Weighted mean

Hey there kiddo! Today we're going to learn about something called "weighted mean."

So imagine you have a toy box full of different toys - you've got some big ones, some small ones, and some in between. Now, let's say you want to know how heavy your toy box is overall.

To figure that out, you might want to give more importance to the heavier toys in the box, right? That's where weighted mean comes in - it takes into account the importance or "weight" of different values in a set of data.

Now, let's say the big toys in your box are 10 pounds, the medium ones are 5 pounds, and the small ones are 2 pounds. You could add up all the weights and divide by the total number of toys to get the average weight - but then you'd be giving just as much importance to the small toys as you are to the big ones.

So instead, you can use weighted mean. To do this, we give more "weight" to the values that are more important. In our example, we might decide that the big toys are more important than the small ones, so we give them more weight.

We can do this by multiplying each value by its weight, and then adding up all those products and dividing by the total weight. In our toy box example, we might give the big toys a weight of 3, the medium toys a weight of 2, and the small toys a weight of 1.

So, to find the weighted mean, we would multiply 10 (the weight of the big toys) by 3, and get 30. We'd then multiply 5 (the weight of the medium toys) by 2, and get 10. Finally, we'd multiply 2 (the weight of the small toys) by 1, and get 2.

We would then add up all those products (30 + 10 + 2 = 42) and divide by the total weight (3 + 2 + 1 = 6), giving us a weighted mean of 7.

So that's it, kiddo! Weighted mean is just a way to average out data while taking into account the importance of each value.