This particular analysis can be used to test a difference between two or more means in one single test.

So, in one way ANOVA the subjects are only classified in one way and influenced by a single factor.

In saying that, with this test the total variations are broken down into sub units.

The first one measuring between the sample means and the second measurement is within a sampled population.

This test is used to test the hypothesis that several means are different

and we’re going to go ahead and begin working with this particular analysis by opening up the second data file.

But before we do that, let’s look at our hypothesis.

For our real life scenario, we’re going to play the role of aqua marine scientists.

In order for us to create an underwater sensor array, four different alloys

are purchased and tested for corrosion resistance.

Five plates of each type of alloy are placed underwater for a total of 60 days.

After that final 60th day, the alloys were lifted and the corrosion pits on each one of the plates were measured.

So our research question states 'Which of the alloys tested would be appropriate for creating an underwater sensor array?'

For this research question our null hypothesis states the four alloys

exhibit the same kind of behavior and our not different from one another.

Our alternative hypothesis states the four alloys are different and do not exhibit the same kind of behavior.

Ensure you have the alloy.sav file opened and to begin running this test,

click on the Analyze menu, select compare means then click on One-Way ANOVA.

In the One –Way ANOVA dialogue box, select the pits variable. Transfer that variable to the Dependent List box.

Then select the alloy variable and transfer it to the Factor list box.

Before we complete and run the test, let’s select the Options button in the upper right hand corner of the dialogue box.

Select the Descriptive text box. Select the Means Plot text box. Click the Continue button.

And finally select the OK button to run your analysis.

In your output viewer, scroll down to your ANOVA table so you can take a look at your significance value.

The Descriptives table provides you statistics on a number of different options.

For instance, it shows you how many of each type of alloy you used with the “N”.

It also provides you the Standard Deviation and your Standard Error for each one of the alloys.

Also, it provides the mean number of corrosion pits on each one of the plates or alloys.

Recall that our null hypothesis is that the four alloy kinds exhibit the same kind of behavior

and are not different from one another.

Our alternative hypothesis states the four alloys are different and do not exhibit the same behavior.

Point zero zero is way less than our .05 threshold and in saying that the null hypothesis

can be rejected and we can conclude that the alloys are indeed different.

So now that we know that they’re different, maybe we would like to know or find out which one of the alloys

are the most favorable to complete our underwater experiment.

For those of you who are visual learners like myself, this means plot makes it easier for you to look at the difference

and compare different corrosion pits for each one of the alloys.

It places them on the plot and compares them side by side and it makes it easier for you to see firsthand

exactly which one has the most and which one has the least number of corrosion pits.