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Consider the data of with low (low birth weight) denoting the binary response, and three
predictors: age=age of mother, smoke=smoking status during pregnancy, and ht=history of
hypertension.
Low = 1 if birth weight 2500g, and low=0 if > 2500g;
smoke: Yes=1, No=0;
ht: Yes=1, No=0.
2.1 Fit a logistic regression using age, smoke and ht as predictors. Write down the model and
interpret the parameters.
2.2 Fit a logistic model with smoke as the predictor. Write down the model and interpret the
effect of smoke on the odds of low birth weight.
2.3 Compare the two models and explain which one is your final model and why?
2.4 Based on the goodness-of-fit test, describe how you think the model you chose fits the data.

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Consider the data of with �low� (low birth weight) denoting the bina.pdf

  • 1. Consider the data of with low (low birth weight) denoting the binary response, and three predictors: age=age of mother, smoke=smoking status during pregnancy, and ht=history of hypertension. Low = 1 if birth weight 2500g, and low=0 if > 2500g; smoke: Yes=1, No=0; ht: Yes=1, No=0. 2.1 Fit a logistic regression using age, smoke and ht as predictors. Write down the model and interpret the parameters. 2.2 Fit a logistic model with smoke as the predictor. Write down the model and interpret the effect of smoke on the odds of low birth weight. 2.3 Compare the two models and explain which one is your final model and why? 2.4 Based on the goodness-of-fit test, describe how you think the model you chose fits the data.