Sum of Squares
We introduced a notation earlier in the course called the sum of squares. This notation was the
SS notation, and will make these formulas much easier to work with.
Notice these are all the same pattern,
SS(x) could be written as
Also note that
There is a measure of linear correlation. The population parameter is denoted by the greek letter rho and the sample statistic is denoted by the roman letter r.
Here are some properties of r
Here is the formula for r. Don't worry about it, we won't be finding it this way. This formula can be simplified through some simple algebra and then some substitutions using the SS notation discussed earlier.
If you divide the numerator and denominator by n, then you get something which is starting to hopefully look familiar. Each of these values have been seen before in the Sum of Squares notation section. So, the linear correlation coefficient can be written in terms of sum of squares.
This is the formula that we would be using for calculating the linear correlation coefficient if we were doing it by hand. Luckily for us, the TI82 has this calculation built into it, and we won't have to do it by hand at all.
The claim we will be testing is "There is significant linear correlation"
The Greek letter for r is rho, so the parameter used for linear correlation is rho
r has a t distribution with n2 degrees of freedom, and the test statistic is given by:
Now, there are n2 degrees of freedom this time. This is a difference from before. As an oversimplification, you subtract one degree of freedom for each variable, and since there are 2 variables, the degrees of freedom are n2.
This doesn't look like our
If you consider the standard error for r is
the formula for the test statistic is , which does look like the pattern we're looking
for.
Remember that
Hypothesis testing is always done under the assumption

Since H_{0} is rho = 0, this formula is equivalent to the one given in the book.
Additional Note: 1r^{2} is later identified as the coefficient of nondetermination
If you are testing to see if there is significant linear correlation (a two tailed test), then there is another way to perform the hypothesis testing. There is a table of critical values for the Pearson's Product Moment Coefficient (PPMC) given in the text book. The degrees of freedom are n2.
Depending on the textbook you're using, you may be required to look up either n or the df in the table. Be sure to look at the table before you use it or you may get wrong critical values. In the Triola text, it is the sample size, n, that you look up. In the Bluman text, it is the df=n2 that you look up.
The test statistic in this case is simply the value of r. You compare the absolute value of r (don't worry if it's negative or positive) to the critical value in the table. If the test statistic is greater than the critical value, then there is significant linear correlation. Furthermore, you are able to say there is significant positive linear correlation if the original value of r is positive, and significant negative linear correlation if the original value of r was negative.
Use the first one if you fail to reject the null hypothesis, that is, your test statistic is not bigger than the critical value.
Use the second one if you reject the null hypothesis (your test statistic is bigger than the critical value) and your test statistic is positive.
Use the last one if you reject the null hypothesis (your test statistic is bigger than the critical value) and your test statistic is negative.
Using the table to look up the critical values is the most common technique. However, the first technique, with the tvalue must be used if it is not a twotail test, or if a different level of significance (other than 0.01 or 0.05) is desired.
Okay, here's a puzzle for you. The first question is ...
If two things are not equal, does that mean one's bigger?
And the second question is similar ...
If one thing is bigger than another, does that mean they're not equal?
Now, the real kicker. One of the answers is "yes", and the other is "no".
Grab your aspirin and think about the statements in the context of hypothesis testing.
Saying that two things are not equal means that you have rejected the null hypothesis in a twotail test. So, if the level of significance is 0.10, then with a twotail test, you have 0.05 on the right side. So, if you say "two things aren't equal" because you reject the null hypothesis, then your probabilityvalue must have been less than 0.05.
To say that one thing is bigger means to reject the null hypothesis with a righttail test. So, if the level of significance is 0.10, then with a right tail test, you have all 0.10 of that on the right side. So, if you say "one thing is bigger" because you reject the null hypothesis, then your probability value must be less than 0.10.
Still with me? Let's analyze the statements above.
What the @*&#^@* does that have to do with anything?
When we use the table to look up the critical values, it only gives us the critical values for a twotail test. That is, we're testing that either there is significant linear correlation or that there isn't (twotail). We're not testing for positive linear correlation (righttail) or negative linear correlation (lefttail).
How then can we get by with our conclusion that uses the word positive or the word negative in it? If we reject the claim that two things are equal (rho = 0), then one has to be bigger. So, either rho is greater than zero (positive correlation) or zero is bigger than rho (negative correlation).
If there is a significant linear correlation between two variables, then one of five situations can be true.
There are some common errors that are made when looking at correlation.