best fit

Lines of best fit

there are a few different methods. Lets take a look at them.

numpy polyfit

This is probably the simplest to implement.

import numpy as np

# Create some sample data

x = [1, 2, 3, 4, 5]

y = [2, 3, 4, 5, 6]

# Compute the line of best fit

coefficients = np.polyfit(x, y, 1)

# Print the coefficients

print(coefficients)

This will output a NumPy array of coefficients that represent the line of best fit. The first element of the array is the y-intercept and the second element is the slope of the line.

sklearn.linear_model LinearRegression

One can use the LinearRegression class from scikit-learn to compute the line of best fit.

from sklearn.linear_model import LinearRegression

# Create some sample data

x = [1, 2, 3, 4, 5]

y = [2, 3, 4, 5, 6]

# Convert lists to 2D arrays

x = [[i] for i in x]

y = [[i] for i in y]

# Create a linear regression model

model = LinearRegression()

# Fit the model to the data

model.fit(x, y)

# Print the coefficients

print(model.intercept_)

print(model.coef_)

This will output the y-intercept and the slope of the line.

statsmodels.api

One can use the statsmodels.regression.linear_model.OLS (Ordinary Least Squares) class to perform linear regression and get the intercept and slope.

import statsmodels.api as sm

# Create some sample data

x = [1, 2, 3, 4, 5]

y = [2, 3, 4, 5, 6]

# Add a column of 1s to the x data to include the intercept term

x = sm.add_constant(x)

# Create an OLS model

model = sm.OLS(y, x)

# Fit the model

results = model.fit()

# Print the coefficients

print(results.params)

This will output an array of coefficients that represent the line of best fit. The first element of the array is the y-intercept and the second element is the slope of the line.