def sampleCorrelation(dataX, dataY):
#dataX= []
#dataY = []
meanX = mean(dataX)
meanY = mean(dataY)
deviationX = standard_deviation(dataX)
deviationY = standard_deviation(dataY)
rNumerator = 0.0
for i in range(len(dataX)):
rNumerator += product(subtraction(dataX[i], meanX),
subtraction(dataY[i], meanY))
rDenominator = product(deviationX, deviationY)
r = division(rNumerator, rDenominator)
return r
def zScore(data):
x = random.choice(data)
meanData = mean(data)
standardDeviation = standard_deviation(data)
numerator = subtraction(x, meanData)
z = division(numerator, standardDeviation)
return z
def sampleSize(data, proportion):
denominator = power(margin_error(data, sample_size=10), 2)
q = subtraction(1.0, proportion)
half_numerator = product(proportion, q)
z = normalProbabilityDensity(denominator)
numerator = product(power(z, 2), half_numerator)
n = division(numerator, denominator)
return n
def sample_size_unknown(percent, confidence, width):
confidence_int = division(confidence, 2)
zscore = normalProbabilityDensity(confidence_int)
error = division(width, 2)
p = subtraction(1, percent)
pTimesq = product(percent, p)
zDivideError = division(zscore, error)
powerZdivError = power(zDivideError, 2)
return product(pTimesq, powerZdivError)
def meanDeviation(data):
global summation
num_values = len(data)
xbar = mean(data)
willSquare = []
squared = []
for items in data:
willSquare.append(abs(subtraction(items, xbar)))
for values in willSquare:
squared.append(power(values, 2))
summation = sum(squared)
return division(summation, num_values)