Black Belt Scientist

It's that time of the year! No, I don't mean April showers bring May flowers, I mean the time when our fellow wild life animals like to visit our backyards often in search for food. Our raccoon friends have become a bit of an annoyance though since they like to sometimes use our deck as latrine. So, I've been collecting some data and have decided to run a binary logistic regression with the help of SigmaXL statistical package to predict the probability that these masked bandits may (or may not) show up. For that, I've been using a safe and natural repellent (I won't broadcast but, it's coyote urine) to help me in discouraging the presence of the poopy animals around our property. The Data Here's a snapshot of the data I've been collecting. As you can see, the outcome (Y: Raccoon Appearance) expected is a binary (dichotomous) variable. I'm trying to figure out if Mr. Coon and friends will show up or not given certain predictors. I've been trackin...

If you read enough material on Six Sigma principles you most likely have come across the calculation of defects per unit (DPU) or defects per million of opportunities (DPMO). You have also, most likely, come across the suggested "Euler's formula" for estimating defect rates. Now, as we all know, 1 - defect rate equals yield. In other others, if you are somehow calculating a defect rate of a process you are also consequently easily computing the yield of the process. For example, a process with 10% defect rate has, mathematically, 90% yield. With that out of our way, let's look at how each is calculated and the issue with Euler's formula when applied to a defect rate level over 10%. DPMO is simply stated, the defects (per unit) divided by one million (that is, one million opportunities of finding a defect). Note: this is any unwelcome departure from the standard, not necessarily the entire unit being defective , the distinction here is very important. At a 4.5 si...

Here's a quick example of how helpful comparative boxplots and density curves can be when it comes to visualizing the behaviour of the data set. I used RStudio to create these visualizations but the most important part of this post is the interpretation of these plots. If you do not use RStudio, feel free to skip to the section where I address the plots themselves. However, if you are an RStudio user you might pick up some coding tips starting at the top of this post. These plots can be created with most statistical software/packages available in the market (I also use Minitab, ProcessMA, and Sigma XL). These are the plots that we are going to look at: First things first, I've loaded the following libraries in RStudio: Then I used the rnorm function in R to randomly create three sets of data. These are heights in inches for males, females, and NBA players. I created 100 data points for each variable, and you can see in the screen shot below the mean and standard deviation for e...

Hello! Here's a list of my current web apps with fun tools for teaching topics such as histograms, normal distribution, mean shift and spread, scatter plots, and boxplots. I continue to work on more fun apps that will support the Lean Six Sigma practitioner and other teachers alike in their content delivery. I hope you'll enjoy the experience! Click on the titles to open the apps. Ontario Sunshine List 2019 Mean shift and spread Normal Distribution Scatter Diagrams Boxplots

As a data analyst, I often refer to ratios rather than absolute numbers in analysis. With COVID-19, it shall not be different. Many are the correct yet misleading plots and tables out there. And many are the plainly wrong, deceiving, and manipulated ones. I'll start with a disclaimer that I have absolutely no political reason, motive, or even energy to spend on this from a political standpoint. I am simply looking at Ontario's reported data, and nothing else. The reported data have been sourced at data.ontario.ca/en/dataset/status-of-covid-19-cases-in-ontario . If you are unsure of the terminology, check out the data dictionary provided by Ontario, although I will mention a few of them here. For this analysis, I am looking into the mortality rates between Jun 9 and Oct 9, 2020 (both overall and LTC) as well as the ICU situation in Ontario (both with and without the need for a ventilator). Overall and LTC (Long Term Care) mortality rates (the number of deaths divided by the tota...