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Chapter 9: Regression Wisdom

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Chapter 9: Regression Wisdom AP Statistics Subsets The Effects of Unusual Points Influential Points: Must dramatically influences the slope of the LSRL. – PowerPoint PPT presentation

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Title: Chapter 9: Regression Wisdom


1
Chapter 9 Regression Wisdom
  • AP Statistics

2
Other Regression Issues
  • Subsets
  • Dangers of extrapolation
  • Possible effects of outliers, high leverage, and
    influential points
  • Problems with regression of summary data
  • Mistakes of inferring causation

3
Subsets
4
Extrapolation
  • The farther our x value is from the mean of x,
    the less we trust our predicted value.
  • Once we venture into new x territory our
    predicted value is an extrapolation.
  • Our extrapolations not reliable because we are
    operating under the assumption that the
    relationship between x and y has changed, even
    for these extreme values of x.
  • Dont extrapolate into the future!!!!!!!!

5
Extrapolation
6
The Effects of Unusual Points
  • Influential Points
  • Must dramatically influences the slope of the
    LSRL.
  • May change the correlation coefficient, depending
    upon where it is placed.
  • Outliers
  • If the point is unusual in the scatterplotnot
    based on the unusualness for one-variable
  • May or may not be influential

7
Unusual Points
8
Unusual Points
9
Unusual Points
10
Lurking Variables and Causation
  • With observational data, as opposed to designed
    experiments, there is not way to be sure that a
    lurking variable is not the cause of any apparent
    association.
  • The lurking variable is some third variable (not
    the explanatory or predictor variable) that is
    driving both variables you have observed.

11
Lurking Variables and Causationz is the lurking
variable
12
Lurking Variables and Causation
  • There have been many studies showing a strong
    positive association between hours spent in
    religious activities (going to church, attending
    religious classes, praying, etc) and life
    expectancy. NOT CAUSATION. There is
    confoundingon average, people who attend
    religious activities also take better care of
    themselves than non-church attendants. They are
    also less likely to smoke, more likely to
    exercise and less likely to be overweight. These
    effects of good habits (lurking variables) are
    confounded with the direct effects of attending
    religious activities.

13
Working With Summary Values
  • Be cautious when working with data values that
    are summaries, such as mean and medians.
  • These values have less variability and therefore
    inflate the strength of the relationship
    (correlation).

14
Summary Data
15
All Data Points
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