题目来源：
https://nbviewer.jupyter.org/github/schmit/cme193-ipython-notebooks-lecture/blob/master/Exercises.ipynb
see Note in part 2
(1)Compute the mean and variance of both x and y
print( 'The average of x is {:.2f}'.format(anascombe['x'].mean()))
print( 'The average of y is {:.2f}'.format(anascombe['y'].mean()))
print( 'The variance of x is {:.2f}'.format(anascombe['x'].var()))
print( 'The variance of y is {:.2f}'.format(anascombe['y'].var()))
结果：
（2）Compute the correlation coefficient between x and y
a=np.array([anascombe['x'],anascombe['y']]) b= np.corrcoef(a) print(b[0][1])
结果：
（3）Compute the liner regression line(hint:use statsmodels and look at the Statsmodels notebook)
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()
结果：
part2:Use Seaborn, visualize all four datasets.
Note:额,做到这里才发现有4个数据集......分4个数据集计算各自的数据特征（part 1）用的方法类似，就不倒回去做part1了......
g = sns.FacetGrid(anascombe, col="dataset") g.map(plt.scatter, "x","y")
结果：
