dcsimg
 

T-Test Example

Blog posts and articles about testing hypotheses with the statistical method called the T-Test.

In my previous post, I showed you how to set up data collection for a gage R&R analysis using the Assistant in Minitab 17. In this case, the goal of the gage R&R study is to test whether a new tool provides an effective metric for assessing resident supervision in a medical facility.   As noted in that post, I'm drawing on one of my favorite bloggers about health care quality, David Kashmer of the... Continue Reading
Right now I’m enjoying my daily dose of morning joe. As the steam rises off the cup, the dark rich liquid triggers a powerful enzyme cascade that jump-starts my brain and central nervous system, delivering potent glints of perspicacity into the dark crevices of my still-dormant consciousness. Feels good, yeah! But is it good for me? Let’s see what the studies say… Drinking more than 4 cups of coffee... 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
I left off last with a post outlining how the Six Sigma students at Rose-Hulman were working on a project to reduce the amount of recycling thrown in the normal trash cans in all of the academic buildings at the institution. Using the DMAIC methodology for completing improvement projects, they had already defined the problem at hand: how could the amount of recycling that’s thrown in the normal trash... Continue Reading
  In my experience, one of the hardest concepts for users to wrap their head around revolves around the Power and Sample Size menu in Minitab's statistical software, and more specifically, the field that asks for the "difference" or "difference to detect."  Let’s start with power. In statistics, the definition of power is the probability that you will correctly reject the null hypothesis when it is... Continue Reading
by Matthew Barsalou, guest blogger.  E. E. Doc Smith, one of the greatest authors ever, wrote many classic books such as The Skylark of Space and his Lensman series. Doc Smith’s imagination knew no limits; his Galactic Patrol had millions of combat fleets under its command and possessed planets turned into movable, armored weapons platforms. Some of the Galactic Patrol’s weapons may be well... Continue Reading
In my recent meetings with people from various companies in the service industries, I realized that one of the problems they face is that they were collecting large amounts of "qualitative" data: types of product, customer profiles, different subsidiaries, several customer requirements, etc. As I discussed in my previous post, one way to look at qualitative data is to use different types of... Continue Reading
In several previous blogs, I have discussed the use of statistics for quality improvement in the service sector. Understandably, services account for a very large part of the economy. Lately, when meeting with several people from financial companies, I realized that one of the problems they faced was that they were collecting large amounts of "qualitative" data: types of product, customer... Continue Reading
If you’re not a statistician, looking through statistical output can sometimes make you feel a bit like Alice in Wonderland. Suddenly, you step into a fantastical world where strange and mysterious phantasms appear out of nowhere.   For example, consider the T and P in your t-test results. “Curiouser and curiouser!” you might exclaim, like Alice, as you gaze at your output. What are these values,... 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
As a member of Minitab's Technical Support team, I get the opportunity to work with many people creating control charts. They know the importance of monitoring their processes with control charts, but many don’t realize that they themselves could play a vital role in improving the effectiveness of the control charts.   In this post I will show you how to take control of your charts by using Minitab... Continue Reading
"Data! Data! Data! I can't make bricks without clay."  — Sherlock Holmes, in Arthur Conan Doyle's The Adventure of the Copper Beeches Whether you're the world's greatest detective trying to crack a case or a person trying to solve a problem at work, you're going to need information. Facts. Data, as Sherlock Holmes says.  But not all data is created equal, especially if you plan to analyze as part of... Continue Reading
Have you ever had a probability plot that looks like this? The probability plot above is based on patient weight (in pounds) after surgery minus patient weight (again, in pounds) before surgery. The red line appears to go through the data, indicating a good fit to the Normal, but there are clusters of plotting points at the same measured value. This occurs on a probability plot when there are many... 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
by Jasmin Wong, guest blogger The combination of statistical methods and injection moulding simulation software gives manufacturers a powerful way to predict moulding defects and to develop a robust moulding process at the part design phase.  CAE (computer-aided engineering) is widely used in the injection moulding industry today to improve product and mould designs as well as to resolve or... 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
I got lost a lot as a child. I got lost at malls, at museums, Christmas markets, and everywhere else you could think of. Had it been in fashion to tether children to their parents at the time, I'm sure my mother would have. As an adult, I've gotten used to using a GPS device to keep me from getting lost. The Assistant in Minitab is like your GPS for statistics. The Assistant is there to provide you... Continue Reading
Using statistical techniques to optimize manufacturing processes is quite common now, but using the same approach on social topics is still an innovative approach. For example, if our objective is to improve student academic performances, should we increase teachers wages or would it be better to reduce the number of students in a class? Many social topics (the effect of increasing the minimum... 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