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by: Debra Tee

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# Lecture 3 STATS 250

Debra Tee
UM
GPA 3.85

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Collecting and Using Sample Data Wisely, Descriptive Statistics, Biases, Margin of errors, Confidence Intervals, Sample Size.
COURSE
Introduction to Statistics
PROF.
Brenda Gunderson
TYPE
Class Notes
PAGES
3
WORDS
KARMA
25 ?

## Popular in Statistics

This 3 page Class Notes was uploaded by Debra Tee on Tuesday September 27, 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: 09/27/16
Lecture  3:  Sampling:  Surveys  and  How  to  Ask   Questions     5.1  Collecting  and  Using  Sample  Data  Wisely     Definitions:   -­   Descriptive  Statistics:  Describing  data  using  numerical  summaries   (such  as  the  mean,  IQR,  etc.)  and  graphical  summaries  (such  as   histograms,  bar  charts,  etc.).   -­   Inferential  Statistics:  Using  sample  information  to  make  conclusions   about  a  larger  group  of  items/individuals  than  just  those  in  the   sample.   -­   Population:  The  entire  group  of  items/individuals  that  we  want   information  about,  about  which  inferences  are  to  be  made.   -­   Sample:  The  smaller  group,  the  part  of  the  population  we  actually   examine  in  order  to  gather  information.   -­   Variable:  The  characteristic  of  the  items  or  individuals  that  we  want  to   learn  about.         Fundamental  Rule  for  Using  Data  for  Inference:     -­   Available  data  can  be  used  to  make  inferences  about  a  much  larger   group  if  the  data  can  be  considered  to  be  representative  with  regard   to  the  question(s)  of  interest.     Sample  vs  Census  survey     -   Many  times  we  cannot  sample  the  whole  population,  so  we  have  to   sample  certain  people  and  gauge  the  percentage  of  the  entire   population  who  have  a  certain  trait  or  opinion  to  within  3%.   -   The  key  is  to  use  a  proper  sampling  method.     Bias:  How  Surveys  can  go  Wrong   -   Biased  if  the  method  used  to  obtain  those  results  would  consistently   produce  values  that  are  either  too  high  or  too  low.   -   Selection  bias  occurs  if  the  method  for  selecting  the  participants   produces  a  sample  that  does  not  represent  the  population  of  interest.   -   Nonparticipation  bias  (nonresponse  bias)  occurs  when  a   representative  sample  is  chosen  for  a  survey,  but  a  subset  cannot  be   contacted  or  does  not  respond.   -   Biased  response  or  response  bias  occurs  when  participants   respond  differently  from  how  they  truly  feel.  The  way  questions  are   worded,  the  way  the  interviewer  behaves,  as  well  as  many  other   factors  might  lead  an  individual  to  provide  false  information.   5.2  Margin  of  Error,  Confidence  Intervals,  and  Sample   Size     -­  Survey  is  used  to  find  a  proportion  based  on  a  representative  sample  from   the  population  of  interest.     The  measure  of  accuracy,  how  close  the  proportion  comes  to  the  truth  of   the  entire  population  is  called  the  margin  of  error   Conservative  (approximate  95%)  Margin  of  Error  =1/sqrt(n)  where  n  is  the   sample  size.   Approximate  95%  Confidence  Interval  for  p:  sample  proportion  ±  1/sqrt(n)  -­ >  p-­hat  ±  1/sqrt(n)

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