def margin_error(data, sample_size):
zscore = zScore(data)
standardDeviation = standard_deviation(data)
denominator = root(sample_size, 2)
willMultiply = division(standardDeviation, denominator)
marginOfError = product(zscore, willMultiply)
return marginOfError
def samplesizeKnownPop(data, confidence, error):
z = division(confidence, 2)
ztable_value = normalProbabilityDensity(z)
standardDeviation = standard_deviation(data)
ztableTimesStandard = product(ztable_value, standardDeviation)
nextStep = division(ztableTimesStandard, error)
sample_size = power(nextStep, 2)
return sample_size
def skewness(data):
theMode = mode(data)
meanMinusMode = mean(data) - theMode[0]
standardDeviation = standard_deviation(data)
skewnessCoefficient = division(meanMinusMode, standardDeviation)
return skewnessCoefficient