06 Introduction to residuals and least-squares regression#

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import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats

khanacademy

Introduction to residuals and least-squares regression fig 1

x = [35, 40, 45, 50, 55, 55, 60, 60, 70, 70]
y = [45, 100, 45, 100, 125, 175, 125, 165, 165, 200 ]

plt.scatter(x, y)
plt.xlabel('Height (inches)')
plt.ylabel('Weight')
plt.show()
../_images/06 Introduction to residuals and least-squares regression_6_0.png 
sns.scatterplot(x,
                y, 
                x_bins=[0, 10, 20, 30, 40, 50, 60, 70, 80],
                y_bins=[0, 50, 100, 150, 200, 250, 300])
plt.xlabel('Height (inches)')
plt.ylabel('Weight')
plt.show()

/opt/hostedtoolcache/Python/3.9.13/x64/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
../_images/06 Introduction to residuals and least-squares regression_7_1.png