interpret R Output – StatisticThe phenomenon of repealing criminal offenses is called recidivism. A study was conducted
among a sample of prisoners at a correctional facility in Baltimore to predict recidivism on the
basis of key demographic variables. The categorical dependent variable is Repeat (coded as 1 if
a prisoner has previously been convicted of a crime, and 0 if this is the first offense). The
independent variables are Age (in years), Marred (marital status coded as 1 if married and
living with spouse at the time of the offense), and Education (number of years).
Table 1 depicts the output from running a logistic regression model with all predictors using
60% of the data as training data.
(a) Suppose a particular prisoner is 42 years of age, not married and has had 7 years of education.
What is the predicted probability that this prisoner has previously been convicted?
Clearly show all calculations.
Assuming a cutoff of 0.5, should this individual be classified as a repeat offender or not?
(b) Provide an interpretation of the coefficient of Married.
(c) Compute the accuracy of the classifier (Hint: based on the result of the validation data).
(d) How many false positives are there in the validation data scoring? see the whole Q in the attachment
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