Polynomial regression is a more advanced type of linear regression. It’s used to find relationships between two things that are not perfectly linear. For example, you might want to know how the height of a child affects their weight. The relationship between height and weight is not perfectly linear, because taller children are not always heavier children. However, polynomial regression can be used to find a curve that best fits the data.
To explain polynomial regression, you could use the following analogy:
Imagine you have a garden hose. If you turn on the water and hold the hose straight, the water will come out in a straight line. This is like linear regression.
But if you bend the hose, the water will come out in a curve. This is like polynomial regression.
Polynomial regression can be used to find relationships between all sorts of things that are not perfectly linear. For example, it can be used to predict how the sales of a product will change as the price of the product changes, or how the performance of a car will change as the speed of the car increases.
Here is a simpler explanation:
Polynomial regression is a way to find the relationship between two things that are not perfectly linear. It’s like bending a garden hose to make the water come out in a curve.