import scipy.stats as stats

import math

​

# Specify sample occurrences (x), sample size (n) and confidence level

x = 6

n = 30

confidence_level = 0.95

​

# Calculate the point estimate, alpha, the critical z-value, the standrad error, and the margin of error

point_estimate = x/n

alpha = (1-confidence_level)

critical_z = stats.norm.ppf(1-alpha/2)

standard_error = math.sqrt((point_estimate*(1-point_estimate)/n))

margin_of_error = critical_z * standard_error

​

# Calculate the lower and upper bound of the confidence interval

lower_bound = point_estimate - margin_of_error

upper_bound = point_estimate + margin_of_error

​

# Print the results

print("Point Estimate: {:.3f}".format(point_estimate))

print("Critical Z-value: {:.3f}".format(critical_z))

print("Margin of Error: {:.3f}".format(margin_of_error))

print("Confidence Interval: [{:.3f},{:.3f}]".format(lower_bound,upper_bound))

print("The {:.1%} confidence interval for the population proportion is:".format(confidence_level))

print("between {:.3f} and {:.3f}".format(lower_bound,upper_bound))