## Introduction to Minitab

* These instructions are written for *
*Minitab version 14.*

- Minitab Windows
- Opening, Closing, Saving
- Entering Data
- Dialog Boxes
- Calculator
- Managing Graphs
- Manipulating Data in the Data Window

- Stem plots
- Pie Charts
- Bar Charts
- Histograms
- Enhancing Your Histogram
- Box Plots
- Descriptive Statistics
- Standardizing Data
- Normal Probability Calculations
- Normal Quantile (Probability) Plot

- Random Samples from a Data Column
- Random Digits
- Bernoulli Trials
- Probability Densities
- Normal Probability Density
- Binomial Distribution

- Confidence Interval for Mean
- Z-Test for Mean
- t-Test for Mean
- Paired t-Test for 2 Means
- t-Test for 2 Means
- Sign Test
- Rank Sum Test (Wilcoxon Test)
- Proportions
- Chi Squared Test for Independence
- Inference for Regression

When you open Minitab (Mtb), you find a **session window **in
the top half of the main window and the **data window **in the
bottom half. There is also a **project manager window **which
is usually hidden. To see the project manager window,
click on **Window **at the very top of the main window and
check Project Manager.

To activate one of these windows, click on it. The toolbars
at the top changes depending on the active window.

The main window menu bar has the usual menu items (**File**
for opening, closing, and saving; **Edit **for copying,
pasting, and deleting) and specific menu items for **Data**
manipulation, **Calc**ulation, **Graph**ing, and **Stat**istics.

The **Editor **menu item allows you to **Enable Commands **in
the session window and to make its **Output Editable**.
If you wish to clean up the session window by deleting e.g. error
messages and adding comments, you should uncheck **Enable
Commands** and check **Output Editable**. The **Tools**
item provides access to the Windows calculator, Windows Explorer,
and Notepad. It also allows you to customize all other menu
items, e.g. to add statistical routines or custom graph routines
to the menu.

Click on **File **in the main Mtb window to open Mtb
files. You can open Mtb projects (file extension .mtj), Mtb
worksheets (file extensions .mtw, .mtb, .mtp and others), and Mtb
graphs (file extension .mgf). You can also open spreadsheets and
text files. Usually you'll be interested in projects and
worksheets.

Worksheetsare essentially data files in spreadsheet format. Opening them creates a new data window. No other information is loaded into Mtb. Several worksheets can be open at the same time.

Projectscontain also records of what happened in the session window, graphs, a list of all variables that are currently stored, possibly a report in MS Word format etc. You can open only one project at a time.

To save a worksheet, click on **File > Save Current
Worksheet **or **File > Save Current Worksheet As ... **and
follow the prompts. Have a USB drive or some other
media ready to save your worksheet.

To save a project, click on **File > Save Project **and
follow the prompts. When
you save the project, you save all the information about your
work: the contents of all the windows, including the columns of
data in each Data window, stored constants and matrices, the
complete text in the Session window and History folder, and each
Graph window. This allows you to interrupt your work and
pick up later where you left off.

Activate the data window by clicking on it.

Check to insure that the arrow in the box in the upper left
corner of the data window page is pointing downward (click on it
to switch it).

Place the name of your variable in the top cell of the column
(directly under C1, or whatever column you put your data
in). Move the cursor to the first cell in your column
and enter your first data value. Press Enter. If the
arrow in the upper left corner is pointing down, the cursor will
automatically move to the next cell in the column. You can also
use the up and down arrows or the mouse to move to other cells.
Continue until you have all the values of that variable entered
into that column. Don't leave any empty cells.

Move to the next column and repeat the steps with your next
variable.

Minitab considers a data column as __numerical__ as long as
all entries in its cells are numbers. If one or more cells are
non-numerical (text, symbols), the entire data column is
considered __categorical__, and the column label is changed
from e.g. C2 to C2-T. Integer entries with spaces are interpreted
as __dates__, and the column label is changed from C2 to C2-D.
*It can be tedious to undo such a change in data type, so be
careful when entering data. *

Data types can be changed by going to **Data > Change
Data Type**.

To copy data (cells, groups of cells or columns) within a Mtb
data window or between data windows, select the cells with the
mouse. You can also select a group of columns (highlight the
column names instead of the cells).

Go to **Edit > Copy Cells.**

Move the mouse to the location where you want to enter the data
and go to **Edit > Paste Cells.**

You can also copy and paste data to and from other applications
(spreadsheet columns, text files) in this manner.

**Caution: **

**Copying data columns from an HTML file or a Notepad (text) file into Mtb may destroy the alignment of the columns.****Some data files on textbook CDs may have data that look numerical but are interpreted as categorical data when copied into Mtb, due to embedded spaces etc. The solution is to clean up the file using Notepad and then copy it into Minitab.**

These boxes pop up when you choose submenus from the **Data,
Calc, Stat,** and **Graph **menus.

All dialog boxes list available variables in a big window on the
left and expect input in smaller fields on the right.

Click in a Variables input field to activate it.

Select one or more variables from the list on the left by typing
its name in the input field or by highlighting it and clicking on
the Select button. Double-clicking on the variable name also
works.

Other fields expect you to enter the location of output data or
to enter numerical values. Radio buttons or check boxes may
change the options for input. Click on OK when you are done.

Click in the **Graph Variables **input field to activate
it.

Select one or more variables from the list on the left by typing
in its name or by highlighting it and clicking on the Select
button.

Design your graph with the **Data View **window (e.g. symbols
or connected points or bar graphs). Available choices appear when
you click on the arrow symbols. **Scale**, **Label**,
and **Data Options** provide ways to add titles and
legends, control the axes, spacing of tick marks, grids etc..

To generate **Multiple Graphs**, click on the
corresponding button **Frame > Multiple Graphs**. By
clicking on the appropriate radio button, you can overlay several
graphs on the same page (e.g. side by side box plots).

Click on OK when you are done.

Go to **Graph > Stem-and-Leaf**.

Use the Graph Dialog window to select
the desired variable.

Within the dialog box you can choose to trim outliers (click in
box) or adjust the stem increments (i.e. split or combine stems).

Click OK to generate the plot.

The plot will appear in the Session window.

Go to **Graph > Pie Chart**.

Choose between charting raw data and values from a table.

- Raw data should be stored in one or several columns as lists of symbols or numbers. All data are interpreted as categorical. Enter the column(s) that contain the lists and click OK. Mtb determines the count (number of occurences) for each symbol and makes a pie chart with a legend. If several data columns are chosen, several pie charts are made in the same graph or on separate graphs, depending on the setting chosen from the Multiple Graph window.
- A one-way table should be stored as a column of labels for the categorical variables and one or several columns that contain the summary variables (numerical levels for each categorical variable), properly lined up. Enter the categorical variable and the summary variables and click OK. Mtb makes one or more pie charts with legends on the same graph, depending on how many summary data columns are chosen.

Go to **Graph > Bar Chart. **Choose whether
your data represent raw data ("counts of unique
values") or values in a table. Then choose the type of bar
chart you want to produce.

- Raw data ("counts of unique values") should be
stored in a column as a list of symbols (letters, words,
numbers...). All data are interpreted as categorical.
*Thus the values 2, 2.0, 2.000 all are counted separately.*Enter the column label or name that contain the list and click OK. Mtb determines the count (number of occurences) for each symbol and makes a bar chart with a legend. - A one-way table should be stored as a column of labels for the categorical variables and one or several columns that contain the summary variables (numerical levels for each categorical variable), properly lined up. Enter the categorical variable and the summary variables and click OK. Mtb makes one or more bar charts with legends on separate graphs, depending on how many summary data columns are chosen.

Your two-way table may be in a single column (say in the column DATA) , with classification variables for the table rows and columns in two other columns (say in columns ROW and COLUMN). Alternatively it may in the form of several columns, e.g. with labels DATA1 and DATA2, properly lined up, with an additional column, say ROWS, for the row labels.

- If the data are in a single column, with two columns for
classification, go to
**Graph > Bar Chart**. Choose "values from a table". Choose any of the three bar chart types in the upper row, for example "Cluster", and click OK. Enter the name of the column containing the numerical values as Graph variable and the columns for classification as Categorical variable. Then click OK.*This will produce clusters of bar graphs and will put them all in the same window. If you want to see the bar graphs clustered by the other variable, reverse the order in which the categorical variables have been entered. Experiment with other choices to produce the bar chart that serves your needs.* - If the data are in a several columns with labels, with
one column for the row labels, go to
**Graph > Bar Chart**. Choose "values from a table". Choose any of the two bar chart types in the lower row, for example "Stack", and click OK. Enter the names of the columns containing the numerical values as Graph variables and the column for Row Labels. Choose the Table Arrangement and the way the cateory values are to be stacked. Then click OK.*This will produce stacked bar graphs and will put them all in the same window. Experiment with other choices to produce the bar chart that serves your needs*

This can be done either by using the Data View, Scale, Labels, Data Options buttons in the main dialog window before the chart has been made, or by editing portions of the chart after it has been made. See the section on changing the look of a histogram.

*If the chart doesn't look the way you want it, close it and
redo it. All previous settings are still in the dialog
boxes. Change a few things at a time until the chart looks
right.*

Go to **Graph > Histogram**.

Choose the type of histogram and click OK..

In the Graph Dialog box, select
the variable(s) for which you want to make a histogram. Note that
you can choose several variables at once, so it is possible to
make histograms of several different variables simultaneously.
These will usually appear in separate graph windows, unless you
choose to have graphs overlaid on the same page in **Multiple
Graphs.**

Back to top

You can change the appearance of a histogram by clicking on the appropriate buttons in the graph dialog window.

- Choose between histograms for frequency, relative frequency (percent) and density by clicking on Scale > Y-Scale Type. You can also make a cumulative histogram with this option.

You can also change the histogram after it has been made by clicking on the bars, the title, the axes, the axis labels etc. and editing them. For example,

- the fill type and the color of bars can be changed (click on a bar > Edit Bars > Attributes)
- the width and location of histogram intervals can be changed (click on a bar > Edit Bars > Binning)
- titles and their font can be changed (double-click on the title)
- axis labels and their font can be changed (double-click on a label)

Go to **Graph > Boxplot**.

Choose a graph type from the top or bottom row. "Simple
Y" here means that the data are in a single column,
"Multiple Y's" means several columns. Choose "With
Groups" if there is an additional column of categorical
variables for classification. This will allow you to make
side-by-side boxplots for comparison.

Enter the column name(s) for the data in the graph dialog window. Use the buttons in the dialig window to change the overall appearance and press OK.

Boxplots can also be edited after they have been made - double-click on the title, the box, axes and labels, or the entire window to bring up options for changing the appearance.

A graph can be moved around on the screen, its size may be
changed as any other window, and it may be closed. Once a
graph has been closed, it is gone and must be recreated. In
addition, the menu bar item **Window **will allow
you to manage the histograms. With **Window > Close all
Graphs** you can clean up a cluttered work area.

To copy a graph to a report (e.g. to MS Word), righ-click on it
and select **Copy Graph**, then paste the graph into
your report. In MS Word it is possible to resize the graph
easily.

Go to **Stat > Basic Statistics > Diplay Descriptive
Statistics**.

Enter your desired variable(s) in “Variable” box.

Click OK.

The descriptive statistics (number of observations, mean, median,
maximum, minimum, quartiles, standard deviation, and a few
others) will appear in the Session window.

By clicking on the Graphs button in the dialog window, you can produce histograms or boxplots.

Go to **Calc > Calculator**.

You can enter calculator operations for variables from the left
window in the **Expression **window and store the result in
another column. You can also use the functions from the
list (highlight and click **Select**).

Start by putting a title on the new column you want to form.

Go to **Calc > Calculator**.

In the **Expression** box, enter the formula for the
transformation.

Enter the name of the new column in the **Store result in
variable** box and click OK.

*Example:*

*To transform centigrade temperatures (stored in C1) to
Fahrenheit and store the Fahrenheit data in C2, go to **Calc
> Calculator**. In the **Store results
in variable** box, enter C2. In the **Expression**
box, enter 32 + (9/5)*C1. Then click OK. Column C2
will now contain the Fahrenheit temperatures.*

Go to **Calc > Standardize.**

Select the data columns you want to standardize in the Dialog box and enter the column where the
standardized columns go. Leave the radio button at "subtract
mean and divide by standard deviation". Click OK.

Go to **Calc > Probability Distributions > Normal.**

To find the probability that a variable with a normal
distribution is less than a certain value, say X, click the radio
button "Cumulative probability".

Enter the mean and standard deviation.

Click the button "Input constant" and enter the value X
in the field.

Click OK.

The answer appears in the session window.

To find a value **x** such that a variable with a
normal distribution is less than **x** with given
probability p, click the radio button "Inverse
cumulative probability".

Enter the mean and standard deviation.

Click the button "Input constant" and enter the
probability p in the field.

Click OK.

The answer **x** appears in the session window.

To produce a normal quantile plot, go to **Graph >
Probability Plot**. Select whether your data are in a
single column (for a single plot) or if there are several data
columns and press OK.

Enter the name of the column(s) in the dialog window and press OK again.

The result is a normal quantile plot, flipped about the diagonal: The normal probabilities (probabilities for z-scores) are plotted against the data. The plot also has a straight line and error boundaries for comparison.

Alternatively, go to **Stat > Basic Statistics >
Normality Test**.Enter the name of the column with the
data, leave everything else at the default setting, and press OK.

The result is the same plot, with slightly changed scales and without error boundaries.

Go to **Graph > Scatterplot**.

Choose the type of scatterplot you wish to make.

In the Graph dialog box, enter the
column number or the name of the explanatory variable in the X
box and the column number of name of the response variable in the
Y box.

Leave all other options at their default setting and click OK.

- For multiple scatterplots on the same page, enter the
explanatory variables in the X box and the response
variables in the Y box. Then choose
**Frame > Multiple Graphs > Overlaid on the same graph**. Mtb will choose different symbols for the different plots by default. You can changes these using the**Edit Attributes**button. - For scatterplot with a third (categorical) variable, choose "With Groups" in the selection screen. The graph dialog window will have a field where you can enter the categorical variable. Mtb will make a scatterplot with different symbols.
- Click on points in the scatterplot to see where they are in the dataset.

To obtain the correlation coefficient between two quantitative
variables without using the “Scatterplot” instruction,
go to **Stat > Basic Statistics > Correlation**.

Under **Variables**, select the two columns of data and click
on OK.

Go to **Stat > Regression > Regression**.

In the Dialog Box, enter your response
variable (y-axis) in the box marked **Response **and your
explanatory variable (x-axis) in the box marked **Predictors**.
Click OK to read off the equation for the regression line in the
session window.

*Note that in the “Predictors:” box there is room
for more than one explanatory variable. We can enter more
than one explanatory variables to do multiple regression. *

While you are still in the Regression Dialog Box and before
you move on to obtain the regression analysis, you can store and
plot the residuals:

Click on Storage > Residuals and click on OK.

Back in the Regression Dialog Box, click on Graphs.

In the ensuing dialog box (Regression - Graphs), enter your
explanatory variable in the large box at the bottom: **Residuals
versus the variables **and click on OK

At this point Minitab will compute the regression analysis and draw the residual plot. The regression analysis will appear in the session window and the residual plot will appear immediately thereafter as a separate graph. The residuals will be stored in a new column called RESI1 in the worksheet.

*This approach gives the least squares regression equation,
the coefficients a and b, the coefficient of determination R*^{2}*,
and information on “unusual” observations (outliers)
and influential observations, but not the regression line.*

To obtain a graph with the least squares regression line
fitted to the scatterplot:

Go to **Stat > Regression > Fitted Line Plot**.

In the dialog box, select the response and explanatory
variables in the appropriate boxes.

If you haven't calculated the residuals yet, go to **Storage
> Residuals **and click OK.

Click OK to obtain the scatterplot with the regression line.

The regression analysis will appear in the session
window. You will obtain the regression equation, the
coefficients a and b, and the coefficient of determination. After
this information appears in the session window, Minitab will draw
the scatterplot with the least squares regression line, the
regression equation, and R^{2}.

If you want to see the Residual Plot at this time, activate
the data window by clicking on it and click on the **Edit Last
Dialog** icon, to bring back the **Fitted Line Plot** dialog
box.

Enter the residual column RESI1 as the **Response Variable**.
Leave the **Predictor** box unchanged.

Click on OK

You will get a graph of the residuals plotted against the
explanatory variable, i.e. a residual plot.

To conduct tests of the null hypotheses that the slope or the
constant term are zero, go to **Stat > Regression >
Regression** and carry out the regression as above. In the
session window, you can read off the p-values for these tests.

Go to **Graph > Time Series Plot**.

Chose the type of graph you want to produce and click OK.

Select your variable(s) in the Series window. Mtb will
assume that the index of the data is the time variable. You can
change this in the graph dialog window by clicking on Time/Scale.

The **Data View **button allows you to change the appearance
of the time plot and to make a smoothed scatterplot.

__If your two-way table is in a single column, with classification variables
for the table rows and columns in two other columns:__

Go to **Stat > Tables > Cross Tabulation** **and
Chi-Square**.

Select the two variables for rows and columns.in the **Classification
variables **window. Enter the column containing the
frequencies.

Check **Count, Row Percent,** etc., as you desire and click on
OK. The results will be displayed in the session window.

To do a c^{2} test for independence for
data in this form, click on **Chi -Square** in the dialog window and check
Chi-Square analysis as well as any other details you wish to display. Then click
OK.

__If your two-way table is is already stored in table form in the worksheet:__

Go to **Stat > Tables > Chi-Square test (Table in Worksheet)**.

Enter the columns that contain the table and click on OK.Select the two
variables for rows and columns.in the **Classification variables **window.

The results of the Chi-Square test for independence will be displayed in the
session window (degrees of freedom, expected cell counts, cell contributions to
c^{2}, overall value of
c^{2}, p-value).

*To compute row percents and column percents, stack the columns of the
table with Data > Stack > Columns to turn the table into a single column. Make
sure there are row and column subscripts in two separate columns (Minitab will
make the column subscripts automatically). Then proceed as above.
*

Go to **Data > Copy > Columns** **to Columns**.

Enter the columns to be copied in and their destination.

Go to **Data > Copy > Columns** **to Columns**.

After entering the columns to be copied and the destination in
the dialog window, click on Subset and enter the rows to be used
or to be excluded.

Go to **Data > Stack **and choose **Columns **or **Blocks
of Columns **or** Rows..**

Select the columns you want to stack and their destination
columns in the dialog box.

You can keep track of the origin of the stacked columns by **storing
subscripts **in another column.

This is the same as "separating column data according to categories or characteristics".

Go to **Data > Unstack Columns.**

In the window **Unstack the data in **select the column you
want to separate.

In the **Using subscripts in** window, enter the column
containing the category or characteristic according to which you
want to separate the data.

Enter where you want to store the unstacked data, either in a **new
worksheet **or **after the last column in use**.

This can be done by first unstacking a block of columns by category and then deleting all columns that are not needed.

To draw a simple random sample from data stored in a column,
go to **Calc > Random Data > Sample from Columns**.

Select the column you want to sample from, enter the size of your
sample (**Sample ... rows from column**) and enter the column
where the samples are to be stored.

Choose whether you want to sample with or without replacement
(check box) and click OK.

To make a list of random digits, go to **Calc >
Random Data > Integer**.

Enter how many (groups of) random digits you need in
"Generate ... rows of digits".

Enter the column where the digits are to be stored.

To get single digits, choose Minimum value = 0 and Maximum value
= 9. To get two-digit groups, choose Minimum value =
0 and Maximum value = 99. Of course, other choices are also
possible.

Click OK to generate the random digits.

Work with the digits just like you would work with random digits
found in a table.

To simulate a series of n Bernoulli trials
(outcomes are either 1 , "success", with
probability p or 0, "failure",
with probability 1-p), go to **Calc > Random Data >
Bernoulli**. Enter the success probability p and
the number n of trials ("Generate n
rows of data"). Then enter the column where the data are to
be stored. To simulate several series of n
trials each, enter several column locations, one for each series.

To make a plot of a density curve, start a fresh worksheet (**File
> New > Worksheet**). Let's say you want to make a
density curve for a < x < b.

- Go to
**Calc > Make Patterned Data > Simple Set of Numbers.**Chose**From first value**a and**To last value**b. Then enter the increment (**In steps of**), e.g. 0.1.**Store patterned data in**a column of your choice, e.g. C1 - Now go to
**Calc > Probability Distributions**and pick the distribution you want to work with.

Click the radio button "Probability density".

Enter the parameters required for the distribution.

Click the radio button "Input column" and select C1.

Type C2 into the field "optional storage" and click OK. - Finally make a scatterplot of
C2 versus C1. Use the
**Data View**button to turn off symbols. - To make a plot of a
__normal probability density__N(m,s), use the above steps with first value m-3s and last value m+3s.

To compute an individual binomial probability of k
successes in n trials for a success probability
of p,

Click on **Calc > Probability Distributions >
Binomial,**

click the radio button for **Probability,**

enter the number of trials and the probability of success,

click the radio button for **Input Constant** and enter the
number of successes k,

and click Enter.

The probability of exactly k successes will appear in the session window.

To compute the binomial probability of k or fewer
successes in n trials for a success probability
of p,

Click on **Calc > Probability Distributions >
Binomial,**

click the radio button for **Cumulative Probability,**

enter the number of trials and the probability of success,

click the radio button for **Input Constant** and enter the
number of successes,

and click Enter.

The probability of k or fewer successes will appear in the session window.

To make a binomial probability table for n trials
with a success probability of p , first go to **Calc >
Patterned Data > Simple Set of Numbers,**

store the patterned data in C1 from first value of
0 to last value of n, in steps of 1.

Then go to **Calc > Probability Distributions > Binomial,**

click the radio button for **Probability,**

enter the number of trials and the probability of success,

click the radio button for **Input column **and enter C1,

enter C2 in **Optional Storage**, and click Enter.

Go to **Stat > Basic Statistics > 1 Sample Z...**.

Select the variable for whose mean you want to find a confidence
interval in the dialog window.

Enter the standard deviation.

Click on **Options**, enter the **Confidenc level **(in
percent), and enter and leave the **Alternative** on
**not equal**.

Click OK twice and read off the result in the Session window. You
can choose **Graphs **in the dialog box to illustrate the
results.

Go to **Stat > Basic Statistics > 1 Sample t**.

Select the variable for whose mean you want to find a confidence
interval.

Click on **Options**, enter the **Confidenc
level **(in percent), and enter and leave the **Alternative**
on **not equal**.

Click OK twice and read off the result in the Session window. You
can choose **Graphs **in the dialog box to illustrate the
results.

Go to **Stat > Basic Statistics > 1 Sample Z**.

Select the variable for whose mean you want to conduct the test
and enter the standard deviation.

Enter the value for the null hypothesis in the **Test mean **field
and click on **Options **to choose the **Alternative**

Click OK twice. The results appear in the Session window. You can
choose **Graphs **in the dialog box to illustrate the results.

Go to **Stat > Basic Statistics > 1 Sample t**.

Select the variable for whose mean you want to conduct the test..

Enter the value for the null hypothesis in the **Test mean **field
and click on **Options **to choose the **Alternative**

Click OK twice. The results appear in the Session window. You can
choose **Graphs **in the dialog box to illustrate the results.

Go to **Stat > Basic Statistics > Paired t**.

Select the two variables for whose means you want to conduct the
test..

Click OK. Mtb will conduct a test of the null hypothesis of equal
means against the alternative that the means are not equal. It
will also produce a 95% confidence interval for the difference of
the means. To change any of these settings, go to **Options**.

The results (95% confidence interval and p-value for the
alternative of unequal means) appear in the Session window. You
can choose **Graphs **in the dialog box to illustrate the
results.

Go to **Stat > Basic Statistics > 2 Sample t**.

If all samples are in one column, click the top radio button and
select the variables that contain the data and the subscripts.

If the samples are in two different columns, click the middle radio button and select the columns that contain the samples.

Summarized data can also be entered (lower radio button).

Click on **Options** to select the **alternative**
and to choose the **confidence level **for a confidence
interval. Mtb assumes that the variances are unequal.

Click OK.

The results appear in the Session window, including a p-value and
an estimate for the number of degrees of freedom. You can choose **Graphs
**in the dialog box to illustrate the results.

Go to **Stat > Nonparametrics > 1-Sample Sign Test**.

Enter or choose the variable in the dialog window.

Click the **Test median** radio button and enter the
value for the null hypothesis.

Click OK. The result (p-value) appears in the session window.

Go to **Stat > Nonparametrics > 1-Sample Wilcoxon**.

Enter or choose the variable in the dialog window.

Click the **Test median** radio button and enter the
value for the null hypothesis.

Click OK. The result (p-value and Wilcoxon statistic) appears in
the session window.

Go to **Stat > Basic Statistics > 1 Proportion**.

If the data (1 for success, 0 for failure) are in a data column,
click the top radio button and enter the name of that column in
the top box of the dialog window.

If you have summarized data (number of trials and number of
successes), click the bottom radio button and enter the values.

Click **Options **to choose

- the value for the proportion in the null hypothesis
(default: p
_{0}= 0.5) - what the alternative is (default: p
_{a}not equal p_{0}) - the confidence level for the confidence interval (default: 95%)
- whether you want to use the normal approximation (default: no)

Click OK twice to see the results in the session.

Go to **Stat > Basic Statistics > 2 Proportions**.

If the data (1 for success, 0 for failure) are in one data column
with subscripts in another data column, click the top radio
button and enter the name of the columns containing the samples
and the subscripts.

If the data (1 for success, 0 for failure) are in two data
columns, click the middle radio button and enter the name of the
columns containing the samples.

If you have summarized data (number of trials and number of
successes), click the bottom radio button and enter the values
for the two samples.

Click **Options **to choose

- the value for the difference of the two proportions in the null hypothesis (default: difference = 0)
- what the alternative is (default: difference of proportions is not equal to assigned value)
- the confidence level for the confidence interval (default: 95%)
- whether you want to use a pooled estimate for the values of p (default: no)

Click OK twice to see the results.