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Introducing
ROIstat

The free and easy graphic user interface harnessing the power of R for professors, students, and working professionals alike.

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Professor / Teacher

Students don't need to know how to use R to learn the power of statistics.

Do you spend more time instructing introductory students in how to script in R instead of how and when to use statistics? This GUI still runs in R (and is free!) but allows you to focus on helping the students understand statistics. When they are ready for more advanced statistics and need to learn R, they will already have it installed and working.

Student

This graphical user interface gives you a tool that allows you to focus on learning how to use statistics to solve your problems, not on how to write a command in R. The packages that are used in ROIstat have been shown to give the correct answers, so you don't have to worry about how some package you downloaded is going to do an analysis.

Oh, and its FREE!

Professional Engineer, Six Sigma Belt, Data Analyst

Are you frustrated when you know R can do something you need, but you don't remember the command or the syntax? Do you spend a lot of time going back to your notes rather than getting the answer?

Once you learn how statistics can be used to solve practical, everyday problems, you want to get in, get the answer, and get on with your life. ROIstat puts the power of R into a graphic user interface so you don't have to remember what to type to get results from your data quickly and accurately.

Oh, and it is FREE!

My students love it and they are ALL using it. This class had the highest exam 1 scores in the past 4 semesters that I've taught the class.

One student who struggles in class told me, "I wouldn't be able to pass this class without this app."

What a difference this app has made!

ROIstat Features

Import data from Google Sheets, the R environment, or from any of a number of local files such as .txt, .csv, .xls, .xlsx, .sav and many others. Easily set the class of the data, or exclude certain data vectors from the analysis. Use graphical controls to quickly filter the data, all without altering the original file.

Generate the binomial, Poisson, Normal, exponential, and F-distributions by entering parameters. Calculate critical values for common test statistics.

Enter statistics directly into the interface for a number of one- and two-sample tests. Not having to deal with data makes it easier to teach and easier to learn.

Whether you are a Black Belt or in charge of improving processes, you may not be using your stats software every day, and you can waste a lot of time trying to remember how to do something that you know the software can do. R is "hard to learn and easy to forget," as a former student once said.

When you know how to use the power of statistics to make decisions, you want to get in, get the answer, and get on with your life. ROIstat allows you to get your analysis, and the ROI that comes with it, quickly without having to remember how to script it in R.

A comprehensive arsenal of statistical tests the working professional needs to get in, get an answer, and get on with their job.

Use data configured in columns for each group or with columns for factors and data.

Easily and rapidly generate histograms, frequency diagrams, and density plots to explore your data. Overlay them on one axis, add a normal curve, or add specifications with the click of a button.

Explore your data with a wide variety of statistics. Get estimates of the confidence intervals of means and standard deviations. Create a scatterplot and try out various regression models.

Perform oneway analysis of variance (ANOVA) for fixed and random effects. Includes dispersion analysis. If ANOVA assumptions are violated, you can also select the Kruskal-Wallis ANOVA by ranks.

Perform post-hoc analyses on fixed, random, and Kruskal-Wallis ANOVAs.

For fixed and K-W ANOVAs, you can display a profile plot with points connected by lines, or as a violin or boxplot. For fixed effects you can choose between Tukey's HSD or Games & Howell for all pair-wise comparisons. For K-W, all pair-wise comparisons are made with the Mann-Whitney U test. Family-wise error is controlled with Bonferroni-Dunn. The results for both are collected in an easy-to-read table and the results of individual tests can be shown.

For random effects, a graph is displayed showing the magnitude of the random effect on a background of the distribution of means. This gives an indication of what benefits could be captured if the random effect is reduced.

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