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by: Amanda Berg

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# Notes 9/25 STAT 121

Amanda Berg
BYU
GPA 3.65
Principles of Statistics
Dr. Christopher Reese

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Cautions in regression analysis notes
COURSE
Principles of Statistics
PROF.
Dr. Christopher Reese
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
regression, line, scatterplot, Statistics
KARMA
25 ?

## Popular in Statistics

This 2 page Class Notes was uploaded by Amanda Berg on Sunday September 27, 2015. The Class Notes belongs to STAT 121 at Brigham Young University taught by Dr. Christopher Reese in Fall 2015. Since its upload, it has received 52 views. For similar materials see Principles of Statistics in Statistics at Brigham Young University.

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Date Created: 09/27/15
Notes 923 Confounding when you can39t determine whether the response variable is a result of the explanatory variable or a lurking variable There are effects of 2 explanatory variables that can39t be separated because of how the study takes place Only exist because you designed and conducted the study badly ElIIIIII at E 3 run m E M E I EEIIII cc m H H 5m i l Dther US Country In this data one could conclude that the cause for an individual39s SAT math score is their country of origin However there are other variables that are not being taken into account lurking variables such as the educational level of those who took the test For example if the sample of international students were elite students planning on coming to the USA to study but you just used a sample of regular high schoolers from the US without elite education there is a lurking variable that isn39t being accounted for The varying levels of education between samples is something you knew about when choosing the study but this variable isn39t being taken into account and the study was therefore not performed very well There may be a relationship between country of origin and SAT score but because the sample of the population wasn t chosen well there is an extra variable education level whose relationship to the response variable can39t be distinguished from the actual explanatory variable39s relationship to the response variable The relationship is confounded Potential lurking variable Often you see graphs that have strong linear relationships between the explanatory and response variables and you decide that there is a relationship between the two variables However often there are more variables that are not being taken into account that may affect the strength of the relationship between the explanatory and response variables A child is riding on a plane and every time the seat belt sign comes on the ride becomes super bumpy The explanatory variable is the seatbelt sign and the response variable is bumpiness Of course we know that when a seatbelt sign comes on on a plane it is because there is turbulence However this child comes to think that there is causation for bumpiness the seat belt sign He thinks that the reason for the bumpiness is the seatbelt sign There is a lurking variable that the boy is unaware of affecting this relationship turbulence HIGH CORRELATION DOES NOT IMPLY CAUSATION Causann strong relationship consistent relationship across different studies ogica doseresponse relationship aeged cause precedes the effect in time aeged cause is plausible supported in animal experiments Simpson39s paradox when including the lurking variable causes us to rethink the direction of an association

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