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Which is worse, underestimation or over estimation?
Whilst obviously accurate estimates are the best outcome, over-estimation is less bad than underestimation. Underestimation can impact dependencies and the overall quality of the project. Overestimation may be wasteful for the resources on a particular task, but it is less likely to impact other tasks or overall quality.
How does overestimation affect the quality of a task?
Overestimation may be wasteful for the resources on a particular task, but it is less likely to impact other tasks or overall quality. Of course, a third option following the critical chain methodologies is to consider adding buffers to the schedule to allow for some underestimation at the individual task level.
Is there a penalty for overestimation in software?
In software, the penalty for overestimation is linear and bounded -work will expand to fill available time, but it will not expand any further.
Which is the most common metric for linear regression?
MSE – Means Square Error (L2 Loss) Mean Squared Error (MSE), also known as Least Squares Error (LSE), is the simple and commonly used evaluation metrics for linear regression. To compute MSE, take the difference between actual and predicted values of each observation, square the differences, and then find out the mean.
Which is better, a map or a Bayesian estimate?
Many problems will have Bayesian and frequentist solutions that are similar so long as the Bayesian does not have too strong of a prior. Assuming you have accurate prior information, MAP is better if the problem has a zero-one loss function on the estimate.
What’s the best way to do story estimation?
Story estimation is a way of educating and informing decision. Get the team on board. Break up items that exceed the maximum. If you get infinity or something too large for your time boundary, add risk mitigation or spikes to gain more understanding. No distractions during story estimation.