interpret R Output – Statistic

The 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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