Chapter 7: Linear Regression Flashcards

1
Q

Regression definition go!

A
  • uses relationship between variables for prediction
  • two variables are involved….the predictor and the criterion
  • variables are not equal
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2
Q

Least square criterion?

A

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

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3
Q

Least Squares regression line?

A
  • line that minimizes total prediction error
  • all vert distances should be in their absolute min
  • only one line….
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4
Q

Least squares regression line formula?

A
- Y'= byX+ay 
Y'= predicted value 
by= slope 
ay= y-axis intercept for min error in y 
X= any value of x
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5
Q

How to make the regression line?

A

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

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6
Q

What is the best predictor of Y???

A

the regression line

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7
Q

Prediction errors?WTF?

A
  • the weaker the relationshup the more the scores vary from the regression line
    the stronger the regression line =the closer
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8
Q

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

A
  • want it to be small
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9
Q

Characteristics of the standard error of the estimate?

A
  • treat it like standard deviation for predictions of percents..
    +1/-1 = 68%
    +/- 2= 95%
    +/- 3= 99%
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10
Q

Homoscedasticity ?

A

constant variance

  • basing std error on the spread
  • best case
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11
Q

Heteroscedasicity??

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