1. Least Squares Regression Chapter 3.2

2. Interpreting a Regression Line
Requires explanatory and response variables
Describes how y will change as x changes.
Interpreting a Regression Line
Y = a + bx
b = slope ; a = y-intercept (when x = 0)
Interpreting slope: “y” increases /decreases on average by “b” for each additional “x”
Interpreting y-intercept: “y” is predicted to be “a” when no “x” are present.

3. Example of interpreting a regression line:
Fat gain = – (NEA change)
The slope b = shows us that fat gained goes down on average by kilograms for each additional calorie of NEA.
The y-intercept a = tells us that if there are no NEA calories present, we still have a predicted value of kilograms of fat gain.

4. Extrapolation is the use of a regression line for prediction outside the range of values (often not accurate so you should avoid this).
LSRL – the line that makes the sum of the squared vertical distances of the data points from the line as small as possible.

5. Three Ways to Find LSRL
When given the summary statistics: use formula packet with:
Slope: b= r(Sy/Sx)
Y-int: a=Ӯ-b