Weighted Moving Average Calculator
Calculates a weighted average of the supplied recent observations. The worked condition keeps the method and source values visible for an independent check.
Describe the observed sequence when the result is reused
Weighted moving average
The design boundary at the chosen parameters
Construct a second plausible scenario that changes one uncertain input while keeping the rest coherent. Compare the statistic and practical interpretation across both cases. The page-specific quantity is weighted moving average.
If a small defensible change reverses the conclusion, report the sensitivity rather than hiding it behind one preferred scenario. The chosen weighted moving average convention remains attached to the source record. Before reusing this result, write down the observed scale, model boundary, and convention behind the displayed value. That record separates a changed dataset from a changed definition and gives the next analyst a clear route back to the original calculation.
Where the method applies before comparing methods
Calculates a weighted average of the supplied recent observations. The displayed relationship is sum(weights×values)/sum(weights), and each symbol is tied to a labeled field. The page-specific quantity is weighted moving average.
Values 12,15,18 with weights 1,2,3 give a weighted average of 16. This example is a numerical check, not proof that the model describes every dataset. The chosen weighted moving average convention remains attached to the source record. Before reusing this result, write down the observed scale, model boundary, and convention behind the displayed value. That record separates a changed dataset from a changed definition and gives the next analyst a clear route back to the original calculation.
Keeping a reproducible record when the result is reused
Weights must align with the values and their sum must be positive; larger recent weights emphasize responsiveness. The page-specific quantity is weighted moving average.
The unit of analysis, time order, sample boundary, and treatment of ties or missing values remain outside the answer unless they are entered. Keep those choices beside this time-series result. The chosen weighted moving average convention remains attached to the source record.
What the statistic can support in the worked condition
Check scales and domains before evaluating weighted moving average. Counts, probabilities, rates, windows, and squared units are not interchangeable merely because a field accepts a number.
If one input changes, predict the direction of the result from the formula first. That catches reversed groups, invalid windows, and parameterization errors. The chosen weighted moving average convention remains attached to the source record.
For another time-series question, compare simple moving average and exponential smoothing.
The reference quantity before reporting
Recalculate one intermediate quantity from sum(weights×values)/sum(weights) and work back from the displayed answer. The source values should be enough for another analyst to reproduce weighted moving average.
Use a boundary case when possible: equal values, a probability near zero, a window of two, or a rate of zero. Expected limiting behavior is often more informative than another decimal place. The chosen weighted moving average convention remains attached to the source record.
Evidence beside the calculation under the stated model in this example
The number answers one statistical question. It does not establish causation, model fit, representativeness, or a useful decision threshold by itself. The page-specific quantity is weighted moving average.
Practical meaning depends on the measurement scale and consequences. State the comparison or benchmark before presenting weighted moving average as evidence.
Before reporting this result in this example
Before reporting, what should be checked before reusing this result for this calculation?
Keep the inputs, units, method name, exclusions, and unrounded output together. The reported quantity here is weighted moving average.
Under the stated model, why might another program return a different number before reporting?
Parameterization, interpolation, tie rules, window placement, and rounding can differ. The reported quantity here is weighted moving average.
When software results differ, when should the calculation be repeated before reporting?
Repeat it when an input, sample boundary, time window, or model assumption changes. The reported quantity here is weighted moving average.