Quick Start to StatTools

Quick Start to StatTools
Step 1: Review the Data and Plan the Analysis
Before you can perform any intelligent statistical analysis on a data set, you must first understand the
data. Data sets are typically arranged in a rectangular array as you see here, where each column
corresponds to a variable, each row corresponds to an observation (or sometime called a case), and
variable names are usually listed in the top row. In this data set, each row has information about a bank
employee, and the variables provide information about the employees.
Variables are either categorical or numeric. In this data set, Gender is clearly categorical because it
contains text. With categorical variables, it is often a good idea to create 0-1, or dummy, variables. This
has been done in column D, where an IF function has been used to record 1 for females and 0 for males.
The variables Age, Years (of experience), and Salary are numeric. Generally, you perform different types
of analysis on categorical variables than on numeric variables, so you should make sure you understand
which is which. By the way, the “variable” Employee in column B is simply an index, so you are unlikely
to include it in any statistical analysis.
Next, you should plan the overall analysis. What do you want to learn about this bank data? You might
want to study the variables one at a time. For example, you might want to know the breakdown of
males versus females, the average age of all employees, or the distribution of salaries. You then might
want to explore relationships between the variables. For example, you might want to know whether
males tend to have more years of experience than females, or you might want to explore whether older
employees make higher salaries than younger employees. You might then want to involve all of the
variables in a regression analysis to see whether females make lower salaries than males, after
controlling for age and years of experience.
Obviously, there is not one single “right answer” on how to analyze such a data set. However, the
questions you ask lead naturally to the types of StatTools procedures you will employ, so you should
take time to formulate good questions.

Now it’s your turn
Take a quick look at the data in this data set. Are there any missing data (blanks)? Do there appear to be
any data values that are “wrong” (out of the ordinary)? Actually, this data set is “clean,” but these are
the types of questions you should ask about real data sets before you dive in.
Step 2: Create a StatTools Data Set
Before you can analyze any data set with StatTools, you must first perform one very simple step: You
must create a StatTools data set.
To create a StatTools data set:
Select any cell inside the data set. Then click the Data Set Manager button on the StatTools
ribbon. Respond Yes to whether you want to create a StatTools data set, and you will see the
following dialog box. It provides quite a few options, such as giving a descriptive name to the
data set (like Bank Data), changing the data range (although StatTools usually guesses it
correctly), and applying cell formatting (for emphasis). There are also options in the Variables
section, but you can generally accept the defaults and then click OK.
Once you click OK, you will see several changes:
If you checked Apply Cell Formatting, the top row of the data set will be painted blue, and other
minor formatting changes will be apparent. This reminds you that this is a StatTools data set.
The columns will have range names, such as ST_Gender, ST_Age, and so on.

Most importantly, you will be able to perform StatTools statistical procedures on the data set, as
explained shortly. In fact, the rest of this tutorial works as shown only if you have created the
StatTools data set in this step.
If you save the file and then reopen it, the StatTools data set will still be in effect. This means that you
have to perform this step only once per data set. However, you are allowed to create multiple StatTools
data sets in the same file. This is a good reason for giving each a descriptive name.
Now it’s your turn.
Create a StatTools data set for the bank data.
Step 3: Perform One-Variable Analyses
The rest of the steps in this tutorial indicate a logical progression of analyses you might perform on this
data set and on many other data sets. You don’t necessarily have to follow this exact progression, but
keep in mind that you are using StatTools to learn about the data. Therefore, this progression is natural.
A good first step is to examine the variables one at a time. You can do this with tables of summary
statistics, such as means, standard deviations, quartiles, and others, or you can do it with charts, the
most common being a histogram. You can do all of this quickly and easily with StatTools.
To see summary statistics for one or more variables:
Select One-Variable Summary from the Summary Statistics dropdown list, and in the resulting
dialog box, check the variables and summary statistics of interest to you. Those checked in the
dialog box below are recommended for now. Actually, StatTools will ignore choices of
categorical variables such as Gender; it requires numeric variables here. Also, if you happen to
see two columns of check boxes in the variable list section, click the Format button and select
Unstacked. This purpose of this option will be explained shortly.

This dialog box is typical of most other StatTools dialog boxes. The top section lets you choose a data set
and check one or more variables of interest. The bottom section lets you choose how you want the
analysis to be performed. Note the three buttons at the bottom. You will see these buttons in all
StatTools dialog boxes.
The left button is for help. The middle button allows you to save selections for later analyses. For
example, if you typically want only the summary statistics checked here, you can save these choices for
later one-variable analyses. The right button is especially useful. It brings up the following Application
Settings dialog box where, among other things, you can choose from four possible report placements.
For example, if you want each report to be placed on a new worksheet in the current workbook, you can
select ActiveWorkbook.

Here are the resulting summary statistics.
For example, the mean and standard deviation of Salary are $63,875 and $18,010, and 75% of the
employees’ salaries are below $70,400, the 3 rd quartile. Also, the mean of the 0-1 Female variable
implies that about 67% of the employees are females.
To see the distribution of one or more variables graphically, you can create histograms.
To create a histogram of one or more variables:
Select Histogram from the Summary Graphs dropdown list, and select the variables of interest
(again, numeric only). Those checked in the dialog box below are recommended for now. Each
histogram is a column chart of counts of observations in various categories, called bins. The
bottom of this dialog box indicates that StatTools can choose these bins automatically, and this
is often a good choice, although you can supply your own bins if you like. (Again, if you see two
columns of check boxes in the variable list section, click the Format button and select
Unstacked.)
This produces a worksheet with four histograms, one for each checked variable. For example, here is the
histogram of Salary. As you can see, this distribution is skewed to the right. Most employees are in the
lower salary ranges, but a few make considerably higher salaries.

Now it’s your turn
Create the summary statistics and histograms illustrated above. Experiment with report placements. You
can change this report placement setting as often as you like—or you can always stick with your
favorite.
Step 4: Examine Relationships Between Variables
Once you understand how each variable varies individually, using the tools in the previous step, you can
then look for relationships between variables. Three tools will be illustrated here: (1) a table of summary
statistics for a numeric variable, broken down by categories of a categorical variable, (2) side-by-side box
plots for seeing how the distribution of a numeric variable varies over categories of a categorical
variable, and (3) scatterplots of numeric variables versus other numeric variables. Again, StatTools
performs all of these quickly and easily, so you have time to explore plenty of potential relationships.
First, you might want to break the Salary summary statistics down by Gender. To do this in StatTools,
you first need to understand the difference between stacked and unstacked formats. The data would be
unstacked if there were two separate Salary columns, one for males and one for females. However, this
data set is stacked, meaning that there are two “long” variables, Salary and Gender. The male and
females salaries are stacked into one Salary column, and the Gender column indicates the category of
each.
To get summary statistics of one variable broken down by category:
In the One-Variable Summary dialog box, click the Format button and choose Stacked. This
creates two lists of check boxes, one for the “Cat” (or categorical) variable and one for the “Val”
(or value) variable. Check Gender for the categorical variable, and Salary for the value variable.
Then click OK.

The resulting summary statistics are as follows. For example, the means and standard deviations
indicate that male salaries are larger on average, and are more spread out, than female salaries.
Second, you might want to compare the male and female salary distributions graphically. You can do
this with histograms in StatTools, again using the Stacked format option, but unless you change the bins
for these two histograms, they will be on slightly different scales, which makes a comparison across
gender difficult. A better way to compare salaries across gender is with side-by-side box-whisker plots
(commonly shortened to box plots).
To create side-by-side box plots:
Select Box-Whisker Plot from the Summary Graphs dropdown list, select Stacked as the Format,
and choose the Cat and Val variables as before.

Here is the result. You can read more about box plots in StatTools help, but this plot makes it clear that
females tend to make smaller salaries than males (the female box is slightly to the left), and their
salaries tend to have less variation the male salaries (the female box is narrower). Note that the red
points are called outliers.
Third, you might want to see whether there is a relationship between Age and Salary, or between Years
(of experience) and Salary. The best way to do this graphically is with scatterplots.
To create one or more scatterplots:
Select Scatterplot from the Summary Graphs dropdown list. As shown in the following dialog
box, you can select any number of variables for the X axis and any for the Y axis. Those checked
in the dialog box below are recommended for now. For these selections, you will get two
scatterplots: Salary (Y) versus Age (X), and Salary (Y) versus Years (X). You can also see the
correlations between the variables below the scatterplots by checking the circled option.

Here are the two scatterplots. Both variables Age and Years are positively correlated with Salary, but
Years is more strongly related to Salary. You can see this from the tighter upward scatter in the second
plot, and from the higher correlation shown at the bottom (0.616 for Years versus 0.384 for Age). Of
course, both of these make sense. The chances are that a given employee’s salary will increase as he or
she gets older and gains more experience.

Now it’s your turn.
Perform the statistical analyses shown here: summary statistics of Salary broken down by Gender, box
plots of Salary broken down by Gender, and scatterplots of Salary versus Age and Years. Of course, you
can try a few more on your own if you like. By this time, you should be seeing that you can do a lot of
analysis with StatTools very quickly.
Step 5: Perform Further Analyses
The advantage of using StatTools is that it lets you perform many statistical analyses very quickly and
easily. As mentioned earlier, it is up to you to ask the right questions about the data set and then run
the appropriate StatTools procedures. Of course, these depend on the objectives of the analysis.
However, StatTools has many tools, some relatively simple and others more complex, for analyzing data.
One particular tool will be examined here: regression analysis.
So far, you have seen that on average, the male salaries are larger than the female salaries. This clearly
raises the question of gender discrimination at this bank. But the bank might provide a counterargument
that males get paid more because they are older and have more years of experience. The analysis so far
has not “controlled” for these two other variables when comparing males to females, but regression
can. And best of all, StatTools can run one or more regressions in a matter of seconds.
To run a regression:
Select Regression from the Regression and Classification dropdown list to bring up the
following dialog box. There are many options here, but to keep it simple for this quick start
tutorial, you can accept all of the defaults and simply choose the dependent (D) variable and
independent (I) variables as shown.

This immediately produces the following results.

Although this is a lot to digest, and you need some background in regression analysis to understand it
all, two key numbers have been circled. First, the R-Square value indicates that about 49% of the
variation in salaries has been explained by the variables in the regression equation (gender, age, and
years of experience). Second, the coefficient of the Female variable indicates that, even after controlling
for age and years of experience, females average close to $13,000 less in salary than males. (By this
time, you might have noticed that the 0-1 Female variable was created in the first place so that it could
be entered in this regression equation for Salary. Otherwise, it wouldn’t be possible to measure the
effect of Gender on Salary with regression.)
Now it’s your turn.
Run this regression analysis or other statistical analyses with StatTools, and using your knowledge of
statistics, interpret the results.
More StatTools Examples
This completes the tutorial. However, we urge you to learn more. One way is to examine the example
models packaged with StatTools. To see them, choose Example Spreadsheets from the Help dropdown
list. This opens a file with a variety models that illustrate the statistical procedures available in StatTools.
To see any of them, just click the corresponding link.
In addition, there is extensive documentation on all StatTools features under the Help dropdown list,
there are many more resources on the Palisade web site www.palisade.com, and Palisade offers a range
of training, consulting, and customization services. We hope these will help you to become one of the
increasing number of people who are able to make sense of all the data in today’s data-driven world.