1)
a) Binomial Distribution
b) Measures dispersion of probabilities with respect to a mean average value. Each possible value of S from 0 to N, the probability of observing S correct predictions given a sample of N independent examples of true accuracy P

2)
a) (150 + 180 + 420) / (150 + 180 + 420 + 30 + 50 + 50 + 40 + 50 + 30) = 0.75

# Variance of S $\sigma^2_S = N_p(1-p)$ 
# Std Dev of S $\sigma_S = \sqrt{N_p(1-p)}$
# Variance in F $\sigma_f = \frac{\sigma_S}{N} = \sqrt{\frac{N_p(1-p)}{N^2}} = \sqrt{\frac{p(1-p)}{N}}$

# Estimate of Predictive Accuracy $\mu_f = \frac{S}{N}$
# Successful Trials $S$
# Number of Trials $N$


750 Successes 1000 Trials
S = 750 
N = 1000
$\mu_f$ = 0.75
$\sqrt{(0.75 \times 0.25)/1000} = 0.0137$
when c = 80%, (100-80)/2 = 10%, z = 1.28

$\mu_f \pm z \times \sigma_f = 0.75 \pm (1.28 \times 0.0137)$
$= 0.75 \pm 0.0175$

p lies between 73.25% and 76.75%, with 80% confidence.

3)
a)
Stratified Holdout, data split to guarantee same distribution of class values in training and test set
b)
Repeated Holdout, training and testing done several times with different splits. Overall estimate of predictive accuracy is average of predicted accuracy in different iteration