python第十四周作业

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=β 0 +β 1 x+ϵ y=β0+β1x+ϵ (hint: use statsmodels and look at the Statsmodels notebook)

代码：

%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")
    anascombe = pd.read_csv('data/anscombe.csv')
    anascombe.head()
    
    print("x mean:", str(anascombe['x'].mean()))
    print("x variance:", str(anascombe['x'].var()))
    print("y mean:", str(anascombe['y'].mean()))
    print('y veriance', str(anascombe['y'].var()))
    print('correlation coefficient:', (np.corrcoef(np.array([anascombe['x'], anascombe['y']])))[0][1])
    
    ac = np.random.rand(len(anascombe)) < 0.5
    smf.ols('y~x', anascombe[ac].reset_index(drop=True)).fit().summary() seaborn as sns
    
    import statsmodels.api as sm
    import statsmodels.formula.api as smf
    
    sns.set_context("talk")
    anascombe = pd.read_csv('data/anscombe.csv')
    anascombe.head()
    
    print("x mean:", str(anascombe['x'].mean()))
    print("x variance:", str(anascombe['x'].var()))
    print("y mean:", str(anascombe['y'].mean()))
    print('y veriance', str(anascombe['y'].var()))
    print('correlation coefficient:', (np.corrcoef(np.array([anascombe['x'], anascombe['y']])))[0][1])
    
    ac = np.random.rand(len(anascombe)) < 0.5
    smf.ols('y~x', anascombe[ac].reset_index(drop=True)).fit().summary()

Part 2¶

Using Seaborn, visualize all four datasets.

hint: use sns.FacetGrid combined with plt.scatter

sns.FacetGrid(anascombe, col="dataset").map(plt.scatter, "x","y")