We'll create a model that predicts crop yields for apples and oranges (target variables) by looking at the average temperature, rainfall, and humidity (input variables or features) in a region. Here's the training data:

In a linear regression model, each target variable is estimated to be a weighted sum of the input variables, offset by some constant, known as a bias :

yield_appple = (someWeight)w11 * temp + w12 * rainfall + w13 * humidity + b1
b1 is the bias

yield_orange = (someWeight)w21 * temp + w22 * rainfall + w23 * humidity + b2 b2 is the bias