How can I visualize the results of a linear regression?

How can I visualize the results of a linear regression?

You can visualize linear regression results using scatter plots with a regression line, residual plots, or using libraries like Matplotlib or Seaborn in Python.

Can you show me an example using Python?

Sure! Here's a simple example:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Sample data
X = np.array([[1], [2], [3], [4], [5]])
Y = np.array([1, 2, 3, 4, 5])

# Create a linear regression model
model = LinearRegression()
model.fit(X, Y)

# Predict values
predictions = model.predict(X)

# Plotting
plt.scatter(X, Y, color='blue')
plt.plot(X, predictions, color='red')
plt.title('Linear Regression Result')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

What do the residual plots tell us?

Residual plots help us assess the goodness of fit of the model. If the residuals are randomly scattered, it indicates a good fit. Patterns in the residuals may suggest that the model is not capturing some aspect of the data.

Are there any other visualization techniques I should know about?

Yes! Besides scatter plots and residual plots, you can also use heatmaps for correlation matrices, box plots for outliers, and pair plots to visualize relationships between multiple variables.