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In this video we will illustrate how one can use regression models in place of
tests conducted for correlation analysis.
We will return to the basics. There are two types, or
mostly two types of variables. First, are the categorical variables for which we
use chi-square tests to determine if there is an association between the two.
And second are the categorical variables, or
we could have continuous variables where we use the Pearson correlation test.
We will focus on just the continuous variables. We can plot two continuous
variables in a scatter plot. The teaching evaluation scores are on the Y axis and
the normalized beauty scores are on the X axis. You could sort of see a relationship
between the two variables. It's an upward sloping type of a relationship.
We see that as the beauty score increases, so does the teaching evaluation score.
Remember, we used the Pearson correlation test to determine the relationship and
its significance level.
Now let's do the same in regression. Just like we did with the T test and
the F test, we will fit a linear model for both the beauty, an evaluation score,
values and print out the models summary.
Taking a closer look, it prints out a P value of 4.25 * 10
raised to power negative 5, which is less than 0.05..
That is very similar to when we run the Pearson r function.
It will also give us the R-squared- value, that is, if we took square root 0.036.
It will give us 0.189 which is the some value as
the correlation coefficient from computing the Pearson R.
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