Multiple regression is a way to find the relationship between more than two things. For example, you might want to know how the size of a toy car, its weight, and its material affect how fast it can go. You could measure the sizes, weights, materials, and speeds of a bunch of toy cars, and then use multiple regression to find a line that best fits the data.
Multiple regression is similar to linear regression, but instead of just one explanatory variable, it has multiple explanatory variables. This means that it can take into account multiple factors that might affect the outcome variable.
Here is a simple analogy that you can use to explain multiple regression:
Imagine you have a recipe for a cake. The recipe tells you how much flour, sugar, eggs, and milk you need to use. But what if you want to know how the amount of each ingredient affects the taste of the cake?
You could use multiple regression to find out. You could make a bunch of cakes using different amounts of flour, sugar, eggs, and milk. Then you could ask people to taste the cakes and rate them. You could use the data from your experiment to find a line that best fits the data. This line would show you how the amount of each ingredient affects the taste of the cake.
Multiple regression is a powerful tool that can be used to find relationships between all sorts of things. For example, it can be used to predict how well a student will do on a test based on their grades in class, their study habits, and their sleep quality. Or it can be used to predict how many sales a company will make based on their advertising budget, their product price, and the quality of their customer service.