Basics of Statistics

from scipy import stats
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style


plt.style.use('seaborn')

Type of data

Continuous

Any value within an interval is acceptable. like float, interval

cv = np.random.random_sample(10) # continuous values
cv

array([0.66031092, 0.65617515, 0.03930539, 0.09361998, 0.85361171,
       0.69670654, 0.91267306, 0.59063556, 0.48679222, 0.64625406])

Discrete

Only integers are acceptable. countables.

dv = np.random.randint(0,10,10) # discrete values
dv

array([6, 8, 2, 2, 3, 9, 5, 8, 5, 6])

Categorical

Ordinal

Categorical data with explicit ordering. Like encoded in a series

np.array([1, 2, 3, 4, 5])

array([1, 2, 3, 4, 5])

Nominal

specific set of values from a category. there is no ordering, counting.

np.array([
    'apple',
    'orange',
    'grapes'
])

array(['apple', 'orange', 'grapes'], dtype='<U6')

Binary

only two possible categories, like a switch.

np.array([True, False])

array([ True, False])

Location Estimates

mean(average)

\(\mu=\frac{1}{m}\sum_{i=1}^{m}X^{(i)}\)

dv,dv.mean()

(array([6, 8, 2, 2, 3, 9, 5, 8, 5, 6]), 5.4)

trimmed mean

sorted values are trimmed p values from edges,to remove extreme values for mean calculation.

\(\mu = \frac{\sum_{i=p+1}^{m-p} X^{(i)}}{m - 2p}\)

p = 2

dv.sort()

dv, dv[p : -p].mean()

(array([2, 2, 3, 5, 5, 6, 6, 8, 8, 9]), 5.5)

weighted mean

mean where weights are associated to the values.

\(\mu_w=\frac{\sum_{i=1}^{m} w^{(i)} X^{(i)}}{\sum_{i=1}^{m}w^{(i)}}\)

x = np.random.random_sample(10)
w = np.random.randint(1,4,10)

x, w, (x * w).sum() / w.sum()

(array([0.47831889, 0.15173979, 0.65283203, 0.49839894, 0.12887122,
        0.40593656, 0.46486771, 0.03209976, 0.96926736, 0.72582018]),
 array([1, 2, 2, 3, 1, 3, 1, 1, 1, 2]),
 0.4616009083013748)

median

the value such that one-half of the data lies above and below. (like 50th percentile)

why do we need median? It has less sensitive to data but still what value it can give. median only depends on the data that is located in the center of the sorted array.

so lets take an example of comparing two sets of values of incomes of two regions. if one region has an outlier(very very very rich people). then mean will be a lot different then median. so to compare most common salaries of these two regions, mean will not give best results.

x = np.random.random_sample(10)
x

array([0.34440759, 0.02302378, 0.26151817, 0.29985192, 0.47751307,
       0.60439014, 0.40955753, 0.27753061, 0.46542145, 0.66317304])

sorted(x), np.median(x)

([0.023023778580702925,
  0.26151816856942345,
  0.27753060775839367,
  0.29985191612402406,
  0.3444075862196835,
  0.4095575286433396,
  0.46542144991510237,
  0.4775130708375265,
  0.6043901382260869,
  0.6631730366737562],
 0.37698255743151154)

mode

The most commonly occurring category or value in a data set. It is generally used for categorical dataset and not for numerical dataset.

categorical_data = np.array([
    'apple',
    'orange',
    'mango',
    'orange',
    'orange',
    'orange',
    'mango',
    'mango',
    'mango'
])

from collections import Counter

category_counts = Counter(categorical_data)
category_counts

Counter({'apple': 1, 'orange': 4, 'mango': 4})

plt.bar(category_counts.keys(),category_counts.values())
plt.title("Frequency Table")
plt.xlabel("category")
plt.ylabel("Frequency")

Text(0, 0.5, 'Frequency')

category_counts

Counter({'apple': 1, 'orange': 4, 'mango': 4})

plt.pie(category_counts.values(),labels=category_counts.keys(),autopct='%1.1f%%')
plt.show()

stats.mode(categorical_data)

ModeResult(mode=array(['mango'], dtype='<U6'), count=array([4]))

Variability Estimates

variability or dispersion to signify whether the data values are tightly clustered or spread out.

variance

population variance \(\sigma^2 = \frac{\sum{(x - \mu)^2}}{n}\)

sample variance \(s^2 = \frac{\sum{(x - \bar{x})^2}}{n-1}\)

Alternate formula

\begin{align} \Sigma{(x - \mu)}^2 &= \Sigma{(x^2 - 2 x \mu + \mu^2)}\\ &= \Sigma{x^2} - 2 \mu \Sigma{x} + \Sigma \mu^2\\ \because \Sigma{x} = n\mu\\ &= \Sigma{x^2} - 2n \mu^2 + \mu^2 \Sigma{1}\\ &= \Sigma{x^2} - 2n \mu^2 + n\mu^2\\ \Sigma{(x - \mu)}^2 &= \Sigma{x^2} - n \mu^2\\ \sigma^2 &= \frac{\sum{(x - \mu)^2}}{n} = \frac{\Sigma{x^2}}{n} - \mu^2 \end{align}

average of squared difference of each datapoint from dataset’s mean

sorted(dv), dv.var(), np.square(dv - dv.mean()).sum() / dv.shape[0] #ddof = 0

([2, 2, 3, 5, 5, 6, 6, 8, 8, 9], 5.640000000000001, 5.640000000000001)

dv.var(ddof=1), np.square(dv - dv.mean()).sum() / (dv.shape[0] - 1) #ddof = 1

(6.2666666666666675, 6.2666666666666675)

degree of freedom(dof)

degree of freedom describes freedomto vary. The number of observations in data that are free to vary while estimating statistical parameters.

eg:- if there are 10 values and sum of all values = n * avg

like avg = 3.5 and sum of all values = 35

so with possibility that first value could vary and so on 9 values are free to vary but the 10th value is not free to vary because the sum should be 35.

10th value should be a specific number.

degree of freedom = number of observation - number of required relations among the observations

In variance denominator is n-1 not n. But why though? generally n is too large so n or n-1 doesn’t create much difference.

but if we use n as denominator in variance formula, we are underestimating the true value of variance and std deviation in the population. Called biased estimate

and if we use n-1 as denominator then standard deviation becomes an unbiased estimate

deviation

difference between observed value and estimate of location.

standard deviation

standard deviation is easier to explain as it is on the same scale as the original data.

population standard deviation \(\sigma = \sqrt \frac{\sum{(x - \mu)^2}}{n}\)

sample standard deviation \(s = \sqrt \frac{\sum{(x - \bar{x})^2}}{n-1}\)

## sqrt of variance
dv, dv.std(ddof=0) # ddof = 0

(array([2, 2, 3, 5, 5, 6, 6, 8, 8, 9]), 2.3748684174075834)

dv, dv.std(ddof=1) # ddof = 1

(array([2, 2, 3, 5, 5, 6, 6, 8, 8, 9]), 2.503331114069145)

mean absolute deviation (MAD)

\(\text{mean absolute deviation} = \frac{\sum{|x - \bar{x}|}}{n}\)

like manhattan norm, l1-norm

x = np.random.random_sample(10)
x

array([0.75856087, 0.27304572, 0.34091385, 0.65378246, 0.60178779,
       0.61511515, 0.06909667, 0.62935881, 0.59193468, 0.92534561])

x, np.abs(x - x.mean()).sum() / x.shape[0]

(array([0.75856087, 0.27304572, 0.34091385, 0.65378246, 0.60178779,
        0.61511515, 0.06909667, 0.62935881, 0.59193468, 0.92534561]),
 0.19092524774625713)

range (max - min)

dv, np.ptp(dv)

(array([2, 2, 3, 5, 5, 6, 6, 8, 8, 9]), 7)

percentile

\(Percentile = \frac{rank of value(r_x)}{total values(N)} * 100\)

\(r_x = \frac{Percentile}{100} * {N}\)

## a value ,below which lies given the percentage of data points
dv,np.percentile(dv,100, method='lower')

(array([2, 2, 3, 5, 5, 6, 6, 8, 8, 9]), 9)

Quartiles

dv, np.percentile(dv,[25,50,75], method='lower')

(array([2, 2, 3, 5, 5, 6, 6, 8, 8, 9]), array([3, 5, 6]))

Inter Quartile Range (IQR)

## difference between third quartile Q3 and first quartile Q1

dv, stats.iqr(dv,rng=(25,75), interpolation="lower")

(array([2, 2, 3, 5, 5, 6, 6, 8, 8, 9]), 3)

skewness

• majority of the data present on side of the distribution

right skewed	positive value
left skewed	negative value
unskewed	zero

unskewed

np.random.seed(0)

ucv = stats.norm(0,1).rvs(10000)

ucv.sort()
pdf = stats.norm.pdf(ucv)

plt.axvline(ucv.mean(),color='k')
plt.plot(ucv,pdf)
plt.hist(ucv,bins=20,density=True,alpha=0.7)
plt.boxplot(ucv,vert=False)
plt.show()

## skewness
stats.skew(ucv)

0.026634616738395525

right skewed

scv = stats.skewnorm.rvs(4,size=10000)

plt.axvline(scv.mean(),color='k')
plt.hist(scv,bins=20,density=True,alpha=0.7)
plt.boxplot(scv,vert=False)
plt.show()

## skewness
stats.skew(scv)

0.8179623055636133

left skewed

scv = stats.skewnorm.rvs(-4,size=10000)

plt.axvline(scv.mean(),color='k')
plt.hist(scv,bins=20,density=True,alpha=0.7)
plt.boxplot(scv,vert=False)
plt.show()

## skewness
stats.skew(scv)

-0.8325460520764684

Kurtosis

• indicates how much of data is concentrated around mean or shape of the probability distribution.

• default Ficsher definition

• can be changed to pearson

ucv = stats.norm(0,1).rvs(10000)

plt.axvline(ucv.mean(),color='k')
plt.hist(ucv,bins=20,density=True,alpha=0.7)
plt.boxplot(ucv,vert=False)
plt.show()

## kurtosis
stats.kurtosis(ucv)

0.016226092494214583

Multi Variate Analysis

from sklearn.datasets import make_regression

X, y = make_regression(n_samples=1000,n_features=3)

X.shape, y.shape

((1000, 3), (1000,))

hexbin

plt.hexbin(X[...,0], y, gridsize = 20, cmap='Greens')
plt.grid()

boxplot

plt.boxplot(X)
plt.show()

violinplot

plt.violinplot(X,showmeans=True,showextrema=True,showmedians=True)
plt.show()