DS Regression Coefficients

The “Coefficients Part” in Regression Table

“Coef” stands for coefficient, which is the result of the linear regression function.
The linear regression function can be expressed mathematically as:

Calorie_Burnage = 0.3296 * Average_Pulse + 346.8662

This means:

For every 1 increase in Average Pulse, Calorie Burnage increases by 0.3296 (or 0.3 when rounded).
When Average Pulse equals 0, Calorie Burnage is 346.8662 (or 346.9 when rounded).

Remember, the intercept helps fine-tune the model’s prediction accuracy.

Do you think this is a good model?

Define the Linear Regression Function in Python

Define the linear regression function in Python to make predictions.

What will Calorie Burnage be when Average Pulse is: 120, 130, 150, and 180?

Example

def Predict_Calorie_Burnage(Average_Pulse):
return(0.3296*Average_Pulse + 346.8662)

print(Predict_Calorie_Burnage(120))
print(Predict_Calorie_Burnage(130))
print(Predict_Calorie_Burnage(150))
print(Predict_Calorie_Burnage(180))