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by: Erica Evans

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# Week 7 notes Comm106

Erica Evans
Stanford
GPA 3.9

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Notes from 2/17 and 2/19
COURSE
Communication Research Methods
PROF.
Jennifer Pan
TYPE
Class Notes
PAGES
2
WORDS
KARMA
25 ?

## Popular in Communication Studies

This 2 page Class Notes was uploaded by Erica Evans on Monday February 22, 2016. The Class Notes belongs to Comm106 at Stanford University taught by Jennifer Pan in Fall 2016. Since its upload, it has received 8 views. For similar materials see Communication Research Methods in Communication Studies at Stanford University.

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Date Created: 02/22/16
Comm106   2/19/2016       Statistics   • If  we  change  one  unit  of  one  thing,  how  will  that  change  the  other  thing?     • Linear  regression!  à  we  assume  there  is  a  linear  relationship     • Y  =  a  +  bx  à  High  school  algebra  equation  for  a  straight  line     • For  any  value  of  x,  we  get  some  expected  value  of  y     • Suppose  we  have  two  points  and  draw  a  straight  line  between  them,  we  can   represent  this  as  an  equation.  The  slope  is  the  rise/run.  Y  =  y-­‐intercept  +   slope*x.       Writing  equations   • Population  relationships  are  written  in  Greek  letters     • Our  estimates  from  the  sample  are  written  with  ‘hats’     • Y  is  the  actual  value  of  y  in  the  sample  y(with  a  hat)  is  what  we  predict  based   on  the  regression  line       Regression   • Lets  us  predict  y  for  any  value  of  x     • On  average  y  changes  by  (b)(y’s  units)  for  a  1  (x’s  units)  increase  in  x     • If  we  are  looking  at  the  relationship  between  basketball  player’s  height  and   number  of  blocks  in  a  game  à  On  average  a  player’s  blocks  per  game   changes  by  .108  blocks  for  a  1  inch  increase  in  height.     • To  predict  y  for  any  x,  just  plug  in  the  numbers     • Residual:  the  difference  between  the  actual  value  and  the  predicted  value     • OLS  estimation  (ordinary  least  squares  estimation)  à  the  technique  used  to   draw  the  line  of  regression     • Drawing  a  line  through  the  points  to  minimize  the  residuals.    There  are   different  ways  to  draw  lines  of  regression;  this  is  a  specific  method.     • à  Minimize  the  total  squared  vertical  distances  from  the  actual  data  points   in  the  sample.  Minimize  the  sum  of  the  squared  residuals.     • Another  technique  is  least  absolute  distance…  this  will  also  produce  a   straight  line!     • In  regression,  units  matter!  (Unlike  correlation)  à  like  switching  from   Celsius  to  Fahrenheit  will  change  your  equation.     • Linear  regression  is  asymmetric  while  correlation  is  symmetric  à  Y  on  X  is   not  the  same  as  X  on  Y.     Comm106     2/17/2016       How  to  analyze  a  graph:     • Pattern:  What  is  the  shape?  Curvilinear?  Linear?  No  pattern?     • Direction:  negative  or  positive?     • Strength:  how  close  are  the  points  to  each  other?  Does  it  look  consistent?     • Are  there  exceptions?     • Correlation  coefficient:  how  closely  does  the  data  follow  a  straight-­‐line  trend   and  how  closely  does  the  data  cluster  to  that  line?     • R  can  be  any  value  from  -­‐1  to  1     • 1  means  perfect  positive  linear  correlation     • -­‐1  means  perfect  negative  linear  association     • 0  implies  no  association     • Correlation  formula:     • X  =  1,2,4,1  (average  =2,  sd  =1.41)   • Y  =  1,3,4,3  (average  =3,  sd=1.63)     • Z-­‐score  =  number-­‐average/sd   • Multiply  the  corresponding  z  scores  to  get  4  numbers  then  add  them  all   togetherà  divide  by  n-­‐1  (which  is  4-­‐1  in  this  case)   • R  =  (.87+0+1.74+0)/3  =  .87     • Function  in  R  à  cor()     • Correlation  is  symmetric  –  x  and  y,  is  same  as  y  and  x.  That’s  why  it  doesn’t   matter  what  order  your  input  the  variables  in  R       Be  careful   • Sometimes  observations  are  stacked  on  the  same  point  in  a  scatter-­‐plot

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