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To create a designed experiment using the Assistant, open Minitab and select Assistant > DOE > Plan and Create. You'll be presented with a decision tree that helps you take a sequential approach to the experimentation process by offering a choice between a screening design and a modeling design.
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A screening design is important if you have a lot of potential factors to consider and you want to figure out which ones are important. The Assistant guides you through the process of testing and analyzing the main effects of 6 to 15 factors, and identifies the factors that have greatest influence on the response.
Once you've identified the critical factors, you can use the modeling design. Select this option, and the Assistant guides you through testing and analyzing 2 to 5 critical factors and helps you find optimal settings for your process.
Even if you're an old hand at analyzing designed experiments, you may want to use the Assistant to create designs since the Assistant lets you print out easy-to-use data collection forms for each experimental run. After you've collected and entered your data, the designs created in the Assistant can also be analyzed using Minitab's core DOE tools available through the Stat > DOE menu.
For grilling steaks, there aren't that many variables to consider, so we'll use the Assistant to plan and create a modeling design that will optimize our grilling process. Select Assistant > DOE > Plan and Create, then click the "Create Modeling Design" button.
First we enter the name of our Response and the goal of the experiment. Our response is "Flavor," and the goal is "Maximize the response." Next, we enter our factors. We'll look at three critical variables:
If we wanted to, we could select more than 1 replicate of the experiment. A replicate is simply a complete set of experimental runs, so if we did 3 replicates, we would repeat the full experiment three times. But since this experiment has 16 runs, and neither our budget nor our stomachs are limitless, we'll stick with a single replicate.
Alternatively, you can just record the results of each run in the worksheet the Assistant creates, which you'll need to do anyway. But having the printed data collection forms can make it much easier to keep track of where you are in the experiment, and exactly what your factor settings should be for each run.
If you've used the Assistant in Minitab for other methods, you know that it seeks to demystify your analysis and make it easy to understand. When you create your experiment, the Assistant gives you a Report Card and Summary Report that explain the steps of the DOE and important considerations, and a summary of your goals and what your analysis will show.
Now it's time to cook some steaks, and rate the flavor of each. If you want to do this for real and collect your own data, please do so! Tomorrow's post will show how to analyze your data with the Assistant.
Siena Heights University students, faculty, and staff can utilize Minitab, software used for data analysis and statistics, on or off-campus starting this year. With our annual renewal of the software, Minitab now offers a web version and a downloadable Windows desktop version of their software free to all members of the SHU community. To access your Minitab account, visit this link or look for an email from Minitab sent earlier this year.
Minitab now supports accessing the statistical program through a new web app made available to SHU. The web application has many of the same features you may be familiar with and now includes direct OneDrive support, allowing for quick saving and loading from your Siena Heights University cloud storage. If you utilize a MacOS device or Linux-based machine, you will need to use the web application. Minitab discontinued support for all non-Windows platforms in early 2021.
To access the web app, visit app.minitab.com and enter your full SHU email address. You will not be able to use social media logins on this page. Once you enter your email address and click Next, you will need to sign in through Office.com if you are not logged in already. After signing in successfully, access to the web application and account settings will be given.
Minitab currently maintains a Windows desktop application of Minitab. Starting in 2021, the MacOS version was deprecated, leaving only the Windows app and web-based app available to use. This application is free to download for all SHU students, staff, and faculty, and can be found after signing in to the Minitab website with your SHU credentials. Minitab only supports 64-bit versions of Windows, and suggests 32-bit platforms utilize the web application instead.
Minitab provides a comparison chart between the web application and the Windows desktop application. Once all features of the Desktop application are integrated to the web version, Minitab will discontinue any downloadable form of their software, opting instead to be fully web-based.
You do not need to print out any of the graphs that you generate for thisactivity. Just look at them on the screen and answer questions based off ofthem in the activity. There is one graph (the histogram) that you need tocopy onto your paper.
You can change the statistics that are given by clicking on thestatistics button. In particular, the N* and SE Mean won't be used rightnow. The SE Mean will be used in later chapters, but the number of missingcases is rarely used. Other options in the statistics menu that we will useoccasionally are the variance, range, interquartile range, and sum ofsquares.
You may describe more than one variable at a time. However, in thisproblem, we only have one variable, time, that we want todescribe. The other two variables are categorical variables used forclassification purposes only, it would make no sense to describe them.Sample output from the descriptive statistics command is shown in thefigure.
This is the way to describe the time foreach of the heats. We use the "By Variable"option to do this. The column used for the By Variable should be acategorical variable such as the gender, race, age group (but not age as anumber), or heat number. There should be few categories forthis variable, do not use variables that have large numbers of unique valuesfor the By Variable. Do not use measurement variables (height, weight, age,time) as the by variable.
Normally, we would let Minitab just automatically assign groups for us,but in this case, we're specifically looking for bars that are one standarddeviation wide. That means that we're going to have to do some extra workthat we wouldn't normally have to do.
Find the mean minus three times the standard deviation and the mean plusthree times the standard deviation: 5.949 - 3(0.987) = 2.988 and 5.949 +3(0.987) = 8.910. These numbers correspond to our lowest and highest classboundaries and will be used later. Notice that our data falls within thisrange since our minimum is 4.04 and our maximum is 8.60. If our dataextended beyond 3 standard deviations, then we would need to extendthose ranges to fit all of the data.
The old version of Minitab (version 13) would allow you to set all kindsof options before you generated the graph. The new version (version 14)allows you to look at the graph and then play with the settings. There'sarguments in favor of both directions, but for most people, the new way isprobably better. The rest of this will involve changing the graph to give uswhat we want to have.
Since the file "walk.mtw" was already provided for you, youwill probably be okay without saving a project file for this activity.There's not much work that wouldn't be easy to recreate if you messed up andhad to go back.
For other projects, be sure to save your work! This allows you to gothrough and work on the activity incrementally (you can do part of it oneday and finish it another day). All open windows are saved when you save theproject, but if you close a graph, it won't be saved. When you arecompletely done with the project, you may wish to close the graphs. Thiswill make the files smaller and keep us from running out of room on thedrive.
The whole 68-95-99.7 thing is approximate anyway, and it's meant to helpyou determine if the data is normally distributed. The normal model istalked about in chapter 5 and so we won't dwell on it too much right now,but there are other things we can look at, of course, we just don't knowabout them yet. So, if you take the time to read this section, you'll beahead of the game for later.
The Anderson Darling Normality Test is designed to test the claim thatthe data comes from a population that is normally distributed (unimodal,symmetric, bell-shaped, no outliers). The p-value is the likelihood ofgetting the results we got if the data has a normal distribution. Typically,as long as the p-value is more than 5% (0.05), there's no reason to questionthe normal distribution. In this case, the p-value is 0.246, so we'll retainthe claim that the dat is normally distributed.
The kurtosis is a measure of the peakedness of a curve. A normal curvehas a kurtosis of 0, a negative value means it is flatter than a normalmodel and a value greater than 0 means it is sharper than a normal model.The kurtosis for our data is 0.904122, which indicates that our data may bemore peaked than what we would like.
Notice I didn't really answer the question. That's because in statistics,there isn't always a definite answer. We deal a lot with levels of gray.Later, we'll make an assumption that our data comes from a normal populationand base our decisions on that assumption. All in all, our data is closeenough to not cause problems with that assumption.
The Analysis of Variance is used to test whether or not three or moremeans are the same. The response variable is the variable whose value you'retesting and the factor variable is a categorical variable used to define thegroups.
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