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StudyGuide Math Test

by: Natalie Land

StudyGuide Math Test STC 103

Marketplace > University of Miami > Communication > STC 103 > StudyGuide Math Test
Natalie Land
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About this Document

Here is a study guide for you guys and cheat sheet to print out and use for the exam!
Statistical Reasoning for Strategic Communication
Bo Ra Yook
Study Guide
Math, STC, STC103, Statistics, statistical methods
50 ?




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This 1 page Study Guide was uploaded by Natalie Land on Wednesday September 21, 2016. The Study Guide belongs to STC 103 at University of Miami taught by Bo Ra Yook in Fall 2016. Since its upload, it has received 38 views. For similar materials see Statistical Reasoning for Strategic Communication in Communication at University of Miami.


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Date Created: 09/21/16
•   Research:  the  systematic  investigation  of  phenomena  that  leads  us  to  understand,  describe,  and  predict  outcomes.  Begins  with  a  problem,  or  question,  can  be  descriptive,  relational,  or   causal.     •   Research  question:  statement  of  the  problem.  IE:  what  is  the  most  popular  automobile     •   Hypothesis:  a  testable  prediction  based  on  theory  or  previous  observation  IE:  Students  anxiety  about  public  speaking  are  associated  with  their  grades   •   Research  method:  strategy,  plan  and  activities  undertaken  to  accomplish  research   •   Quantitative  Research:  data  gathered  that  has  been  measured  in  discrete  units  that  have  the  potential  to  be  generalized.  Objective,  systematic,  controlled.  Survey,  experiment.   o   Descriptive:  no  manipulation.  Seeing  things  as  they  are,  surveys  and  polls   o   Experimental:  something  has  been  manipulated,  lab  experiment   o   Used  when  measured  yields  a  useful  description  of  what’s  being  studied,  or  when  you  wish  to  generalize,  or  when  you  want  to  know  probability.   •   Qualitative  Research:  data  that  has  been  gathered  in  symbolic  units  that  are  unique  to  particular  research  and  can’t  be  generalized.  Subjective,  and  less  controlled.  In  depth  interview,  focus   group.     •   Phenomenon:  any  object  or  event     •   Variable:  an  observable  characteristic  of  an  object  or  event   •   Independent  variable:  the  variable  that  represents  the  cause  of  the  dependent  variable,  it  can  be  naturally  occurring  and  is  difficult  to  change,  manipulated  can  be  predictor   •   Dependent  variable:  the  variable  that  represents  the  effect  of  the  IV.  Variable  that’s  tried  to  be  explained,  can  be  called  outcome  or  effect   •   Data:  the  reports  of  observation  of  variables.  Reflect  something  measured,  has  no  meaning  until  researchers  create  meaningful  measurement  system.  It  has  to  be  reliable  and  valid  so  it  can   be  useful   •   Statistics:  a  brand  of  applied  mathematics  that  specializes  in  procedures  for  describing  and  reasoning  from  measures,  observation  that  ha s  been  made  and  changed  to  discrete  units     •   Measurement:  a  scheme  for  the  assignment  of  numbers  or  symbols  to  specify  different  characteristics  of  a  var iable,  creates  logic,  links  obervations  to  numbers,  standardized   •   Sampling:  researchers  deal  with  large  numbers  such  as  large  audiences  or  stake  holders  that  are  too  large  for  data  so  we  sample  from  larger  population   •   Population:  a  total  set  of  units.  Sample:  a  subset  or  subgroup  of  a  population.  Descriptive  Statistics:  calculated  values  that  represent  certain  overall  characteristics  of  a  body  of  data.  Sampling   Statistics:  calculated  values  that  represent  how  sample  characteristics  vary  from  population  characteristics.  Probabilistic  Sampling  Statistics:  a  known  and  equal  chance  to  all  population   members  to  be  included  in  the  sample.     •   Two  classifications  of  variables  when  it  comes  to  measurement  categorical  and  continuous:  Sophomores  feel  less  stressed  than  freshman  during  first  week  of  the  semester.  Here  sophomore   and  freshmen  would  be  categorical  and  less  stressed  would  be  continuous   •   We  use  scales  to  measure  variables  in  research.  Scale:  a  specific  scheme  for  assigning  numbers  or  symbols  to  designate  characteristics  of  a  variable   •   Categorical  and  continuous  are  the  variables  and  four  levels  of  measurement  are  nominal  and  ordinal  that  fall  under  categorical,  and  then  there  is  inter val  and  ratio  falling  under  continuous   •   Nominal  scale:  assignment  of  numbers  to  categories,  has  no  mathematical  meaning  so  order  doesn’t  matter,  so  like  soup  is  1  tomato  is  2  salads  is  3.  Ordinal  Scale:  order  does  matter.     •   Interval  Scale:  behavioral  intention  measure.  How  likely,  Ratio:  has  an  absolute  zero,  age,  time  as  a  unit  of  measure,  distance  as  unit  of  measure,  weight   •   Reliability:  the  internal  and  external  consistency  of  measurement:  There  are  two  types,  temporal  stability  degree  to  which  scores  are  stable  over  time  and  internal  consistency  degree  to   which  various  questions  or  items  all  tap  on  the  same  thing     •   Validity:  the  degree  to  which  scales  measure  what  researches  claim  they  measure.  Goodness  of  fit.  Is  it  measuring  what  we  want  to  measure.  If  you  want  a  measure  to  be  useful  it  has  to  be   both  valid  and  reliable.  For  a  measure  to  be  valid  it  must  be  reliable  but  validity  means  its  reliable  but  reliable  doesn’t  mean  its  valid     •   Sample  vs  Population:  descriptive  statistics:  to  describe,  summarize,  and  organize.  Sampling  statistics:  to  test  the  predicted  relationship  in  hypothesis   •   Categorical  Data  descriptions:  frequency:  the  number  of  times  that  something  happens.  Frequency  distribution:  an  organized  tabulation  showing  the  number  of  individuals  located  in  each   category.  Can  be  displayed  in  a  table  or  graph  and  it  always  shows  the  categories  that  make  up  the  scale  and  the  frequency  or  number  of  individuals  in  each  category   •   Bar  chart:  x  axis  is  typically  the  categories,  Y  axis:  frequencies  or  percentages.  Pie  chart  adds  to  100  per cent     •   The  normal  curve,  measures  of  central  tendency  and  measures  of  dispersion  are  all   continuous  data  descriptions.    A  frequency  polygon  is  a  graph  representing  the  frequency  of  scores  in  a   smooth,  unbroken  curve.  Points  are  connected.  Histogram  is  a  horizontal  line  at  each  point  corresponds  to  the  interval  of  each  score.     •   Symmetrical  distribution:  each  side  is  a  mirror  image  of  the  other.  Skewed  distribution:  scores  pile  up  on  one  side  and  taper  off  in  the  tail.  If  the  longer  side  of  the  curve  is  to  the  right  its   positive  skewedness  to  the  left  negative  skewedness.  Skewedness:  a  measure  of  asymmetry.  Kurtosis:  relative  height  of  the  curve’s  middle  peak  (level  of  peakedness)     •   If  it  has  a  leptokurtic  curve  it  has  fewer  differences  higher  peak  if  its  normal  which  is  mesokurtic  then  its  normal  and  if  its  platykurtic  its  flat  which  is  more  differences  than  leptokurtic   •   Central  tendency:  statistical  measure,  how  scores  tend  to  cluster  in  a  particular  distribution,  purpose  is  to  find  single  score  that  is  most  typical  or  best  represents  entire  group.  3  ms:  mean   median  amd  mode     •   When  mode  equals  mean  equals  median  there  is  a  normal  curve.  If  the  mean  minus  the  median  is  greater  than  zero  than  the  distribution  is  positively  skewed  if  mean  minus  median  is  less   than  zero  distribution  is  negatively  skewed   •   Mean  is  appropriate  for:  calculating  the  sum  of  all  X,  and  knowing  the  value  of  every  score,  it  is  not  appropriate:  for  extreme  scores,  skewed  distribution,  undetermined,  open  ended   distribution,  ordinal,  nominal   •   Median  good  for  extreme  scores,  skewed  distribution,  undetermined  values,  open  ended  distribution  not  good  for  nominal  data     •   Mode  good  for  nominal  data,  categorical  variables,  and  describing  shape,  not  good  for  interval  or  ratio  data     •   Measures  of  dispersion:  variability:  distance  of  the  spread  of  scores  and  the  purpose  is  to  describe  distribution   o   Variance  and  standard  deviation  is  most  important  measure  of  dispersion:  standard  deviation  and  variance  describe  whether  the  scores  a re  clustered  closely  around  the  mean   or  are  widely  scattered     •   Sampling:  process  used  to  draw  samples,  the  goal  is  to  generalize  and  provide  a  base  estimate  population     •   Probability  sampling:  process  through  which  each  individual  of  the  population  can  be  selected  with  each  observation  having  equal  chance  of  being  selected.     •   Statistic:  a  characteristic  of  a  sample.  Parameter:  characteristic  of  a  population.  Statistical  Inference:  a  process  by  which  parameters  can  be  estimated   •   Population  Distribution:  frequency  which  all  observations  in  the  population  would  be  expected  or  assigned  in  each  category  so  where  you  use  the  entire  population.  Sample:  you  only  use   some  samples.  Sampling:  you  use  the  same  sample  but  you  do  it  more  than  once     •   Standard  Deviation  and  Normal  Curve:  When  scores  are  normally  distributed  the  2/3  rule  says  that  about  2/3  of  the  scores  or  68%  lies  within  1  standard  deviation  away  and  negative  1   standard  deviation  away  from  the  mean  so  between  -­‐1SD  and  +1SD   •   95%  rule:  about  95%  of  scores  lie  within  +2  and  -­‐2  standard  deviations  from  the  mean     •   Normal  Curve:  mostly  bell  shaped  highest  frequency  is  in  the  middle  and  its   symmetrical     •   The  larger  the  sample  size  the  closer  it  will  distribute  in  a  normal  curve     •   Standard  Error:  the  standard  deviation  of  a  sampling  distribution  equals  SD  divided  by  the  square  root  of  the  sample  size     o   Quantifies  how  precisely  you  know  the  true  mean  of  the  population  it  takes  into  account  the  standard  deviation  and  the  sample  size     •   Statistical  Inference:  when  we  know  nothing  about  the  population  except  that  it  is  normally  distributed  what  can  we  say  about  it  based  on  what  we  have  from  the  sample?   o   In  other  words,  how  can  we  estimate  the  population  parameters  based  on  sampling  st atistic     •   Standard  scores:  specifies  how  far  a  specific  unit  (person)  is  above  or  below  the  sample  mean  (x-­‐m/sd)   •   Confidence  interval  for  the  mean:  a  range  of  values  within  which  there  can  be  some  degree  of  confidence  that  the  population  parameter  is  likely  to  fall  under     •   When  population  mean  is  unknown  the  sample  mean  is  best  point  estimate        


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