How linear regression works

Regressione intermedia con statsmodels in Python

Maarten Van den Broeck

Content Developer at DataCamp

The standard simple linear regression plot

A scatter plot with a linear regression trend line.

Visualizing residuals

The scatter plot with the linear regression trend line, plus line segments from the points to the trend line, representing residuals.

A metric for the best fit

The simplest idea (which doesn't work)

Take the sum of all the residuals.
Some residuals are negative.

The next simplest idea (which does work)

Take the square of each residual, and add up those squares.
This is called the sum of squares.

A detour into numerical optimization

A line plot of a quadratic equation

x = np.arange(-4, 5, 0.1)
y = x ** 2 - x + 10

xy_data = pd.DataFrame({"x": x,
                        "y": y})

sns.lineplot(x="x",
             y="y",
             data=xy_data)

Quadratic function y = x ** 2 - x + 10

Using calculus to solve the equation

$y = x ^ 2 - x + 10$

$\frac{\partial y}{\partial x} = 2 x - 1$

$0 = 2 x - 1$

$x = 0.5$

$y = 0.5 ^ 2 - 0.5 + 10 = 9.75$

Not all equations can be solved like this.
You can let Python figure it out.

Don't worry if this doesn't make sense, you won't need it for the exercises.

The previous quadratic function, now solved to find the minimum

minimize()

from scipy.optimize import minimize

def calc_quadratic(x):
  y = x ** 2 - x + 10
  return y

minimize(fun=calc_quadratic,
         x0=3)

      fun: 9.75
 hess_inv: array([[0.5]])
      jac: array([0.])
  message: 'Optimization terminated successfully.'
     nfev: 6
      nit: 2
     njev: 3
   status: 0
  success: True
        x: array([0.49999998])

A linear regression algorithm

Define a function to calculate the sum of squares metric.

Call minimize() to find coefficients that minimize this function.

def calc_sum_of_squares(coeffs):
  intercept, slope = coeffs
  # More calculation!

minimize(
  fun=calc_sum_of_squares,
  x0=0
)

Let's practice!

Regressione intermedia con statsmodels in Python