Smoking from Weight and SpouseRace with an interaction. Copy/paste the p-value of the interaction. If it is <2.2e-16 enter 0. Note: The predictor SpouseRace is a categorical variable so the test should be done by using drop1(). Check the previous activity and make sure to specify the correct test when using drop1(). `{r} data=(Q10) M10<-glm(Smoking-Weight*SpouseRace, data=Q10,family="binomial") visualize_model (Q10) drop1 (M10, test="chisq") Single term deletions Model: Smoking - Weight * SpouseRace Df Deviance AIC LRT Pr(>Chi) <none> 401.33 421.33 Weight:SpouseRace 4 406.96 418.96 5.632 0.2284

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import numpy as np import pandas as pd from sklearnmodelselection import train... View the full answer