【Jupyter】练习题

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+ϵy=β0+β1x+ϵ (hint: use statsmodels and look at the Statsmodels notebook)

OUTPUT

                 

Code

print( 'The mean of x is : ', end="")
print(anascombe['x'].mean())
print( 'The mean of y is : ', end="")
print(anascombe['y'].mean())
print( 'The variance of x is : ', end="")
print(anascombe['x'].var())
print( 'The variance of x is : ', end="")
print(anascombe['y'].var())

print("The correlation coefficient between x and y: ", end="") 
print((np.corrcoef(np.array([anascombe['x'], anascombe['y']])))[0][1]) 

n = len(anascombe)  
is_train = np.random.rand(n) < 0.7  
train = anascombe[is_train].reset_index(drop=True)  
test = anascombe[~is_train].reset_index(drop=True)  
lin_model = smf.ols('y ~ x', train).fit()  
lin_model.summary()

Part 2

        Using Seaborn, visualize all four datasets.

OUTPUT

Code

# your code here
m = sns.FacetGrid(anascombe, col="dataset")  
m.map(plt.scatter, "x","y")