The most powerful free weighted average calculator for statistical analysis. Compute weighted means, weighted variance, weighted standard deviation, and coefficient of variation with live interactive charts and real-time formula breakdowns.
Enter data to compute weighted mean, variance & standard deviation.
A weighted average in statistics assigns different importance to each data point based on its frequency, reliability, or significance. For example, if a measurement of 25°C occurs 50 times and 30°C occurs 10 times, the weighted mean is 25.83°C — not the simple average of 27.5°C. This statistics calculator automates the entire process including variance and standard deviation.
Simple averaging treats all observations equally regardless of how often they occur or how reliable they are. If 90% of your samples measure 10.2 and 10% measure 15.8, the simple average is 13.0 — but the weighted mean is 10.76. The weighted average calculator for statistics prevents this analytical error.
Type each observation or measurement into the 'Value' column — test scores, survey ratings, lab readings, or any numerical data you want to analyze statistically.
Enter the weight for each value. In statistics, weights are typically observation frequencies, sample sizes, inverse variances, or reliability scores. The calculator accepts any positive numbers.
The statistics weighted average calculator computes your result instantly. See the weighted mean, weighted variance, standard deviation, coefficient of variation, and all interactive charts update in real time.
The statistical weighted average formula: multiply each observation by its frequency, sum those products, then divide by the total frequency. This produces the true weighted mean — the foundation for computing weighted variance and standard deviation.
Enter each observation alongside its frequency. For example: Score 85 (40 students), Score 72 (35 students), and Score 95 (25 students).
The calculator multiplies each value by its weight. Score 85: 85 × 40 = 3,400, Score 72: 72 × 35 = 2,520, Score 95: 95 × 25 = 2,375.
3,400 + 2,520 + 2,375 = 8,295. This is the numerator in the weighted mean formula.
Sum the frequencies: 40 + 35 + 25 = 100. This is the denominator.
8,295 ÷ 100 = 82.95. The calculator shows this instantly — the true weighted mean, not 84.00 (simple average).
Averaging data values without weighting by frequency produces misleading central tendency. 90% of samples at 10 and 10% at 50 is not 30. The statistics calculator weights correctly.
Dividing total by category count instead of total frequency is wrong. The weighted average statistics calculator always divides by the sum of all frequencies.
Swapping the data value with its frequency count produces a completely wrong weighted mean. The calculator's labeled columns prevent this mix-up.
Multiply each value by its frequency. Sum those products. Sum all frequencies. Divide. The statistics weighted average calculator automates this entire workflow and adds variance and standard deviation.
Use the statistics weighted average calculator to compute the true mean score. Edit the scores and student counts below to see the result update instantly.
The weighted mean score is 82.95, not 84.00 (simple average). Score A (85) dominates because it has the highest frequency (40 students). The statistics calculator shows how observation counts determine the true central tendency.
Calculate the weighted average rating from a survey. Edit the ratings and response counts below.
The weighted average rating is 3.85 out of 5, reflecting the distribution of survey responses. 'Good' has the most responses and thus the largest influence on the weighted result.
Calculate the weighted mean of frequency distributions where each class midpoint is weighted by its frequency count.
Compute weighted average ratings from surveys where response categories have different numbers of respondents.
Calculate weighted means of laboratory measurements where weights represent measurement precision or sample sizes.
Combine results from multiple studies using weighted averages where weights are inverse variances or sample sizes.
Frequencies must be positive. The statistics calculator requires positive weights (frequencies or precision scores). A weight of zero means the observation is excluded from the statistical analysis entirely.
Equal frequencies = simple average. If every observation has the same frequency, the weighted mean equals the simple arithmetic mean — same formula, same result. Unequal frequencies are where weighted statistics shine.
Weighted mean always falls between min and max values. No matter how you distribute frequencies, the weighted mean will always be between the lowest and highest individual data values in your dataset.
Higher-frequency observations dominate the mean. The more frequently an observation occurs, the more the weighted mean is pulled toward that value. This is the fundamental principle behind frequency-weighted statistical analysis.
Fifteen purpose-built weighted average calculators — each tailored to a specific domain with unique inputs, outputs, and interactive visualizations.
Calculate your final grade using weighted assignments, exams, and projects.
Compute your grade point average across multiple courses.
Apply a weighted moving average to time-series data.
Portfolio returns, WACC, and investment-weighted metrics with real-time breakdowns.
Inventory valuation, unit costs, and supplier comparison with quantity weighting.
Blended pay rates, overtime costs, and department salary analysis by headcount.
Weighted durations, delivery estimates, and PERT scheduling by task frequency.
Weighted mean, variance, standard deviation, and coefficient of variation analysis.
Compute the weighted arithmetic mean from data values with different frequencies or importance weights.
Compute composite scores from weighted categories for rubrics, tests, and evaluations with letter grades.
Calculate VWAP, average purchase price, and procurement costs weighted by quantity or volume.
Compute true portfolio returns by weighting each asset's performance by its dollar allocation.
Combine ratings from multiple review sources weighted by review count or credibility.
Compute blended interest rates across loans, savings, and credit lines weighted by balance.
Analyze blended profit margins across products, services, and segments weighted by revenue.
A weighted mean is the average of a dataset where each value is multiplied by a weight (usually its frequency or reliability) before summing and dividing. Unlike a simple mean that treats all values equally, the weighted mean accounts for how often or how reliably each value occurs, producing a more accurate measure of central tendency.
First compute the weighted mean. Then for each value, calculate the squared difference from the weighted mean, multiply by its weight, and sum all these products. Divide by the sum of weights (or sum of weights minus 1 for sample variance). Weighted variance = Σ[wᵢ × (xᵢ - x̄w)²] / Σwᵢ.
Unweighted standard deviation treats all observations equally. Weighted standard deviation accounts for the frequency or importance of each observation. When data points have different frequencies, weighted SD gives a more accurate measure of spread because it reflects the actual distribution of values.
Use weighted averages when: (1) data points have different frequencies, (2) measurements have different precision levels, (3) combining results from studies with different sample sizes, (4) calculating means from grouped frequency tables, or (5) when some observations are inherently more important than others.
The coefficient of variation is the weighted standard deviation divided by the weighted mean, expressed as a percentage (CV% = σw / x̄w × 100). It measures relative variability. A lower CV indicates more consistent data. This calculator computes CV automatically from your weighted data.