Black Belt Scientist

I have only completed 2 full marathons so far in my life, and many halves. I love running and enjoy all the benefits that come with it. I was just recently cleared by my doctor to start running again after a long wait on some back issues I was experiencing. So I set a goal of getting back on the road in September. The thing is, I spent half of the month down in Brazil and I noticed a difference in my running pace. While Brazil's temperatures are definitely warmer than Canada in September (average of 26.8 C in Brazil versus 17 C in Canada), I also ran through a neighbourhood that has a lot of hills while down South. So, I decided to run some plots (no pun intended) and some statistical tests on the data using R for everything as a way of sharing with you all the cool things you can do for your Six Sigma projects using this incredible statistical program. Here's a quick look at the dataset: And here's my research question: "is there a statistically significant difference...

My oldest daughter Maya turned 15 yesterday, Aug 21, 2022. I took some time and went to see her at the end of her special day. Amongst other things, we were discussing wisdom, and how we mature over time. Indeed, a natural process for all of us. I was telling Maya that as she grows older, she will learn things. She will learn things in school, and she will learn things in life. Perhaps more in life than in school. Then we caught ourselves discussing a more numerical type of analysis (Maya has always been amazing with numbers). We realized that, when she was at her year zero (of course, this is a figure of speech) I was 30. She was a baby whom only knew how to cry for food while I knew a little bit about life and things. Now she is 15, she knows a little bit more than what she knew when she was 0, or 5, or 10. I'm 45, I also know a little bit more than what I knew when I was 30 (or 0, or 5, or 10). We decided to graph the data, looking into the "wisdom rate of growth" over...

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