*Calculating Principal Components* |

## Principal Component Plots

Examine the scatter plot of the first two principal
components shown in Figure 19.6.
Each marker on the plot represents
two principal component scores.
The output component scores are a linear combination
of the standardized Y variables with coefficients
equal to the eigenvectors of the correlation matrix.
| Click on the observations with the four highest values for **PCR1**. |

The resulting scatter plot should now appear as shown
in Figure 19.8.

These four observations correspond to
Mike Schmidt, Reggie Jackson, Tony Perez, and Pete Rose.
The label for Mike Schmidt is not shown because the observation is too close
to Reggie Jackson.
This is not unexpected since the first principal component
is a measure of the player's overall career performance.
Now examine observations in the second principal
component direction on the scatter plot.
Recall that the second component appeared to be a measure of
the combined performance of home runs and runs batted in
versus other career performance.
The observations with large values of **PCR2**
correspond to Mike Schmidt and Reggie Jackson.
As one might expect, both players have high
career-long home runs and runs batted in.

**Figure 19.8:** Scatter Plot of First Two Principal Components

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