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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 »

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
Analysis of variance (ANOVA) is great when you want to compare the differences between group means. For example, you can use ANOVA to assess how three different alloys are related to the mean strength of a product. However, most ANOVA tests assess one response variable at a time, which can be a big problem in certain situations. Fortunately, Minitab statistical software offers a... Continue Reading
Using a sample to estimate the properties of an entire population is common practice in statistics. For example, the mean from a random sample estimates that parameter for an entire population. In linear regression analysis, we’re used to the idea that the regression coefficients are estimates of the true parameters. However, it’s easy to forget that R-squared (R2) is also an estimate.... Continue Reading
I’ve written about the importance of checking your residual plots when performing linear regression analysis. If you don’t satisfy the assumptions for an analysis, you might not be able to trust the results. One of the assumptions for regression analysis is that the residuals are normally distributed. Typically, you assess this assumption using the normal probability plot of the residuals. Are... Continue Reading
Astronomy is cool! And, it’s gotten even more exciting with the search for exoplanets. You’ve probably heard about newly discovered exoplanets that are extremely different from Earth. These include hot Jupiters, super-cold iceballs, super-heated hellholes, very-low-density puffballs, and ultra-speedy planets that orbit their star in just hours. And then there is PSR J1719-1438 which has the mass... 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