Linear regression method helps us understand the relationship between variables. The independent variables are also called as predictor variables. So when we use two or more predictor variables we call it multiple linear regression. It extends simple linear regression by considering multiple predictors, allowing us to model complex relationships and make predictions based on the combined influence of these variables.
Correlation tells us if the two predictor things are related to each other. If they go up together, it’s a positive correlation. If one goes up while the other goes down, it’s negative. Strong correlations mean a tight connection, while weak ones mean they’re not closely linked.
Sometimes, the relationship between variables isn’t a straight line; it’s more like a curve. The quadratic model helps us deal with these curvy relationships, like when things go up and then down or the other way around. It’s like using a flexible ruler instead of a rigid one.
In summary, linear regression with two predictors helps us make predictions based on multiple factors. Understanding the correlation between predictors helps us see how they’re connected, and the quadratic model allows us to handle more complex relationships in the data. These tools are valuable for making sense of real-world data.