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 »

In my previous post, I described how I was asked to weigh in on the ethics of researchers (DeStefano et al. 2004) who reportedly discarded data and potentially set scientific knowledge back a decade. I assessed the study in question and found that no data was discarded and that the researchers used good statistical practices. In this post, I assess a study by Brian S. Hooker that was... Continue Reading
The other day I received a request from a friend to look into a new study in a peer reviewed journal that found a link between MMR vaccinations and an increased risk of autism in African Americans boys. To draw this conclusion, the new study reanalyzed data that was discarded a decade ago by a previous study. My friend wanted to know, from a statistical perspective, was it unethical for the... Continue Reading
Previously, I showed why there is no R-squared for nonlinear regression. Anyone who uses nonlinear regression will also notice that there are no P values for the predictor variables. What’s going on? Just like there are good reasons not to calculate R-squared for nonlinear regression, there are also good reasons not to calculate P values for the coefficients. Why not—and what to use instead—are the... Continue Reading
Previously, I’ve written about when to choose nonlinear regression and how to model curvature with both linear and nonlinear regression. Since then, I’ve received several comments expressing confusion about what differentiates nonlinear equations from linear equations. This confusion is understandable because both types can model curves. So, if it’s not the ability to model a curve, what isthe... Continue Reading
In regression analysis, you'd like your regression model to have significant variables and to produce a high R-squared value. This low P value / high R2 combination indicates that changes in the predictors are related to changes in the response variable and that your model explains a lot of the response variability. This combination seems to go together naturally. But what if your regression model... Continue Reading
In Minitab, the Assistant menu is your interactive guide to choosing the right tool, analyzing data correctly, and interpreting the results. If you’re feeling a bit rusty with choosing and using a particular analysis, the Assistant is your friend! Previously, I’ve written about the new linear model features in Minitab 17. In this post, I’ll work through a multiple regression analysis example and... Continue Reading
There is high pressure to find low P values. Obtaining a low P value for a hypothesis test is make or break because it can lead to funding, articles, and prestige. Statistical significance is everything! My two previous posts looked at several issues related to P values: P values have a higher than expected false positive rate. The same P value from different studies can correspond to different false... Continue Reading
The interpretation of P values would seem to be fairly standard between different studies. Even if two hypothesis tests study different subject matter, we tend to assume that you can interpret a P value of 0.03 the same way for both tests. A P value is a P value, right? Not so fast! While Minitab statistical software can correctly calculate all P values, it can’t factor in the larger context of the... Continue Reading
The P value is used all over statistics, from t-tests to regression analysis. Everyone knows that you use P values to determine statistical significance in a hypothesis test. In fact, P values often determine what studies get published and what projects get funding. Despite being so important, the P value is a slippery concept that people often interpret incorrectly. How do you interpret P values? In... Continue Reading
One-way ANOVA can detect differences between the means of three or more groups. It’s such a classic statistical analysis that it’s hard to imagine it changing much. However, a revolution has been under way for a while now. Fisher's classic one-way ANOVA, which is taught in Stats 101 courses everywhere, may well be obsolete thanks to Welch’s ANOVA. In this post, I not only want to introduce you to... Continue Reading
Nonlinear regression is a very powerful analysis that can fit virtually any curve. However, it's not possible to calculate a valid R-squared for nonlinear regression. This topic gets complicated because, while Minitab statistical software doesn’t calculate R-squared for nonlinear regression, some other packages do. So, what’s going on? Minitab doesn't calculate R-squared for nonlinear models... Continue Reading
We released Minitab 17 Statistical Software a couple of days ago. Certainly every new release of Minitab is a reason to celebrate. However, I am particularly excited about Minitab 17 from a data analyst’s perspective.  If you read my blogs regularly, you’ll know that I’ve extensively used and written about linear models. Minitab 17 has a ton of new features that expand and enhance many types of... Continue Reading
Atlanta was a mess on January 28th, 2014.  Thousands were trapped on the roads overnight while others managed to get to roadside stores to camp out. Thousands of students were forced to spend the night in their schools and the National Guard was called in to get them home. Many wondered how less than three inches of snow could cripple the city, particularly when Atlanta had experienced a similar... Continue Reading
I didn’t expect that our family trip to Florida would end with me driving a plane load of passengers nearly 200 miles to their homes, but it did. Yes, it was a long and strange journey home. A journey that started in the tropical warmth of southern Florida and ended the next morning in central Pennsylvania, which felt like the arctic wastelands thanks to the dreaded polar vortex. During this... Continue Reading
R-squared gets all of the attention when it comes to determining how well a linear model fits the data. However, I've stated previously that R-squared is overrated. Is there a different goodness-of-fit statistic that can be more helpful? You bet! Today, I’ll highlight a sorely underappreciated regression statistic: S, or the standard error of the regression. S provides important information that... Continue Reading
Just how high should R2 be in regression analysis? I hear this question asked quite frequently. Previously, I showed how to interpret R-squared (R2). I also showed how it can be a misleading statistic because a low R-squared isn’t necessarily bad and a high R-squared isn’t necessarily good. Clearly, the answer for “how high should R-squared be” is . . . it depends. In this post, I’ll help you answer... Continue Reading
I’ve written a number of blog posts about regression analysis and I think it’s helpful to collect them in this post to create a regression tutorial. I’ll supplement my own posts with some from my colleagues. This tutorial covers many aspects of regression analysis including: choosing the type of regression analysis to use, specifying the model, interpreting the results, determining how well the... Continue Reading
In my previous post, I looked at how personal income levels fit into the global distribution of incomes. Although, I’d be the last person to suggest that a higher income guarantees more happiness—after all, I’ve visited a number of developing countries and, as long as their basic needs are met, the people seem to be just as happy and hard working as people here at home. So instead of personal... Continue Reading
In the United States, our Thanksgiving holiday is fast approaching. On this day, we give thanks for the good things in our lives. For this post, I wanted to quantify how thankful we should be. Ideally, I’d quantify something truly meaningful, like happiness. Unfortunately, most countries are not like Bhutan, which measures the gross national happiness and incorporates it into their five-year... Continue Reading
In my previous post, I highlighted recent academic research that shows how the presentation style of regression results affects the number of interpretation mistakes. In this post, I present four tips that will help you avoid the more common mistakes of applied regression analysis that I identified in the research literature. I’ll focus on applied regression analysis, which is used to... Continue Reading