What is a weighted mean? Probability-weighted mean
A weighted mean is a kind of average. Instead of each data point contributing equally to the final mean, some data points contribute more “weight” than others. If all the weights are equal, then the weighted mean equals the arithmetic mean (the regular “average” you’re used to). Weighted means are very common in statistics, especially when studying populations.
Use in insurance industry
Basically the probability-weighted mean is used to calculate a best estimate of incurred but not reported claims and future claims expected to be incurred within the contract boundary. Based on a set of random variables, the experience of the policy/portfolio/company is projected, and the outcome is noted. Then this is done again with a new set of random variables. In fact, this process is repeated thousands of times.
The Arithmetic Mean
When you find a mean for a set of numbers, all the numbers carry an equal weight. For example, if you want to find the arithmetic mean of 1, 3, 5, 7, and 10 (i.e. a set of 5 numbers):
- Add up your data points: 1 + 3 + 5 + 7 + 10 = 26. Probability-weighted mean
- Divide by the number of items in the set: 26 / 5 = 5.2. Probability-weighted mean
What do we mean by “equal weight”? The first sentence in some tests is sometimes “All questions carry an equal weight”). It’s saying that all the questions in the exam are worth the same number of points. If you have a 100 point exam and 10 questions, each question is worth 1/10th of the points. In the above question, you have of a set of five numbers. You can think of each number contributing 1/5 to the total mean (as there are 5 numbers in the set). Probability-weighted mean
The Weighted Mean
In some cases, you might want a number to have more weight. In that case, you’ll want to find the weighted mean. To find the weighted mean: Probability-weighted mean
- Multiply the numbers in your data set by the weights. Probability-weighted mean
- Add the results up. Probability-weighted mean
For that set of number above with equal weights (1/5 for each number), the math to find the weighted mean would be:
1(*1/5) + 3(*1/5) + 5(*1/5) + 7(*1/5) + 10(*1/5) = 5.2. Probability-weighted mean
Sample problem: You take three 100-point exams in your statistics class and score 80, 80 and 95. The last exam is much easier than the first two, so your professor has given it less weight. The weights for the three exams are:
Exam 1: 40 % of your grade. (Note: 40% as a decimal is .4.)
Exam 2: 40 % of your grade. Probability-weighted mean
Exam 3: 20 % of your grade. Probability-weighted mean
What is your final weighted average for the class?
Multiply the numbers in your data set by the weights:
.4(80) = 32 Probability-weighted mean
.4(80) = 32 Probability-weighted mean
.2(95) = 19 Probability-weighted mean
Add the numbers up. 32 + 32 + 19 = 83.
The percent weight given to each exam is called a weighting factor.
Weighted Mean Formula
The weighted mean is relatively easy to find. But in some cases the weights might not add up to 1. In those cases, you’ll need to use the weighted mean formula. The only difference between the formula and the steps above is that you divide by the sum of all the weights.
The image above is the technical formula for the weighted mean. In simple terms, the formula can be written as:
Weighted mean = Σ w x / Σ w
Σ = the sum of (in other words…add them up!).
w = the weights.
x = the value.
To use the formula:
- Multiply the numbers in your data set by the weights.
- Add the numbers in Step 1 up. Set this number aside for a moment.
- Add up all of the weights.
- Divide the numbers you found in Step 2 by the number you found in Step 3.
In the sample grades problem above, all of the weights add up to 1 (.4 + .4 + .2) so you would divide your answer (83) by 1:
83 / 1 = 83.
However, let’s say your weighted means added up to 1.2 instead of 1. You’d divide 83 by 1.2 to get:
83 / 1.2 = 69.17.
Warning: The weighted mean can be easily influenced by outliers in your data. If you have very high or very low values in your data set, the weighted mean may not be a good statistic to rely on.
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