Definition
Least-squares forecast
A least-squares forecast fits a straight line to a series of data points — minimizing the sum of squared distances from the line — and extrapolates it to project a future value. In delivery forecasting it's the simplest honest projection: fit a trend to the remaining-scope burndown and read off where the line crosses zero to estimate a completion date.
Key takeaways
- A least-squares forecast fits the best straight line to a data series and extrapolates it to project a future value.
- In delivery forecasting it fits the remaining-scope burndown and reads where the line crosses zero for a finish date.
- It yields one date, not a probability — and should abstain on flat trends or sparse data rather than extrapolate nonsense.
- Planoda surfaces a least-squares forecast on cycles and insights, beneath a fuller Monte Carlo projection.
Given a noisy series like a cycle's daily remaining work, you want the single line that best summarizes the trend. Least-squares regression finds it by choosing the slope and intercept that minimize the total squared error to the points. The slope tells you the rate work is being completed, and projecting the line to zero gives an expected finish date — a measured estimate, not a guessed one.
Its virtue is simplicity and its limit is the same: a single line assumes a roughly steady trend and yields one date, not a probability. When the data is too sparse or the slope is flat or rising (scope not shrinking), the honest move is to report 'no trend' or 'insufficient data' rather than extrapolate nonsense. For a distribution of outcomes rather than a point estimate, a Monte Carlo over historical throughput is the richer next step.
Planoda computes a least-squares delivery forecast from the live burndown series and surfaces it on cycle and insights views with an at-risk verdict against the deadline — the transparent baseline beneath a fuller probabilistic forecast.
Related terms
- Delivery forecastingDelivery forecasting is projecting when work will finish from a team's observed throughput rather than from a chosen deadline. It fits a trend to the remaining-scope trajectory and extrapolates the completion date — and, in its probabilistic form, returns a range with confidence levels instead of a single date, so commitments are grounded in evidence rather than optimism.
- Delivery forecastA delivery forecast projects when work will actually finish, based on the team's observed throughput rather than a wished-for deadline. Instead of asking everyone to re-estimate, it fits a trend to the remaining-scope trajectory and extrapolates the completion date — and says so honestly when the data won't support a projection.
- Monte Carlo ForecastingMonte Carlo forecasting predicts delivery by running thousands of simulated futures from a team's historical throughput, producing a probability rather than a single date. Instead of 'we'll finish on the 20th,' it answers 'an 85% chance of finishing by the 24th.' It replaces false-precision estimates with honest, evidence-based ranges drawn from how the team actually performs.
- BurndownA burndown chart tracks remaining work against time over a cycle, sloping from the total scope down toward zero as items are completed. It shows whether a team is on pace to finish what it committed to, making slippage visible early. The ideal line falls steadily; a flat line warns that work is stalling.
- ThroughputThroughput is the number of work items a team completes in a given period — issues finished per week, for example. It is the simplest flow metric: a direct count of output over time. Tracked across periods, throughput reveals a team's real delivery capacity and is the basis for probabilistic, estimate-free forecasting.