1. AP Stats: 3.3 Least-Squares Regression Line
Regression Line is a straight line that describes how a response variable (y) changes as an explanatory variable (x) changes. This is a MODEL for the data.
We us the regression line to make PREDICTIONS

2. Least-squares regression line (LSRL)
of y on x is the line that makes the sum of the squares of the vertical distances of the data points from the line as small as possible.

3. Calculator: LinReg(a+bx) L1, L2, Y1
Equations of the LSRL
***SLOPE intercept
Calculator: LinReg(a+bx) L1, L2, Y1

4. Coefficient of Determination - r2
The fraction of the variation in the values of y that is explained by least-squares regression of y on x.
“we say ?% of the variation in y is explained by least-squares regression of y on x.”

5. Residuals
– The difference between an observed value of the response variable and the value predicted by the regression line.
Residual = observed y – predicted y
Resid =

6. Residual Plot
: plots the residuals on the vertical axis against the explanatory variable on the horizontal axis.
    The mean of the residuals is always 0 (if not we have rounding error)

7. Influential
An observation is Influential if removing it would markedly change the position of the regression line. Points that are outliers in the x direction are often influential.