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## Week 1: Intro to Stats

by: Kaiyana Dudley

7

0

3

# Week 1: Intro to Stats Sociology 210

Kaiyana Dudley

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These notes cover the first week of elementary statistics. These are basic concepts and definitions.
COURSE
Elementary Statistics
PROF.
Jennifer Barber
TYPE
Class Notes
PAGES
3
WORDS
CONCEPTS
Statistics, intro to statistics, Stats, Math, Data Analysis
KARMA
Free

## Popular in Statistics

This 3 page Class Notes was uploaded by Kaiyana Dudley on Thursday August 11, 2016. The Class Notes belongs to Sociology 210 at University of Michigan taught by Jennifer Barber in Fall 2016. Since its upload, it has received 7 views. For similar materials see Elementary Statistics in Statistics at University of Michigan.

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Date Created: 08/11/16
Week  1 Saturday,  September   10,  2016 12:54  PM Introduction  to  Statistics • Why  am  I  taking  this  class? ○ The  main  purpose  is  to  understand  the  basic  ideas  of  statistics  by  learning   how  statistical  procedures  are  done  so  that  you  may  conduct  your  own   quantitative  research ○ You  will  also  learn  about  Stata,  which  is  stats  software • Why  do  I  care? ○ Statistics  is  necessary  everywhere,  especially  in  sociological  scientific   research.  Because  sociology  mainly  studies  human  interactions,  data  is   needed  to  give  credibility  to  theories,  analyses,  and  such. • Ok,  so  what  is  statistics? ○ Stats  is  a  body  of  methods  to  used  to  collect  and  analyze  data   in  two  ways,   to  describe  or  estimate. ○ We  use  stats  to  tell  us  about  a  lot  of  different  numbers  in  a  condensed  way   for  simpler  understanding.   ○ Statistics  is  short  for  statistical  procedures because  they  are  sets  of   mathematical  procedures  or  equations. ○ We  will  be  using  stats  to  make  sense  of  behavioral  sciences  like  sociology   and  psychology. ○ Stats  helps  us  organize,  summarize,  and  communicate  such  information   accurately. • What  exactly  are  we  trying  to  study  with  stats? ○ Basically,  we're  studying  a  cert   opulation… § and  this  isn't  necessarily  a  group  of  people,  but  a  total  set  of  subjects   being  studied… § like  Korean  immigrants,  or  ants  at  a  park,  or  bacteria  in  a  refrigerator. § Put  plainly,  a  population  is  a  set  of  individuals,   the ,  s  of  analysis of  interest  in  a  study. § We  indicate  populations  by  usinp  arameters,  which  are  measurable   factors  that  describe  the  conditions  of  the  population.  Parameters   summarize  the  population,  and  statistics  summarize  the  samples. ○ Populations  can  be  really  big,  right?  So  in  order  to  work  with  a  more   manageable  group,  we  use  samples. § A  sample  is  used  to  represent  the  population.    The  difference   § We  indicate  populations  by  usinp  arameters,  which  are  measurable   factors  that  describe  the  conditions  of  the  population.  Parameters   summarize  the  population,  and  statistics  summarize  the  samples. ○ Populations  can  be  really  big,  right?  So  in  order  to  work  with  a  more   manageable  group,  we  use  samples. § A  sample  is  used  to  represent  the  population.    The  difference   between  the  parameter  and  the  statistic  is  called  the   sampling  error.   □ The  sampling  error  exists  due  to  the  fact  that  we  cannot   account  for  the  entire  population. □ This  figure  from  the  text  shows  a  population,  parameters,   possible  sampling  and  errors,  and  the  statistics: • • How  else  are  stats  measured? ○ Like  I  said  in  the  beginning,  there  are  two  basic  statistical  methods:  to   describe  and  summarize,  or  to  estimate  and  infer. § The  correlational(  nonexperimental)   methodmerely  describes  the   relationships  between  variables  for  each  individual  of  the  population   without  explaining  why. § The  experimental  method uses  an  independent  variable  to  create   conditions  to  measure  dependent  variables. □ The  use  of  an  independent  variable  presents ao   ntrolled condition  to  provide  a  baseline  for  comparisons. □ The  comparisons  are  seen  in  the  dependent  variables   without  explaining  why. § The  experimental  method uses  an  independent  variable  to  create   conditions  to  measure  dependent  variables. □ The  use  of  an  independent  variable  presents ao   ntrolled condition  to  provide  a  baseline  for  comparisons. □ The  comparisons  are  seen  in  the  dependent  variables   □ We  then  can  compare  the  scores,  or  constants,  or  numbers   (they're  all  the  same).  The  score  is  fixed  and  cannot  change. ○ Measuring  variables  produces  a  score  for  each  unit.  Scores  are  represented   by  X  (like  X=10).  When  there  are  two  variables,  then  you  have  an  X  and  Y   score  or  value. § N  is  used  for  the  number  of  scores  in  a  n fo  r  the   sample. § Sigma  Σ) represents the summation of all of the scores. Order of operations (PEMDAS) applies. § A  discrete  variable     usually  has  whole  numbers  that  vary  in  countable   steps. § A  continuous  variable   as  infinitely  divisible  categories,  and  each   score  corresponds  to  an  interval  on  the  scale. ○ There  are  5  scales  to  measure  and  categorize  units  of  analysis. § A  measurement  scale   has  categories  that  classify  the  units  of   analysis. § A  nominal  scale  have  categories  that  only  differ  by  name  rather  than   magnitude  or  direction. § An  ordinal  scale   differentiates  by  direction  of  an  ordered  series. § An  interval  scal  as  ordered  categories  with  equal  intervals. § A  ratio  scale  is  an  interval  scale  where  ratios  of  measurements  reflect   ratios  of  magnitude. ○ Later  in  the  semester,  will  learn  how  to  measure  samples  in  3  specific  ways. 1) Independent  samples  t  test 2) Chi-­‐Square  tests 3) Linear  Regression References Essentials  of  Statistics  for  the  Behavioral  Sciences  by  Frederick  J  Gravetter,  Larry  B.  Wallnau Jennifer  Barber  Lecture  1  Slide  Presentation  (September  7,  2016)

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