1 / 7

Chapter 9 Notes

Chapter 9 Notes. In scatterplots we can have points that are outliers or influential points or both.

jara
Download Presentation

Chapter 9 Notes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Chapter 9 Notes

  2. In scatterplots we can have points that are outliers or influential points or both. • An outlier is an observation that lies outside the overall pattern of the other observations in a scatterplot. An observation can be an outlier in the x direction, the y direction, or in both directions. • An observation is influential if removing it would markedly change the position of the regression line. Points that are outliers in the x direction are often influential. Your book calls these leverage points.

  3. Extrapolation is the use of a regression line (or curve) for prediction outside the domain of values of the explanatory variable x. • Such predictions cannot be trusted.

  4. A Lurking Variable is a variable that has an important effect on the relationship among the variables in a study but is not included among the variables being studied. • Lurking variables can suggest a relationship when there isn’t one or can hide a relationship that exists.

  5. With observational data, as opposed to data from a well designed experiment, there is no way to be sure that a lurking variable is not the cause of any apparent association.

  6. Association vs. Causation • A strong association between two variables is NOT enough to draw conclusions about cause & effect. • Strong association between two variables x and y can reflect: • A) Causation – Change in x causes change in y • B) Common response – Both x and y are Responding to some other unobserved factor • C) Confounding – the effect on y of the explanatory variable x is hopelessly mixed up with the effects on y of other variables.

  7. Data with no apparent linear relationship can also be examined in two ways to see if a relationship still exists: • 1) Check to see if breaking the data down into subsets or groups makes a difference. • 2) If the data is curved in some way and not linear, a relationship still exists. We will explore that in the next chapter.

More Related