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Lecture 4

by: Debra Tee

Lecture 4 STATS 250

Debra Tee
GPA 3.85

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About this Document

This lecture covers some key words of research studies, definitions and examples of experiments and observational studies.
Introduction to Statistics
Brenda Gunderson
Class Notes
Experiments, observational, definitions
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This 3 page Class Notes was uploaded by Debra Tee on Friday September 30, 2016. The Class Notes belongs to STATS 250 at University of Michigan taught by Brenda Gunderson in Fall 2016. Since its upload, it has received 3 views. For similar materials see Introduction to Statistics in Statistics at University of Michigan.


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Date Created: 09/30/16
  Lecture  4:  Gathering  Useful  Data  for  Examining   Relationships     6.1  Speaking  the  Language  of  Research  Studies     Definitions:   -­   Observational  Studies:  The  researchers  simply  observe  or  measure   the  participants  (about  opinions,  behaviors,  or  outcomes)  and  do  not   assign  any  treatments  or  conditions.  Participants  are  not  asked  to  do   anything  differently.     -­   Experiments:  The  researchers  manipulate  something  and  measure   the  effect  of  the  manipulation  on  some  outcome  of  interest.  Often   participants  are  randomly  assigned  to  the  various  conditions  or   treatments.  Most  studies,  either  observational  or  experimental,  are   interested  in  learning  of  the  effect  of  one  variable  (called  the   explanatory  variable)  on  another  variable  (called  the  response  or   outcome  variable).     -­   A  confounding  variable  is  a  variable  that  both  affects  the  response   variable  and  also  is  related  to  the  explanatory  variable.  The  effect  of  a   confounding  variable  on  the  response  variable  cannot  be  separated   from  the  effect  of  the  explanatory  variable.     -­   Confounding  variables  might  be  measured  and  accounted  for  in  the   analysis,  or  they  could  be  unmeasured  lurking  variables.  Confounding   variables  are  especially  a  problem  in  observational  studies.   Randomized  experiments  help  control  the  influence  of  confounding   variables.     -­   Since  the  sample  is  just  a  part  of  the  population  there  will  be  some   uncertainty  about  the  estimates  and  decisions  we  make.  To  measure   and  quantify  that  uncertainty  we  turn  to  PROBABILITY           Example:  A  researcher  at  the  University  of  Michigan  believes  that  the   number  of  times  a  student  visits  the  Student  Health  Center  (SHC)  is   strongly  correlated  with  the  student’s  type  of  diet  and  their  amount  of   weekly  exercise.  The  researcher  selected  a  simple  random  sample  of  100   students  from  a  total  of  3,568  students  that  visited  SHC  last  month  and  first   recorded  the  number  of  visits  made  to  the  SHC  for  each  selected  student   over  the  previous  6  months.  After  recording  the  number  of  visits,  he  looked   into  their  records  and  classified  each  student  according  to  the  type  of  diet   (Home-­‐Cooked  Food  /  Fast  Food)  and  the  amount  of  exercise  (None  /   Twice  a  Week  /  Everyday).       a.  Is  this  an  observational  study  or  a  randomized  experiment?   An  observational  study  (recorded  the  variables,  not  assigned  to  a  level).       b.  What  are  the  explanatory  and  response  variables?   Response  =  number  of  visits  (quantitative  and  discrete)   -­   Explanatory  =  type  of  diet  and  amount  of  exercise  (here  both   categorical) .  


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