Chapter 7: Linear Regression Flashcards Preview

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Flashcards in Chapter 7: Linear Regression Deck (11):
1

Regression definition go!

- uses relationship between variables for prediction
- two variables are involved....the predictor and the criterion
- variables are not equal

2

Least square criterion?

Y prime= predicted y value
Y= observed Y value
Y-Y prime = prediction error for each data point

3

Least Squares regression line?

- line that minimizes total prediction error
- all vert distances should be in their absolute min
-only one line....

4

Least squares regression line formula?

- Y'= byX+ay
Y'= predicted value
by= slope
ay= y-axis intercept for min error in y
X= any value of x

5

How to make the regression line?

solve Y' for the lowest and highest x values.
- plot the value and connect the two points.
calc y int and connect line to the int

6

What is the best predictor of Y???

the regression line

7

Prediction errors?WTF?

- the weaker the relationshup the more the scores vary from the regression line
the stronger the regression line =the closer

8

Size wise what do you want the standard error of the esitmate to be?

- want it to be small

9

Characteristics of the standard error of the estimate?

- treat it like standard deviation for predictions of percents..
+1/-1 = 68%
+/- 2= 95%
+/- 3= 99%

10

Homoscedasticity ?

constant variance
- basing std error on the spread
- best case

11

Heteroscedasicity??

- non constant variance
-worst case
- predictions will not rep well
- cone shaped