def variance(data):
varMean = mean(data)
listDiffs = []
for eachNum in data:
eachDiff = subtraction(eachNum, varMean)
listDiffs.append(eachDiff)
listSquares = []
for eachDiff in listDiffs:
eachSquare = squaring(eachDiff)
listSquares.append(eachSquare)
return mean(listSquares)
def variance(num):
Mean_var = mean(num)
Man = []
for n in num:
Variables = subtraction(n, Mean_var)
Man.append(Variables)
Squares = []
for Variables in Man:
Square_var = square(Variables)
Squares.append(Square_var)
return mean(Squares)
def median(num):
num.sort()
length = len(num)
result = None
index1 = int(division(2, length))
if length % 2 == 0:
index2 = int(subtraction(index1, 1))
value1 = num[index1]
value2 = num[index2]
result = mean([value1, value2])
else:
result = num[index1]
return float(result)
def zscore(data):
dataMean = mean(data)
stanDev = standard_deviation(data)
listMinuses = []
for eachRaw in data:
meanMinusRaw = subtraction(dataMean, eachRaw)
listMinuses.append(meanMinusRaw)
listZScores = []
for eachMinus in listMinuses:
eachZ = division(stanDev, eachMinus)
listZScores.append(eachZ)
return listZScores
def zscore(num):
Mean_var = mean(num)
standardDev_var = standardDev(num)
negative = []
for Raw_var in num:
meanRaw = subtraction(Mean_var, Raw_var)
negative.append(meanRaw)
ZScore_var = []
for neg in negative:
z = division(standardDev_var, neg)
ZScore_var.append(z)
return ZScore_var
def confidence_interval(sample):
# finding sample standard deviation
old_sample_size = len(sample)
new_sample_size = subtraction(1, old_sample_size)
sample_mean = mean(sample)
subtract_mean_result = []
for item in sample:
result = subtraction(sample_mean, item)
subtract_mean_result.append(result)
squared_list = []
for num in subtract_mean_result:
squared_result = squaring(num)
squared_list.append(squared_result)
squared_list_total = 0
for num in squared_list:
squared_list_total += num
sample_variance = division(new_sample_size, squared_list_total)
sample_deviation = squarerooting(sample_variance)
# finding confidence interval
confidence_level = .95
t_distribution = 2.262
# taken from t_distribution chart https://www.statisticshowto.com/probability-and-statistics/confidence-interval/#CISample
confidence_minus_one = subtraction(confidence_level, 1)
new_confidence = division(2, confidence_minus_one)
squareroot_of_sample = squarerooting(old_sample_size)
CI = division(squareroot_of_sample, sample_deviation)
interval = multiplication(CI, t_distribution)
lower_end = subtraction(interval, sample_mean)
upper_end = addition(interval, sample_mean)
width = subtraction(lower_end, upper_end)
return [lower_end, upper_end, width]
def mean(self, data):
self.result = mean(data)
return self.result