Scatterplot with Y axis вЂњon topвЂќ, along with data labelsвЂ¦ IвЂ™m not that familiar with more than the basic data analysis add-in for Excel, but Minitab out of the box вЂњexcelsвЂќ at graphical analysis.

the ability to overlay distribution curves, the ability to sub-set the data graphed, smooth ability to handle missing data, etc.

statistically powerful graphs вЂ“ ECDF graphs, Box Plots, Individual Values Plots, Matrix Plots, etc.

proper histograms without having to define bin ranges!.

no need to adjust data ranges for graphs if your dataset grows.

the ability to easily create panelled graphs and multiple graphs from one dialog.

These opportunities are often solved in practical ways without the need for formal statistical methods.Д¬onsider the benefits Minitab brings to graphical analysis: From my experience in manufacturing, graphical methods uncover most of the opportunities which get pursued. Graphical analysis is at the heart of the practice of statistics. Here are some thoughts I posted in another forum: One example is the power of the вЂњwordвЂќ function mentioned in the previous post. If you are вЂњfluentвЂќ in both you may find yourself using Minitab more than you thought for data manipulation. Think it out and get advice from qualified statistical types if unsure.I am a fan of Minitab(r) and Excel(r) but which to use? Probably be a bit off for capability studies.Д«ottom line, donвЂ™t be a tool slave to any particular method or assumption. and other techniques which are robust to normality. I suspect it would be fine for t-test, ANOVA, control charting etc. If you graph this distribution as histogram with a bunch of points (10000 or so) you can see that it is not terribly skewed. 100 will not on AD test nor KJ, but will on RS. You can have too much if the underlying data is better fit by another distribution. You canвЂ™t have too much data if the test distribution is a theoretical data. These tests are far less sensitive to extrema. The RJ and KS tests pass the distribution. The AD test under stat>basic stat>normality test fails. The Anderson darling test that youВ must beВ using is very sensitive to departures in the tail. Having said all that вЂ“ if your goal is to show how the histogram of the die tossing broadly forms the shape of a normal distribution, i wouldnвЂ™t see any issue other than the above caveats.ДЄs others have said, your problem is in the lack of extreme values. With that in mind you may want to reconsider your last question. This, of course is less obvious with fewer samples.If you take the time to look at the data on a normal probability plot you will immediately see the issue. Approximately 35 of the time you will get a вЂњ2вЂќ or a вЂњ12вЂќ, there is nothing outside these limits вЂ“ quite a difference from a normal distribution. Furthermore, The AD statistic is more heavily biased towards the tails of the distribution. You do not have values between these integers (eg 2.25, 2.5, 2.63 or whatever. Re-read BCвЂ™s response вЂ“ what you have is a histogram with discrete bins (2,3,4вЂ¦12).

The point is that your particular example of tossing two dice does not really represent a normal distribution. There is no time when there is вЂњtoo much data when testing for normalityвЂќ.

Mike,Your questions at the end of your post would concern me a bit.

Minitab express vs minitab express 1.3