Bay Area Entrepreneurs in Statistics

Bay Area Entrepreneurs in Statistics The road to discovery is paved with numbers. Helping you navigate the numbers road is our goal with presentations, social gatherings, and workshops.

We are a mixed group of individuals with a common interest in topics of a numerical nature in an entrepreneurial context. All who share an interest in statistics, dealing with Big Data, numerical analysis and applications, or are just curious are welcome.

Estimating Pi using a Monte Carlo simulation in Excel - the estimate by averaging the last 50 of 500 runs changes each t...
03/15/2025

Estimating Pi using a Monte Carlo simulation in Excel - the estimate by averaging the last 50 of 500 runs changes each time but is usually +/- 1%. This run the result was 3.14 to three significant figures.

What's a couple of degrees between friends when it's already hot with an average annual temperature of 100 dF? Well if ...
06/03/2024

What's a couple of degrees between friends when it's already hot with an average annual temperature of 100 dF? Well if we add 2 dF so the average goes to 102 dF things get a lot hotter. For a simulation run I set a threshold of 125 dF. Yes, the 100 dF average does have days exceeding 125 dF. And the simulation indicates as many as 22! That's hot! But at 102 dF there are a projected 49 days over 125 dF. Either way it is too hot for me, but just a small increase in average can make a big increase in the frequency at the upper extreme.

In the charts that follow, blue represents the 100 dF scenario and red the 102 dF scenario. The simulation was programmed in Excel using a Monte Carlo randomization.

Using a Monte Carlo Program to Define Process Control Parameters © Ken Osborn 2013 [Paper available on request from koz...
02/29/2024

Using a Monte Carlo Program to Define Process Control Parameters © Ken Osborn 2013
[Paper available on request from kozborn@sbcglobal.net]

Abstract: Profiler is a program written in Excel® Visual Basic® and uses a Monte-Carlo simulation to fit process data to a Normal distribution. Output includes raw data and simulation statistics and outliers are identified and flagged. If the data are not skewed and show a good fit except for the outliers, process control limits can be calculated from the Normal curve average and standard deviation.

Data plotted in red in Chart 1 (attached) are from a waste discharge stream controlling for mercury. Clearly some of the values are outliers and do not represent the process when it is in control. Chart 1: Mercury in discharge vs Monte Carlo simulation The blue line represents the Monte Carlo fit to a Normal distribution. The majority of the mercury discharge data exhibit a close fit to the simulation curve with outliers clearly visible.

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