

Minitab  Correlation
The Minitab Worksheet is available. It contains the following data:
Column N Comments Math 5 scores on a 20 point quiz. Science 5 scores on a 20 point quiz. Beauty 5 ranks on beauty (1=most) Sociability 5 ranks on beauty (1=most) Beautyrk 5 ranks on beauty (5=most) SciRank 5 ranks on science quiz (5=most/best)
First have Minitab creat the scatterplot for the Math and Science variables. Use the "Scatter Plot..." command off of the "Graph" menu. That is:
That will take you to the following dialog box:
Choose "Simple". Then you will see the following dialog box:
Type in (or double click them from the left hand menu) Math and Science for the "Y and X variables". The output will look like:
Then compute the correlation. Use the "Correlation..." command off of the "Stat, Basic Statistics" menu. That is:
That will take you to the following dialog box:
Type in (or double click them from the left hand menu) Math and Science. Also, make sure that "Display pvalues" and "Store matrix" are unchecked for now. The output will look like this:
Correlations: Math, Science Pearson correlation of Math and Science = 0.845
Spearman's Rho  Example 1
This time we are interested in the correlation of Beauty and Sociability. First have Minitab creat the scatterplot as we did in the Pearson r example above. The resulting graph will look like:
Since when Pearson's r is computed on ranked data it gives the same value as Spearman's Rho, we compute the correlation in the same way that we did for Pearson's r. That is:
Correlations: Beauty, Sociab Pearson correlation of Beauty and Sociab = 0.800
Spearman's Rho  Example 2
First we need to have Minitab rank the Science data. We will use the calculator in Minitab to generate that variable. Use the "Calculator..." command off of the "Calc" menu. That is:
That will take you to the following dialog box:
Type in (or double click on) SciRank for the "Store result in variable:" box. For the "Expression:", you can type or click on the necessary values. Minitab has lots of useful "Functions:". We can also rerank Beauty (so the numbers go from low to high) using the transformation indicated below. It simply flips the ranking.
Now that both variables are ranked, we can compute the scatterplot as we have previously. The resulting graph will look like:
Then compute the correlation. The output will look like this:
Correlations: BeautyRk, SciRank Pearson correlation of BeautyRk and SciRank = 0.300