Correlations and Regression

This will be a brief tutorial, since there is very little that is required to calculate correlations and linear regressions. To calculate a simple correlation matrix, one must use [Statistics => Correlate => Bivariate...], and [Statistics => Regression => Linear] for the calculation of a linear regression.

For this section, the analyses presented in the computer section of the Correlation and Regression chapter will be replicated. To begin, enter the data as follows,

79 1.67
88 2.25
85 2.50

Simple Correlation

The results from output window should look like the following,

As you can see, r=0.702, and p=.000. The results suggest that the correlation is significant.

Note: In the above example we only created a correlation matrix based on two variables. The process of generating a matrix based on more than two variables is not different. That is, if the dataset consisted of 10 variables, they could have all been placed in the Variables: list. The resulting matrix would include all the possible pairwise correlations.

Correlation and Regression

Linear is possible to output the regression coefficients necessary to predict one variable from the other - that minimize error. To do so, one must select the [Statistics => Regression => Linear...] option. Further, there is a need to know which variable will be used as the dependent variable and which will be used as the independent variable(s). In our current example, GPA will be the dependent variable, and IQ will act as the independent variable. Specifically,

Some of the results of this analysis are presented below,

The correlation is still 0.702, and the p value is still 0.000. The additional statistics are "Constant", or a from the text, and "Slope", or B from the text. If you recall, the dependent variable is GPA, in this case. As such, one can predict GPA with the following,

GPA = -1.777 + 0.0448*IQ

The next section will discuss the calculation of the ANOVA.