12.1 Summary

Bivariate Data: Each data point has two values. The form is $(x,y)$ .

Line of Best Fit or Least Squares Line (LSL): $\hat{y}=a+\text{bx}$

$x$ = independent variable; $y$ = dependent variable

Residual: $\text{Actual y value}-\text{predicted y value}=y-\hat{y}$

Correlation coefficient r:

• Used to determine whether a line of best fit is good for prediction.
• Between -1 and 1 inclusive. The closer $r$ is to 1 or -1, the closer the original points are to a straight line.
• If $r$ is negative, the slope is negative. If $r$ is positive, the slope is positive.
• If $r=0$ , then the line is horizontal.

Sum of Squared Errors (SSE): The smaller the SSE , the better the original set of points fits the line of best fit.

Outlier: A point that does not seem to fit the rest of the data.