Jim Frost

Data analysis gives you the keys to how to manufacture the best product, provide the best services, or answer an academic research question. I’ll share practical tidbits that may help you do just that. Continue Reading »

Control charts are a fantastic tool. These charts plot your process data to identify common cause and special cause variation. By identifying the different causes of variation, you can take action on your process without over-controlling it. Assessing the stability of a process can help you determine whether there is a problem and identify the source of the problem. Is the mean too high, too low,... 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
An exciting new study sheds light on the relationship between P values and the replication of experimental results. This study highlights issues that I've emphasized repeatedly—it is crucial to interpret P values correctly, and significant results must be replicated to be trustworthy. The study also supports my disagreement with the decision by the Journal of Basic and Applied Social Psychology to b... Continue Reading
Repeated measures designs don’t fit our impression of a typical experiment in several key ways. When we think of an experiment, we often think of a design that has a clear distinction between the treatment and control groups. Each subject is in one, and only one, of these non-overlapping groups. Subjects who are in a treatment group are exposed to only one type of treatment. This is the... 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
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
Scientists who use the Hubble Space Telescope to explore the galaxy receive a stream of digitized images in the form binary code. In this state, the information is essentially worthless- these 1s and 0s must first be converted into pictures before the scientists can learn anything from them. The same is true of statistical distributions and parameters that are used to describe sample data. They... Continue Reading
In my previous post, I wrote about the hypothesis testing ban in the Journal of Basic and Applied Social Psychology. I showed how P values and confidence intervals provide important information that descriptive statistics alone don’t provide. In this post, I'll cover the editors’ concerns about hypothesis testing and how to avoid the problems they describe. The editors describe hypothesis testing... Continue Reading
Banned! In February 2015, editor David Trafimow and associate editor Michael Marks of the Journal of Basic and Applied Social Psychology declared that the null hypothesis statistical testing procedure is invalid. They promptly banned P values, confidence intervals, and hypothesis testing from the journal. The journal now requires descriptive statistics and effect sizes. They also encourage large... Continue Reading
The 2016 presidential race is becoming more real. We’ve had several announcements with Ted Cruz, Rand Paul, Hillary Clinton, and Marco Rubio officially entering the race to be President. While the prospective Democratic candidates are down to one, or at most a few, the Republican field is extra-large this election cycle. The first order of business for a GOP candidate is to survive the nomination... Continue Reading
In this series of posts, I show how hypothesis tests and confidence intervals work by focusing on concepts and graphs rather than equations and numbers.   Previously, I used graphs to show what statistical significance really means. In this post, I’ll explain both confidence intervals and confidence levels, and how they’re closely related to P values and significance levels. How to Correctly... Continue Reading
This is a companion post for a series of blog posts about understanding hypothesis tests. In this series, I create a graphical equivalent to a 1-sample t-test and confidence interval to help you understand how it works more intuitively. This post focuses entirely on the steps required to create the graphs. It’s a fairly technical and task-oriented post designed for those who need to create the... Continue Reading
What do significance levels and P values mean in hypothesis tests? What is statistical significance anyway? In this post, I’ll continue to focus on concepts and graphs to help you gain a more intuitive understanding of how hypothesis tests work in statistics. To bring it to life, I’ll add the significance level and P value to the graph in my previous post in order to perform a graphical version of... Continue Reading
Hypothesis testing is an essential procedure in statistics. A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data. When we say that a finding is statistically significant, it’s thanks to a hypothesis test. How do these tests really work and what does statistical significance actually mean? In this series of... Continue Reading
It’s safe to say that most people who use statistics are more familiar with parametric analyses than nonparametric analyses. Nonparametric tests are also called distribution-free tests because they don’t assume that your data follow a specific distribution. You may have heard that you should use nonparametric tests when your data don’t meet the assumptions of the parametric test, especially the... Continue Reading
As someone who has collected and analyzed real data for a living, the idea of using simulated data for a Monte Carlo simulation sounds a bit odd. How can you improve a real product with simulated data? In this post, I’ll help you understand the methods behind Monte Carlo simulation and walk you through a simulation example using Devize. What is Devize, you ask? Devize is Minitab's exciting new,... Continue Reading
Choosing the correct linear regression model can be difficult. After all, the world and how it works is complex. Trying to model it with only a sample doesn’t make it any easier. In this post, I'll review some common statistical methods for selecting models, complications you may face, and provide some practical advice for choosing the best regression model. It starts when a researcher wants to... Continue Reading
Last fall I had a birthday. It wasn’t one of those tougher birthdays where the number ends in a zero. Still, the birthday got me thinking. In response, I told myself, age is just a number. Then I did a mental double-take. Can a statistician say that? After all, numbers are how I understand the world and the way it works. Can age just be a number? After some musing, I concluded that age is just a... Continue Reading
Stepwise regression and best subsets regression are both automatic tools that help you identify useful predictors during the exploratory stages of model building for linear regression. These two procedures use different methods and present you with different output. An obvious question arises. Does one procedure pick the true model more often than the other? I’ll tackle that question in this post. Fi... Continue Reading