jupyter notebook

2018-06-10

Part 1

For each of the four datasets…

Compute the mean and variance of both x and y
Compute the correlation coefficient between x and y
Compute the linear regression line: y=β0+β1x+ϵ (hint: use statsmodels and look at the Statsmodels notebook)

%matplotlib inline

import random

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import statsmodels.api as sm
import statsmodels.formula.api as smf

sns.set_context("talk")

#part 1

anascombe = pd.read_csv('D:/Notebook/anscombe.csv')
print(anascombe)
print()
l=0
r=11
for i in range(4):
    xi = anascombe.x[l:r].values
    yi = anascombe.y[l:r].values
    meanxi = np.mean(xi)
    meanyi = np.mean(yi)
    varxi = np.var(xi)
    varyi = np.var(yi)
    coefxy = np.corrcoef(xi,yi)[0][1]
    tmp = sm.add_constant(xi)
    model = sm.OLS(yi,tmp)
    rst = model.fit()
    params = rst.params
    print('mean_x'+str(i+1),': ',meanxi)
    print('mean_y'+str(i+1),': ',meanyi)
    print('varx'+str(i+1),': ',varxi)
    print('vary'+str(i+1),': ',varyi)
    print('coef_xy: ',coefxy)
    print('线性回归为 y=',params[0],'+',params[1],'*x')
    print()
    l += 11
    r += 11

Part 2
Using Seaborn, visualize all four datasets.

hint: use sns.FacetGrid combined with plt.scatter

#part 2
sns.set(style='whitegrid')   #数据可视化
g = sns.FacetGrid(anascombe,col='dataset',hue='dataset',size=3)
g.map(plt.scatter,'x','y')
plt.show()