Regression Analysis

Blog posts and articles about regression analysis techniques applied to Lean and Six Sigma quality improvement projects.

Did you ever wonder why statistical analyses and concepts often have such weird, cryptic names? One conspiracy theory points to the workings of a secret committee called the ICSSNN. The International Committee for Sadistic Statistical Nomenclature and Numerophobia was formed solely to befuddle and subjugate the masses. Its mission: To select the most awkward, obscure, and confusing name possible... Continue Reading
By Matthew Barsalou, guest blogger A problem must be understood before it can be properly addressed. A thorough understanding of the problem is critical when performing a root cause analysis (RCA) and an RCA is necessary if an organization wants to implement corrective actions that truly address the root cause of the problem. An RCA may also be necessary for process improvement projects; it is... Continue Reading
As Halloween approaches, you are probably taking the necessary steps to protect yourself from the various ghosts, goblins, and witches that are prowling around. Monsters of all sorts are out to get you, unless they’re sufficiently bribed with candy offerings! I’m here to warn you about a ghoul that all statisticians and data scientists need to be aware of: phantom degrees of freedom. These phantoms... Continue Reading
With Speaker John Boehner resigning, Kevin McCarthy quitting before the vote for him to be Speaker, and a possible government shutdown in the works, the Freedom Caucus has certainly been in the news frequently! Depending on your political bent, the Freedom Caucus has caused quite a disruption for either good or bad.  Who are these politicians? The Freedom Caucus is a group of approximately 40... Continue Reading
I was recently asked a couple of questions about stability studies in Minitab. Question 1:  If you enter in a lower and upper spec in the Stability Study dialog window, why do I see only one confidence bound per fitted line on the resulting graph? Shouldn’t there be two? You use a stability study to analyze the stability of a product over time and to determine the product's shelf life. In order to... Continue Reading
I recently guest lectured for an applied regression analysis course at Penn State. Now, before you begin making certain assumptions—because as any statistician will tell you, assumptions are important in regression—you should know that I have no teaching experience whatsoever, and I’m not much older than the students I addressed. I’m just 5 years removed from my undergraduate days at Virginia Tech,... Continue Reading
My previous post showed an example of using ordinary linear regression to model a count response. For that particular count data, shown by the blue circles on the dot plot below, the model assumptions for linear regression were adequately satisfied. But frequently, count data may contain many values equal or close to 0. Also, the distribution of the counts may be right-skewed. In the quality field,... Continue Reading
Ever use dental floss to cut soft cheese? Or Alka Seltzer to clean your toilet bowl? You can find a host of nonconventional uses for ordinary objects online. Some are more peculiar than others. Ever use ordinary linear regression to evaluate a response (outcome) variable of counts?  Technically, ordinary linear regression was designed to evaluate a a continuous response variable. A continuous... Continue Reading
In regression analysis, overfitting a model is a real problem. An overfit model can cause the regression coefficients, p-values, and R-squared to be misleading. In this post, I explain what an overfit model is and how to detect and avoid this problem. An overfit model is one that is too complicated for your data set. When this happens, the regression model becomes tailored to fit the quirks and... Continue Reading
Imagine a multi-million dollar company that released a product without knowing the probability that it will fail after a certain amount of time. “We offer a 2 year warranty, but we have no idea what percentage of our products fail before 2 years.” Crazy, right? Anybody who wanted to ensure the quality of their product would perform a statistical analysis to look at the reliability and survival of... Continue Reading
If you want to use data to predict the impact of different variables, whether it's for business or some personal interest, you need to create a model based on the best information you have at your disposal. In this post and subsequent posts throughout the football season, I'm going to share how I've been developing and applying a model for predicting the outcomes of 4th down decisions in Big... Continue Reading
When you run a regression in Minitab, you receive a huge batch of output, and often it can be hard to know where to start. A lot of times, we get overwhelmed and just go straight to p-values, ignoring a lot of valuable information in the process. This post will give you an introduction to one of the other statistics Minitab displays for you, the VIF, or Variance Inflation Factor.  To start, let's... Continue Reading
Previously, I’ve written about how to interpret regression coefficients and their individual P values. I’ve also written about how to interpret R-squared to assess the strength of the relationship between your model and the response variable. Recently I've been asked, how does the F-test of the overall significance and its P value fit in with these other statistics? That’s the topic of this post! In... Continue Reading
In my previous post, I showed you that the coefficients are different when choosing (-1,0,1) vs (1,0) coding schemes for General Linear Model (or Regression).  We used the two different equations to calculate the same fitted values. Here I will focus on showing what the different coefficients represent.  Let's use the data and models from the last blog post: We can display the means for each level... Continue Reading
Since Minitab 17 Statistical Software launched in February 2014, we've gotten great feedback from many people have been using the General Linear Model and Regression tools. But in speaking with people as part of Minitab's Technical Support team, I've found many are noticing that there are two coding schemes available with each. We frequently get calls from people asking how the coding scheme you... Continue Reading
Imagine that you are watching a race and that you are located close to the finish line. When the first and fastest runners complete the race, the differences in times between them will probably be quite small. Now wait until the last runners arrive and consider their finishing times. For these slowest runners, the differences in completion times will be extremely large. This is due to the fact that... Continue Reading
The NCAA Tournament is right around the corner, and you know what that means: It’s time to start thinking about how you’re going to fill out your bracket! For the last two years I’ve used the Sagarin Predictor Ratings to predict the tournament. However, there is a problem with that strategy this year. The old method uses a regression model that calculates the probability one team has of beating... Continue Reading
by Lion "Ari" Ondiappan Arivazhagan, guest blogger.  An alarming number of borewell accidents, especially involving little children, have occurred across India in the recent past. This is the second of a series of articles on Borewell accidents in India. In the first installment of the series, I used the G-chart in Minitab Statistical Software to predict the probabilities of innocent children... Continue Reading
In part 1 of this post, I covered how Six Sigma students at Rose-Hulman Institute of Technology cleaned up and prepared project data for a regression analysis. Now we're ready to start our analysis. We’ll detail the steps in that process and what we can learn from our results. What Factors Are Important? We collected data about 11 factors we believe could be significant: Whether the date of... Continue Reading
By Peter Olejnik, guest blogger. Previous posts on the Minitab Blog have discussed the work of the Six Sigma students at Rose-Hulman Institute of Technology to reduce the quantities of recyclables that wind up in the trash. Led by Dr. Diane Evans, these students continue to make an important impact on their community. As with any Six Sigma process, the results of the work need to be evaluated. A... Continue Reading