Click on the menu item "Graph", then Click on "Probability Plot..."

Accept the default "Single" Just click on the "OK" button.

In the dialog box,

Double click on "C1 Lifetime" to select it into the "Variables" pane, then

Click on the "Labels..." button.

Provide a more meaningful title for the normal probability plot.

Then click on the "OK" button.
Back in the "Probability Plot" window, click on the "OK" button.

The following normal probability plot (or something very like it) then appears:

A random sample of size 100, drawn from a normal distribution, will have all (or nearly all) of its points near the straight line of a normal probability plot.   Only 5% of all points will fall, by chance, outside the two curves on either side of the line.
Clearly this data set has not been drawn from a normal distribution.

Here are some other normal probability plots (as produced by Version 17 of Minitab - mostly the same as Versions 13 to 16 and 18).

100 data drawn from a Normal distribution N(1000, 10²)

Note how nearly all points lie within the two curves, close to a straight line.

100 data drawn from a standard LogNormal distribution.

Note how this plot resembles that for the exponential distribution.

Open Excel and import the data set, for which you wish to create a normal probability plot, into a column.

Check that the Analysis Tool Pack is present, as follows. Click on the menu item "Tools" and check if the item "Data Analysis..." is present in that drop down list. If it is present, then bypass the next step. Otherwise, click on "Add-Ins...".

Check the options for "Analysis ToolPak" and "Analysis ToolPak - VBA" then click on the "OK" button.

Alongside the genuine data, place a column containing the same number of values. The new values can be any set of distinct numbers. They are needed for the next two steps.

Click on the menu item "Tools" and then Click on "Data Analysis...".

Scroll down the list of Analysis Tools and click on "Regression" then click on the "OK" button.

In the Regression dialog box: Select the range where your data are stored as the "Input Y Range". Select the range where the junk data are stored as the "Input X Range". Choose a location for the tables of values, (most of which will be irrelevant!).   In this example, a separate tab has been chosen. Check the last box "Normal Probability Plots" and leave all other boxes unchecked. then click on the "OK" button.

At the location where the output was chosen to be, various tables and a graph appear.

Resize the graph and move it to another tab.
Right-click on any of the plotted points.

Click on "Add Trendline...".
In the new dialog box, accept the default option (linear trend) and
click on the "OK" button.
You may also wish to tidy the graph up somewhat, to produce a result like this:

In a new project, begin by naming column C1

From the "Calc" menu, select "Random Data" then click on "Normal..."

In the dialog box that pops up; enter 100 as the "Number of rows of data to generate"; select x for "Store in column(s)"; enter 1000 for the "Mean"; enter 10 as the "Standard deviation"; and click "OK".

A random sample of 100 values should now appear in column C1 of the worksheet. These values have been drawn randomly from a normal distribution of population mean 1000 and population standard deviation 10

From the "Stat" menu, select "Basic Statistics" then click on "Display Descriptive Statistics..."

Accept the defaults by just clicking "OK" on the dialog box that pops up.

The summary statistics for your random sample then appear in the Session window.
The exact values will vary each time.
The sample mean and sample median are usually close to but not exactly 1000
and the sample standard deviation is usually close to but not exactly 10,
(and therefore the sample standard error is close to ).

 

Now let Minitab find a 95% two-sided confidence interval for the population mean   µ , based on this random sample.

From the "Stat" menu, select "Basic Statistics" then click on "1-Sample t..."

 

In the dialog box "One-Sample t for the Mean" that pops up, Click on the "Options" button. In the secondary pop-up window we see "95.0" for the "Confidence level" and "Mean not= hypothesized mean" (which means a two-sided interval) for "Alternative hypothesis" Just accept these defaults for now and click "OK" on this dialog box, then click "OK" on the first dialog box.

Using the sample mean, the sample size and the sample standard deviation, Minitab constructs a 95% two-sided confidence interval for   µ .

We can easily change the level of confidence and/or replace the two-sided interval by a one-sided interval.

Repeat the steps "Stat" menu   –>   select "Basic Statistics"   –>   click on "1-Sample t..." In the dialog box "One-Sample t for the Mean" that pops up, Click on the "Options" button. In the secondary pop-up window shown here, change the "Confidence level" from "95.0" to "99.0"; then on the line "Alternative hypothesis", click on the pull-down arrow beside "Mean not= hypothesized mean" and select "Mean > hypothesized mean" instead. Click "OK" on this dialog box, then click "OK" on the first dialog box.

The 99% one-sided confidence interval for   µ  now appears in the Session window. Speaking loosely, we are “99% sure that   µ  is greater than” the value shown here (in this illustration, 996.59)

We can also conduct a classical hypothesis test, by checking the box "Perform hypothesis test" and selecting a value for the null hypothesis.

The additional information appears in the Session window, after the confidence interval. The t value can be compared to the appropriate critical t value. The P value is the probability that another random sample would produce a sample mean at least as extreme as the one we have, given that the population mean truly is the value that we chose for the "hypothesized mean" in the previous dialog box. In this illustration the P value is less than 0.0005 (which rounds off to 0.000 to three decimal places), so that we are very confident indeed (better than 99.95%) that the true mean is greater than 995. Of course, we know that the true population mean in this case is actually 1000 exactly.