Chapter 7: Linear Regression

Question 1

Regression definition go!

Answer 1

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

Question 2

Least square criterion?

Answer 2

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

Question 3

Least Squares regression line?

Answer 3

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

Question 4

Least squares regression line formula?

Answer 4

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

Question 5

How to make the regression line?

Answer 5

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

Question 6

What is the best predictor of Y???

Answer 6

the regression line

Question 7

Prediction errors?WTF?

Answer 7

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

Question 8

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

Answer 8

• want it to be small

Question 9

Characteristics of the standard error of the estimate?

Answer 9

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

Question 10

Homoscedasticity ?

Answer 10

constant variance

• basing std error on the spread
• best case

Question 11

Heteroscedasicity??

Answer 11

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