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by: Natalie Land

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# STC 103: Week 2 and 3 Notes STC 103

Marketplace > University of Miami > Communication > STC 103 > STC 103 Week 2 and 3 Notes
Natalie Land
UM
GPA 4.0

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Here is detailed notes of weeks 2 and 3 from class, easy to understand
COURSE
Statistical Reasoning for Strategic Communication
PROF.
Bo Ra Yook
TYPE
Class Notes
PAGES
3
WORDS
CONCEPTS
Math, STC, STC103, Statistics, statisical
KARMA
Free

## Popular in Communication

This 3 page Class Notes was uploaded by Natalie Land on Monday September 12, 2016. The Class Notes belongs to STC 103 at University of Miami taught by Bo Ra Yook in Fall 2016. Since its upload, it has received 7 views. For similar materials see Statistical Reasoning for Strategic Communication in Communication at University of Miami.

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Date Created: 09/12/16
Chapter  2  STC  103  Notes     •   Descriptive  Statistics:  to  describe,  summarize,  and  organize     o   Just  explaining  what’s  going  on     o   Sampling:  studying  and  testing  prediction  and  hypothesis     §   Experimental     o   Categorical  data:  either  or  classification  of  categories     o   Continuous  data:  continues,  could  be  infinite     §   Nominal,   •   Gender  numbers  don’t  mean  anything   §   ordinal,   •   order  matters   §   interval  ratio   •   how  likely     •   no  absolute  zero     §   ratio   •   absolute  zero     •   Frequency:  number  of  times  something  happens     o   Ie  :  in  one  week  I  eat  four  times  at  subway     o   To  see  the  frequency  distribution,  we  have  an  organized  table,  table  or  graph,   shows  the  categories,  and  the  frequency:  (number  of  individuals  in  each   category)     §   Valid  percentage  of  all  valid  cases,  if  a  variable  is  missing  not  valid     •   Bar  Chart     o   X  axis:  has  the  categories     o   Y  axis:  frequencies  for  each  category     •   Pie  Chart:   o   All  together  is  one  hundred  percent     •   Continuous  data  descriptions     o   Frequencies:  the  normal  curve     o   Measure  of  central  tendency:  how  data  is  spread  or  concentrated     o   Point  is  plotted  above  each  score  or  measurement  and  then  you  connect  the   dots     §   Histogram:  horizontal  line  at  each  point,  corresponds  to  interval  of  scores     §   Frequency  polygon:  graph  representing  the  frequency  of  scores  in   smooth  curve  points  connected     •   In  histogram  one  score  connects  to  another  in  frequency  it   doesn’t     •   Bar  chart  is  for  categorical  data  and  histograms  is  for  continuous   data     •   Continuous  Data     o   How  graph  will  shape?   §   Symmetrical:  data  on  each  side  is  mirrored     §   Skewed  scores:  pile  up  on  one  side  and  tapper  off  on  other     §   Skewedness:  measure  of  asymmetry  if  tail  is  on  right  its  high  scores  this   equals  positive  skew     §     §   positive  skew     §   if  tail  is  on  opposite  side  it’s  a  negative  skew  and  it  means  low  scores     §   kurtosis:  height  of  the  curve:  the  peak     •   leptokurtic:  less  difference  higher  peak     •   platykurtic:  more  difference,  flatter  peak     •   Central  Tendency:     o   How  together  the  data  is  around  the  middle     o   How  scores  are  clustered     §   Spread  out  or  together     o   3  Ms     §   mean   §   mode     §   median   o   purpose:  find  score,  most  typical  or  best  representative  of  the  entire  group     o   mean:  average,  sum  of  scores  divided  by  number  of  sample     o   M  :  sample  mean     o   The  u:  population  mean   o   Median:  middle  point  middle  score     o   If  n  is  odd  identify  middle  score  and  if  n  is  even  you  average  the  middle  pair  to   find  median     o   What  if  there  is  a  lot  of  N  scores     §   if  N  is  odd     •   median  equals  N+1  divided  by  two     •   if  N  is  even   o   N/2  plus  N+2/2     o   Mode:  most  frequent  score     o   If  mean  is  bigger  than  median,  then  we  get  a  positive  skewed     o   If  mean  is  less  than  median:  negatively  skewed     o   Mean  is  good  for  the  sum  of  all  individuals  Ns  or  when  you  know  value  of  every   score   §   Not  good  for  extreme  scores,  ordinal  data,  nominal  or  skewed   distribution     o   Median  is  good  for     §   Skewed  distribution,  undetermined  values,  open  ended,  ordinal  data,     §   Not  good  for  nominal     •   Measures  of  dispersion   o   Variability  of  scores     o   Range:  highest  score  minus  the  lowest  score     §   Based  on  only  two  scores     o   Variability:  distance  of  spread  of  scores  or  distance  of  a  score  from  the  mean     §   Purpose:  to  describe  distribution     o   Most  important  measure  is  variance  and  standard  deviation   §   Standard  deviation:  how  far  individual  score  from  mean     §   Describes  if  scores  are  clustered  closely  around  mean  or  scattered     §   Variance  is  used  for  population  and  sample  is  S  squared  used  for  sample     o   Variance  is  the  sum  of  all  the  (individual  scores  –  the  average  squared)  divided   by  the  number  of  individual  numbers  n-­‐1     o   And  for  the  standard  deviation  its  just  the  square  root  of  the  variance     •   Sample  vs  Population   o   Population:  the  universe  of  objects     §   Public,  target  audience,  too  large  to  measure     o   Sample:  portion  of  that  population   §   Randomly  drawn     o   Sampling:  goal  is  to  generalize  provide  an  estimate  population   o   Random  probability  sampling:  each  individual  in  population  has  equal  chance  of   getting  chosen  for  the  sample     •   Statistic:  characteristic  of  a  sample     •   Parameter:  characteristic  of  a  population   •   Statistical  inference:  process  by  which  parameter  can  be  estimated     •   Population  distribution:  work  for  starbucks  and  want  to  know  if  UM  students  attitudes   toward  starbucks  so  we  measure  attitude  of  all  UM  students     •   Sample  distribution:  draw  a  sample  and  ask  attitude  of  only  100  of  5,000  students  for   example     •   Sampling  distribution:  we  want  to  study  population  but  sub  group  cant  explain  entire   population  so  we  do  man  samples  so  many  samples  of  100  through  5,000  students     •   Frequency:  distribution  tied  to  a  particular  number  of  observations  and  how  this   number  is  divided  among  different  categories     •   Proportion:  of  total  number  of  unites

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