Weighted Average Calculator
Enter values and their weights to calculate the weighted mean. The full sum(weight × value) ÷ sum(weight) working is written out with your numbers, so you can copy it into homework or check a spreadsheet.
Values and Weights
Row 1
Row 2
Row 3
87.3333
Weighted average
60
Sum of weights
2
Rows counted
The working, step by step
Σ(w × x) = 20 × 92 + 40 × 85 = 1840 + 3400 = 5240
Σw = 20 + 40 = 60
weighted average = 5240 ÷ 60 = 87.3333
Grades vs. Statistics
For grades, the values are category scores and the weights come from your syllabus (homework 20%, exams 40%, and so on). For statistics, the weights are usually frequencies or sample sizes — for example, averaging class results where one section has 30 students and another has 12. Either way the formula is identical, and the weights do not need to sum to 100 because the division by Σw normalizes them.
Frequently Asked Questions
- How is a weighted average calculated?
- Multiply each value by its weight, add those products together, and divide by the sum of the weights: weighted average = Σ (weight × value) ÷ Σ(weight). This tool writes out every step with your numbers.
- Do my weights need to add up to 100 or 1?
- No. Because the formula divides by the sum of the weights, any consistent set of weights works — percentages, credit hours, counts, or decimals all give the same result.
- When should I use a weighted average instead of a regular average?
- Use a weighted average when some values matter more than others — course grades with category weights, GPA with credit hours, or survey results with different group sizes. A regular average treats every value equally.
- How do I use this for course grades?
- Enter each category score as the value and its syllabus weight as the weight — for example, homework 92 with weight 20 and exams 85 with weight 40. The result is your overall course percentage.
- Can weights be zero or negative?
- A zero weight simply removes that value from the average. Negative weights are not allowed here because they rarely make sense outside specialized statistics and can produce misleading results.