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Chapter 1

by: Kiva Thompson

Chapter 1 STAT-UB 103

Kiva Thompson

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About this Document

Statistics, Data, & Statistical Thinking
Statistics for Business and Economics
Dr. Andrew Patton
Class Notes
Math, Statistics, Economics
25 ?




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This 40 page Class Notes was uploaded by Kiva Thompson on Monday February 1, 2016. The Class Notes belongs to STAT-UB 103 at New York University taught by Dr. Andrew Patton in Spring 2016. Since its upload, it has received 46 views. For similar materials see Statistics for Business and Economics in Economcs at New York University.


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Date Created: 02/01/16
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It involves Stcoilnatyngnmg,ansfyiigr,semtmgarizing,ic 8 ConMsrucials ConFinaeriPlTreensces Areas   Engineering Business • • Application Forecastigraphics Individual & Team Performance Economics Sports • • 9  rocesses Two   Statistics: Describaidg sDtsdecnsieds,pledictsnnsaekt 10 Inferentialcs Methods   Statistical Statistical Statistics Descriptive = 113 2 Q1 Q2 Q3 Q4 $ Sales  = 30.5 S 0 Statistics 50 25 Descriptive • •Co Prctnn•nctDrzirgbe aata 1. Involves 2. Purpose Population? Statistics Inferential Testing population characteristics • •EstHimypitnesis • Make decisions about 1. Involves 2. Purpose 13 1.3 of Statistics Fundamental Elements 14 Elements   Object upon which we collect data Fundamental All items of interest •g: Person• • • •lArnphtchosdnttNWYalhar-i5Niew01ork state 1. Exp 2.rim Ponptalatioin:: 15 Elements   a person; gender; employment status Fundamental Characteristic of an individual experimental unit Subset of the units of a population Income of a person;PeegnswranedrhliephetPtdatwieoliegimtencyo;paipo • • • • • • • 3. Variable: 4. Sample: 16  lements Fundamental population bancertniitfarsoatatedonttinedsatisiamlp • Estimate•or Sedtitennt(rguallrqlzalfind)aouott the degree of 5. Measure of Reliability       51      s  ha nd             ach    the  ample   AB   ess  f by  he age    s  iewers    f    nd  iewers  OX  ears)  go  BC  iewers  OX  in  elected  verCBS,xecut 00  ll  he    OX  f :ge average  n  f  et  iewers   opulation  egage amples  he he  010),   :he  nterest 00 use f  0  he  f  he :  roadcast each. :  ge (Aug  et orkagof rivale     hypothesis,lat ariable i  nf verage  .1  arietygra hat  he  he  he  he  o  V  er he  f  uppos est  stimate  o . viewer executtive Exa A ccoprdlng51.otdetea) Descb) Describe s)ribeescribe 19  roblems  tatistical  escriptive of    lements popiulesoinatrsample units) that are to be Four 1. Th2 e.populatirm3orrTaareablesnthfcaaitnro 20 Problems    tatistical  nferential  f  lements population infos)taaionrconbteiivdentig Five 1. T 2he pnpu.lrihersaintaberenoasureiufrsnii 21 1.5 Types of Data 22 Data Qualitative Data    f Data Types of Types Data Quantitative 23  ata of   Types are measurements that are recorded on a are measurements that cannot be measured • • • •necrnee,nhegbr,• • • •letren,dflebeadswyasdmade (cash, credit card, check) Quantitative dataQualionoaneaouraalnuupeoricalegaleeth.ey can only be classified into 24 1.6 Collecting Data Data   : researcher exerts strict control over Obtaining : units are observed in natural setting and : book, journal, newspaper, Web site published the data? : a group of people are surveyed and their responses are who But WisociauscieiWnbtuywlseeniVbs,inensmapoiscillici • units recorded variables of interest are recorded 1. Pub 2.isDeesiouerd.expuervmeent. Observation study nxperimental units is a sample Samples of exhibits characteristics typical of those has an equal chance of selection. n representative samplem sample A possessed by the population of interest.such a way that every different  ample Random nas an equal chance of selection. Every sample of size 28  election  f in some way, then the inferences may BIAS Importance Hostatssisedittbe incorrectofaaeroorcsaifnhesoffvitedseoped 29 and  amples  urvey in   bias   : failing to deal with the fact that not all units  f : when one subset of the population has a lowerat units that are still “alive” today and Types probability of beinsglsamepledrsaansiniotherteaoldrrespinfertothessurvey • • • • • • 1. Selection bia2 s. Nonrespon 3s.e Siasvival bias: 30 Sampling   used when the experimental units sample natural grouping of experimental units Random associapedowfuthis.e population can be separated into two or morethin each cluster • • 1. Stratified ran 2. oClsasterlsampling 31 th k  ampling systematically selecting every Random experimental unit from a list of all experimental units. • 3. Systematic sampling 32 ) whether you  ampling truthfully useful when the questions of a pollster  esponse , say ‘Yes.’( tell me (the interviewer) the coin outcome. not taiheads Randomized If the coin came upuptaxes in 2014.” Ranare liklyinsdecptsaloaskownosrdsps.bAidtfktsmnwkeysu.heoetap 33  ampling  esponse Randomized + Probability(“Heads”)*Probability(“Cheated on taxes”) = Probability(“T1.s”)0.5 * Probability(“Cheated on taxes”) Probability(“Yes”) SotiewoeuraostmbEit,orProbprdpitniones”)u= 36 1.7 Critical Thinking with Statistics  hinking Statistical involves applying rational thought and the science of Ststatstcestoicthitallriatonsexaiasandponferencens.Funpdamcestala Problem   ‐orld Real 39 (collection of Ideas sample   Key and ) data populativnariabdata) Describe 1.experimental unitsllect Types 1)StaDtstcapAtvelications ) 40 experimental units all of population) Ideas   for inference Key (collection ofbset data ( about population based on sample populavariabsample InfereMnceasure of reliability 1. Ide2. Ide3. Collect 5. Types2 ofSnaeretitlApplications 41  deas Key (numerical in nature) (categorical in nature) QuantiQualitative Types o1. Dat2. 42 Ideas   Key Data-Co.llet2. iDed.dsSecexeOebmeenttional 43 Ideas   Key Types 1. Rn2.plSamatdoeu.tem5amesatndsamprlsponse sample 44 Ideas   Key Proble1.s itlNctorobraspSnviebissip bias


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