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# Stats with Python: Simple Linear Regression

March 22, 2021  |  6 min read  |  87 views

We’ve seen several aspects of the correlation coefficient in the previous posts. The correlation coefficient treats two variables equally; they are symmetrical. When two variables are not symmetrical, that is, when you want to explain $y$ by $x$, correlation analysis alone is not sufficient. Instead, you might want to conduct a regression analysis.

The simplest approach, simple linear regression, considers a single explanatory variable (independent variable) $x$ for explaining the objective variable (dependent variable) $y$.

$y = \beta_0+\beta_1x$

## Least Square Estimates

How to determine the parameters $\beta_0$ and $\beta_1$ in the above equation? Given the paired data $\{(x_i,y_i)\}_{i=1}^n$, they are determined by the method of least squares. That is, they are chosen to minimize the sum of the squared error between the predicted $\hat{y}_i = b_0+b_1x_i$ and the actual $y_i$:

$\mathcal{L}(b_0,b_1) \coloneqq \sum_{i=1}^n \{ y_i - (b_0+b_1x_i) \}^2.$

Therefore, the least squares estimates are:

$\begin{gathered} \hat{\beta}_1 = \frac{S_{xy}}{S_{x}^2},\\ \hat{\beta}_0 = \bar{y}-\beta_1\bar{x}. \end{gathered}$

, where

$\begin{gathered} S_{xy} \coloneqq \frac{1}{n}\sum_{i = 1}^n (x_i - \overline{x}) (y_i - \overline{y}),\\ S_{x}^2 \coloneqq \frac{1}{n}\sum_{i = 1}^n (x_i - \overline{x})^2,\\ S_{y}^2 \coloneqq \frac{1}{n}\sum_{i = 1}^n (y_i - \overline{y})^2. \end{gathered}$

### Proof

$\mathcal{L}(b_0,b_1) = \sum_{i=1}^n \{ (y_i-\bar{y}) + (\bar{y}-b_0-b_1\bar{x}) -b_1(x_i-\bar{x})\}^2$

Considering $\sum_{i=1}^n(x_i-\bar{x})=0$ and $\sum_{i=1}^n(y_i-\bar{y})=0$,

\begin{aligned} \mathcal{L}(b_0,b_1) &= \sum_{i=1}^n(y_i-\bar{y})^2 + n(\bar{y}-b_0-b_1\bar{x})^2 \\ & \quad + b_1^2\sum_{i=1}^n(x_i-\bar{x})^2 - 2b_1\sum_{i=1}^n(x_i-\bar{x})(y_i-\bar{y})\\ &= nS_{y}^2 + n(\bar{y}-b_0-b_1\bar{x})^2 + nb_1^2S_{x}^2 - 2nb_1S_{xy}\\ &= nS_x^2\Bigl(b_1 - \frac{S_{xy}}{S_x^2}\Bigr)^2 + n(\bar{y}-b_0-b_1\bar{x})^2 + n\Bigl(S_y^2 - \frac{S_{xy}^2}{S_x^2}\Bigr) \end{aligned}

From the above calculation, $\mathcal{L}(b_0,b_1)$ takes its minimum value when $b_1=S_{xy}/S_x^2$ and $b_0 = \bar{y}-b_1\bar{x}$.

## Coefficient of Determination

Now we have the predicted values $\{\hat{y}_i\}_{i=1}^n$. How good are these predictions? To evaluate the goodness, coefficient of determination $R^2$ is frequently used.

$R^2 \coloneqq \frac{ESS}{TSS} = \frac{ \sum_{i=1}^n (\hat{y_i}-\bar{y})^2}{\sum_{i=1}^n (y_i-\bar{y})^2}.$

The coefficient of determination is the ratio of ESS (explained sum of squares) to TSS (total sum of squares). As you may imagine from its notation, the coefficient of determination $R^2$ is the square of the Pearson correlation coefficient $r$.

### Proof

Using the equation: $\hat{y}_i - \bar{y} = \beta_1(x_i - \bar{x})$,

\begin{aligned} R^2 &= \frac{ \sum_{i=1}^n (\beta_1(x_i - \bar{x}))^2}{\sum_{i=1}^n (y_i-\bar{y})^2}\\ &= \frac{n\beta_1^2 S_x^2}{nS_y^2}\\ &= \frac{S_{xy}^2}{S_{x}^4}\frac{S_x^2}{S_y^2}\\ &= \Bigl( \frac{S_{xy}}{S_xS_y} \Bigr)^2\\ &= r^2. \end{aligned}

## Experiment

Lastly, let’s confirm that $R^2=r^2$, introducing how to use linear regression with Python. As done in the previous post, I generated 100 pairs of correlated random samples (x and y).

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style("darkgrid")

n = 100
x = np.random.rand(n)
y = x + 0.5*np.random.rand(n)

Scikit-learn implements linear regression as LinearRegression and coefficient of determination as r2_score. After fitting the model, we can plot the regression line like below:

from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

model = LinearRegression()
model.fit(x.reshape(-1, 1), y.reshape(-1, 1))

sns.scatterplot(x, y)
plt.plot(x, model.predict(x.reshape(-1, 1)), color="k")

As expected, $R^2=r^2$ is confirmed.

r2_score(y, model.predict(x.reshape(-1, 1)))
# >> 0.7922606713476185

np.corrcoef(x, y)[0,1]**2
# >> 0.7922606713476184

## References

[1] 倉田 博史, 星野 崇宏. ”入門統計解析“（第3章）. 新世社. 2009.

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