There is a popular notion in the #NoEstimates paradigm that *Empirical* data is the basis of forecasting the future performance of a development project. In principle this is true, but the concept is not complete in the way it is used. Let's start with the data source used for this conjecture.

There are 12 sample in the example used by #NoEstimates. In this case *stickies per week*. From this *time series* an *average* is calculated for the future. This is the *empirical data is used to estimate in the No Estimates *paradigm. The Average is 18.1667 or just 18 *stickies per week*.

But we all have read or should have read Sam Savage's *The Flaw of Averages*. This is a very nice *populist* book. By *populist* I mean an easily accessible text with little or not mathematics in the book. Although Savage's work is highly mathematically based with his tool set.

There is a simple set of tools that can be applied for Time Series analysis, using past performance to forecast future performance of the system that created the previous time series. The tool is R and is free for all platforms.

Here's the R code for performing a statistically sound forecast to estimate the possible ranges values the past empirical *stickies *can take on in the future.

Put the time series in an Excel file and save it as TEXT named BOOK1

> SPTS=ts(Book1) - apply the Time Series function in R to convert this data to a time series

> SPFIT=arima(SPTS) - apply the simple ARIMA function to the time series

> SPFCST=forecast(SPFIT) - build a forecast from the ARIMA outcome

> plot(SPFCST) - plot the results

Here's that plot. This is the 80% and 90% confidence bands for the possible outcomes in the future from the past performance - empirical data from the past.

The 80% range is 27 to 10 and the 90% range is 30 to 5.

**So the killer question.**

**Would you bet your future on a probability of success with a +65 to -72% range of cost, schedule, or technical performance of the outcomes?**

I hope not. This is a flawed example I know. Too small a sample, no adjustment of the ARIMA factors, just a quick raw assessment of the data used in some quarters as a replacement for actually estimating future performance. But this assessment shows how to *empirical data* COULD support making decisions about future outcomes in the presence of uncertainty using past time series once the naive assumptions of sample size and wide variances are corrected..

**The End**

If you hear you can make decisions without estimating that's pretty much a violation of all established principles of Microeconomics and statistical forecasting. When answer comes back *we sued empirical data*, that your time series empirical data, download R, install all the needed packages, put the data in a file, apply the functions above and see if you really want to commit to spending other peoples money with a confidence range of * +65 to -72% *of performing like you did in the past? I sure hope not!!