Descriptive Statistics: time Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3 Maximum
time 24 0 20.75 1.09 5.32 10.76 17.79 19.99 23.90 33.96
You do not need to print out any of the graphs that you generate for this activity. Just look at them on the screen and answer questions based off of them in the activity. There is one graph (the histogram) that you need to copy onto your paper.
This step can be skipped if you go to File / Open Worksheet and open the walk.mtw file the instructor created for you. This section is still good reading for you to learn about creating worksheets, though.
There is a little bit of setup that you need to do before entering the data.
Minitab can also help by creating some of the values for you.
Once you have your data entered into Minitab, you may work with it. One of the most common things we will do is display the descriptive statistics. This screen will give you the following statistics by default.
You can change the statistics that are given by clicking on the statistics button. In particular, the N* and SE Mean won't be used right now. The SE Mean will be used in later chapters, but the number of missing cases is rarely used. Other options in the statistics menu that we will use occasionally are the variance, range, interquartile range, and sum of squares.
You may describe more than one variable at a time. However, in this problem, we only have one variable, time, that we want to describe. The other two variables are categorical variables used for classification purposes only, it would make no sense to describe them. Sample output from the descriptive statistics command is shown in the figure.
This is the way to describe the time for all of the teams and all of the heats.
You should get some output that looks something like this.
Descriptive Statistics: time Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3 Maximum
time 24 0 20.75 1.09 5.32 10.76 17.79 19.99 23.90 33.96
This is the way to describe the time for each of the heats. We use the "By
Variable" option to do this. The column used for the By Variable should
be a categorical variable such as the gender, race, age group (but not age
as a number), or heat number. There should be few categories for this variable,
do not use variables that have large numbers of unique values for the By Variable.
Do not use measurement variables (height, weight, age, time) as the by variable.
A box plot is a way to graphically explore the data. Choose the variable
you want to describe as the y variable and the way you want to group the data
by the classification variable x. The box plot does not normally have the
mean on it, but we will add it here for reference purposes.
A histogram is a good way to look the data and see where it lies. We can
also use it to let Minitab count the number in each group for us, rather than
us having to do it manually.
Normally, we would let Minitab just automatically assign groups for us, but in this case, we're specifically looking for bars that are one standard deviation wide. That means that we're going to have to do some extra work that we wouldn't normally have to do.
For this example, let's assume that the mean is 20.75 and the standard deviation is 5.32. Find the mean minus three times the standard deviation and the mean plus three times the standard deviation: 20.75 - 3(5.32) = 4.79 and 20.75 + 3(5.32) = 36.71. These numbers correspond to our lowest and highest class boundaries and will be used later.
The old version of Minitab would allow you to set all kinds of options
before you generated the graph. The new version allows you to look at
the graph and then play with the settings. There's arguments in favor of
both
directions, but for most people, the new way is probably better. The rest
of this will involve changing the graph to give us what we want to have.
Since the file "walk.mtw" was already provided for you, you will 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 and had to go back.
Be sure to save your work! This allows you to go through and work on the activity incrementally (you can do part of it one day and finish it another day). All open windows are saved when you save the project, but if you close a graph, it won't be saved. When you are completely done with the project, you may wish to close the graphs. This will make the files smaller and keep us from running out of room on the drive.