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Fig. 6.12 Example of Using Excel’s
¼
correl Function to Compute the Correlation Coefficient
2. The errors of measurement are independent of each other (e.g. the errors from a
specific time period are sometimes correlated with the errors in a previous time
period).
3. The errors fit a normal distribution of Y-values at each of the X-values.
4. The variance of the errors is the same for all X-values (i.e., the variability of the
Y-values is the same for both low and high values of X).
A detailed explanation of these assumptions is beyond the scope of this topic, but
the interested reader can find a detailed discussion of these assumptions in Levine
et al. 2011 , pp. 529-530). (
Now, let’s create a chart summarizing these data.
Important note: Whenever you are preparing a chart, we strongly recommend that
you put the predictor variable (X) on the left, and the criterion variable (Y) on the
right in your Excel spreadsheet, so that you do not get these variables backwards in
your Excel steps and make a mess of the problem in your computations. If you do
this as a habit, you will save yourself a lot of grief.
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