To make comparisons for two populations, consider whether the samples are independent or dependent.
|
|
- Denis Williamson
- 6 years ago
- Views:
Transcription
1 Sociology 54 Testig for differeces betwee two samle meas Cocetually, comarig meas from two differet samles is the same as what we ve doe i oe-samle tests, ecet that ow the hyotheses focus o the arameters of two oulatios. To make comarisos for two oulatios, cosider whether the samles are ideedet or deedet. Ideedet samles: Selectio of members of oe samle has o ifluece o the selectio of members of the other samle. Deedet samles: Selectio of members for oe samle determies the characteristics of the members for the other samle. Whe we comare two grous, we still state two cometig hyotheses. With two samles, we are ow dealig with a samlig distributio of differeces betwee samle meas. The outcome of iterest is the differece betwee two samle statistics (e.g. the differece i mea hours set o housework betwee me ad wome). Just as the samlig distributio of samle meas is ormal, so is the samlig distributio of differeces betwee samle meas (a corollary of the Cetral Limit Theorem). Coductig a hyothesis test comarig two samles: Calculatig a test statistic to determie whether the differece betwee two samle meas is real or simly due to chace variatio is cocetually the same as what we ve already reviewed for a sigle samle. To comare two grous o a quatitative characteristic, we make ifereces about their oulatio meas µ ad µ ad the differece betwee them. Test statistic = (observed differece - eected differece) / amout of variability i samlig distributio of differeces t = ( ) ( µ µ ) ˆ Note that idetificatio of which grou is called ad which is called is arbitrary. What is the stadard error of the samlig distributio of the estimated differece betwee the two samle meas? That is, the degree to which the differece would vary if we reeatedly took samles of size ad.
2 Comarig samle meas (iterval measures) of two ideedet LARGE samles with uequal variaces (>00 for each samle) s s = Where s = stadard deviatio of samle s = stadard deviatio of samle = size of samle = size of samle Whe two estimates are formed from ideedet samles, the samlig distributio of their differece has variace equal to the sum of the variaces of the samlig distributio of the searate estimates. The corresodig test statistic is: z = ( ) ( µ µ ) ˆ Recall that oce you ve calculated the test statistic for your two samles, you ca fid the aroriate -value that corresods to that statistic. That is, what is the likelihood that you would draw two samles with a differece as large as that observed, if i fact there really were o differece betwee the two grous? If the robability is small, we may reject the ull hyothesis ad tetatively accet the research hyothesis. Guidelies to test for statistically sigificat differeces betwee meas of two grous.. Choose a sigificace (α) level.. State hyotheses. 3. Calculate the aroriate test statistic. 4. Determie the -value associated with the test statistic. 5. Draw coclusios.
3 Two ideedet LARGE samles A samle of 000 studets draw from a ublic uiversity fids that studets work. hours er week while a samle of 900 studets draw from a rivate uiversity fids that their studets work a average of 9. hours er week. The samle stadard deviatios are 0.8 hours for the ublic uiversity ad 9.6 hours for the rivate uiversity. Is there a sigificat differece i the umber of hours worked i ublic versus rivate uiversities? 3
4 4 Comarig samle meas for two ideedet SMALL samles (iterval measures). You must make the assumtio that the oulatio variaces are equal to use this formula (Kurtz ). First, obtai a ooled estimate of the stadard deviatio for the two grous: Although we assume that the samle variaces are equal, to obtai the best ossible estimate of the oulatio variace, we take a weighted average of the two variaces rather tha arbitrarily choose oe of them as the estimate. The obtai the estimated stadard error of the samlig distributio of differeces usig the ooled estimate of the stadard deviatio: This is equivalet to: The aroriate test statistic is: ˆ ) ( ) ( t = µ µ with degrees of freedom = - ) ( ) ( ˆ = s s ˆ ˆ = ˆ ˆ =
5 Two ideedet small samles (assume oulatio variaces are equal) The followig two samles idicate salaries for male ad female rofessors (i 969!). Could these differeces i salary arise just by chace? Samle of male rofessors: Samle of female rofessors: N=0 N=5 = 6 (salary figure i 000's) = s = 3.5 s =.83 5
6 6 Comarig roortios for two ideedet large samles (Kurtz.9-95) Let's say that you have two ideedet samles with dichotomous measures. Is that dichotomous variable distributed similarly i the two oulatios? We are ow comarig samles with a qualitative resose variable. This test requires that both samles have 30 or more members, ad the resultig statistic is a z score. The test statistic is the ratio of the differece betwee two samle roortios to the stadard error of the two roortios. The test statistic is estimated with the followig equatio: Test Statistic: ( ) = ) ) ( z c c where c =. c = is a weighted average of the two roortios to adjust if the two samles are of uequal size.
7 Two ideedet large samles (comarig roortios) A study foud the followig results: Of 3 male studets, 53% worked more tha 8 hours er week. Of 79 female studets, 48% worked more tha 8 hours er week. Is there a sigificat differece betwee male ad female studets i the umber of hours worked? 7
8 SPSS Oe-Samle t Test A ewly created radom umber geerator is suosed to geerate a sequece of digits such that each digit is equally likely to be ay of 0,,,, 9. The first 0 umbers geerated are: As a check of whether the rocess works correctly, test whether the mea differs sigificatly from the value eected. Reort the -value ad iterret. You should also do this by had ad cofirm that you get the same results as those reorted by SPSS. SPSS Commads Aalyze - Comare Meas Oe-Samle T Test Select Test Variable (chage) ad Test Value ( ) Okay Oe-Samle Statistics NUMBERS Std. Error N Mea Std. Deviatio Mea Oe-Samle Test NUMBERS Test Value = % Cofidece Iterval of the Mea Differece t df Sig. (-tailed) Differece Lower Uer
9 Two-Samle t Test Usig GSS98.SAV file, determie whether there is a sigificat differece betwee me ad wome i their resose to the followig questio: ABANY: "Please tell me whether or ot you thik it should be ossible for a regat wome to obtai a legal abortio if... the woma wats it for ay reaso?" SEX: Resodet's Se Remember to use syta file set ritback o. Coduct test for differeces Aalyze Comare Meas Ideedet Samles T test Select test (ABANY) ad grou variables (SEX) - Okay Aother Two-Samle t-test Is there a sigificat differece by age (eole older tha ad youger tha 40) i the resose to the questio above? Recode age ito two categories (ages 0-39 ad 40 ad older) ad coduct a t-test. AGE: Age of Resodet 9
10 ADDITIONAL NOTES FOR SELF STUDY AND FUTURE REFERENCE Comarig two deedet samles with iterval measures (Kurtz ) Deedet samles occur whe each observatio i samle matches with a observatio from samle. (Ofte called matched-airs data). Most commoly occurs whe each samle has the same subjects. A eamle of reeated measuremet data. Studet's T test for Paired Comarisos A secial case of the oe-grou t test usig differece scores (e.g. differece i SAT scores from time to time ) from each air of deedet subjects. For matched-airs data, the differece betwee the meas of the two grous equals the mea of the differece scores. t = δ s δ δ = mea differece score S δ = stadard deviatio of differece score N = size of samle size Note: This formula is equivalet to that reseted o age 96 of Kurtz. See also otes o eamle of SAT scores ad effect of a re course discussed i earlier hadout. Some advatages to aalysis with deedet samles. Kow sources of otetial bias are cotrolled usig same subjects i each samle for eamle kees may ossible cofoudig factors fied.. Stadard error of differece may be smaller with deedet samles Assumtios. Radom ad ideedet samlig. Normality assumtio: Normality assumtio alies to oulatio of differece scores. The deedet grous t test is geerally cosidered robust agaist violatio of this assumtio if N > 30. 0
11 SPSS Oe-Samle t Test Use ch7.sav i soc 54 work directory Differece scores for studets who've take a SAT re course. File Name: Aalyze - Comare Meas Oe-Samle T Test Select Test Variable (chage) ad Test Value (0) Okay Oe-Samle Statistics CHANGE Std. Error N Mea Std. Deviatio Mea Oe-Samle Test CHANGE Test Value = 0 95% Cofidece Iterval of the Mea Differece t df Sig. (-tailed) Differece Lower Uer
12 You could also comare these two grous usig a aired samle t-test (these are deedet samles) Paired Samle t-test Usig the dataset cotaiig 0 observatios o re ad ost SAT scores, use the aired samle t- test to determie whether there is a sigificat differece betwee the two scores. Commads: Aalyze Comare Meas Paired Samle t-test Select aired variables (variable ad variable ) - Okay Syta: T-TEST PAIRS= origscre WITH ewscore (PAIRED) /CRITERIA=CIN(.95) /MISSING=ANALYSIS. Paired Samles Test Pair ORIGSCRE - NEWSCORE Mea Paired Differeces 95% Cofidece Iterval of the Std. Error Differece Std. Deviatio Mea Lower Uer t df Sig. (-tailed) Note that these two tests yield eactly the same result.
18. Two-sample problems for population means (σ unknown)
8. Two-samle roblems for oulatio meas (σ ukow) The Practice of Statistics i the Life Scieces Third Editio 04 W.H. Freema ad Comay Objectives (PSLS Chater 8) Comarig two meas (σ ukow) Two-samle situatios
More informationSTAT-UB.0103 NOTES for Wednesday 2012.APR.25. Here s a rehash on the p-value notion:
STAT-UB.3 NOTES for Wedesday 22.APR.25 Here s a rehash o the -value otio: The -value is the smallest α at which H would have bee rejected, with these data. The -value is a measure of SHOCK i the data.
More informationThe Hong Kong University of Science & Technology ISOM551 Introductory Statistics for Business Assignment 3 Suggested Solution
The Hog Kog Uiversity of ciece & Techology IOM55 Itroductory tatistics for Busiess Assigmet 3 uggested olutio Note All values of statistics i Q ad Q4 are obtaied by Excel. Qa. Let be the robability that
More informationp we will use that fact in constructing CI n for population proportion p. The approximation gets better with increasing n.
Estimatig oulatio roortio: We will cosider a dichotomous categorical variable(s) ( classes: A, ot A) i a large oulatio(s). Poulatio(s) should be at least 0 times larger tha the samle(s). We will discuss
More informationConfidence Intervals
Cofidece Itervals Berli Che Deartmet of Comuter Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Referece: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chater 5 & Teachig Material Itroductio
More informationDistribution of Sample Proportions
Distributio of Samle Proortios Probability ad statistics Aswers & Teacher Notes TI-Nsire Ivestigatio Studet 90 mi 7 8 9 10 11 12 Itroductio From revious activity: This activity assumes kowledge of the
More informationComparing your lab results with the others by one-way ANOVA
Comparig your lab results with the others by oe-way ANOVA You may have developed a ew test method ad i your method validatio process you would like to check the method s ruggedess by coductig a simple
More informationConfidence intervals for proportions
Cofidece itervals for roortios Studet Activity 7 8 9 0 2 TI-Nsire Ivestigatio Studet 60 mi Itroductio From revious activity This activity assumes kowledge of the material covered i the activity Distributio
More informationMATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4
MATH 30: Probability ad Statistics 9. Estimatio ad Testig of Parameters Estimatio ad Testig of Parameters We have bee dealig situatios i which we have full kowledge of the distributio of a radom variable.
More informationCommon Large/Small Sample Tests 1/55
Commo Large/Small Sample Tests 1/55 Test of Hypothesis for the Mea (σ Kow) Covert sample result ( x) to a z value Hypothesis Tests for µ Cosider the test H :μ = μ H 1 :μ > μ σ Kow (Assume the populatio
More informationChapter 9, Part B Hypothesis Tests
SlidesPreared by JOHN S.LOUCKS St.Edward suiversity Slide 1 Chater 9, Part B Hyothesis Tests Poulatio Proortio Hyothesis Testig ad Decisio Makig Calculatig the Probability of Tye II Errors Determiig the
More informationConfidence Intervals for the Difference Between Two Proportions
PASS Samle Size Software Chater 6 Cofidece Itervals for the Differece Betwee Two Proortios Itroductio This routie calculates the grou samle sizes ecessary to achieve a secified iterval width of the differece
More informationEstimating Proportions
3/1/018 Outlie for Today Remiders about Missig Values Iterretig Cofidece Itervals Cofidece About Proortios Proortios as Iterval Variables Cofidece Itervals Cofidece Coefficiets Examles Lab Exercise ( arts
More informationExamination Number: (a) (5 points) Compute the sample mean of these data. x = Practice Midterm 2_Spring2017.lwp Page 1 of KM
Last Name First Sig the Hoor Pledge Below PID # Write Your Sectio Number here: Uiversity of North Carolia Ecoomics 4: Ecoomic Statistics Practice Secod Midterm Examiatio Prof. B. Turchi Srig 7 Geeral Istructios:
More informationChapter 18: Sampling Distribution Models
Chater 18: Samlig Distributio Models This is the last bit of theory before we get back to real-world methods. Samlig Distributios: The Big Idea Take a samle ad summarize it with a statistic. Now take aother
More informationData Analysis and Statistical Methods Statistics 651
Data Aalysis ad Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasii/teachig.html Suhasii Subba Rao Review of testig: Example The admistrator of a ursig home wats to do a time ad motio
More informationFinal Examination Solutions 17/6/2010
The Islamic Uiversity of Gaza Faculty of Commerce epartmet of Ecoomics ad Political Scieces A Itroductio to Statistics Course (ECOE 30) Sprig Semester 009-00 Fial Eamiatio Solutios 7/6/00 Name: I: Istructor:
More informationOverview. p 2. Chapter 9. Pooled Estimate of. q = 1 p. Notation for Two Proportions. Inferences about Two Proportions. Assumptions
Chapter 9 Slide Ifereces from Two Samples 9- Overview 9- Ifereces about Two Proportios 9- Ifereces about Two Meas: Idepedet Samples 9-4 Ifereces about Matched Pairs 9-5 Comparig Variatio i Two Samples
More informationInterval Estimation (Confidence Interval = C.I.): An interval estimate of some population parameter is an interval of the form (, ),
Cofidece Iterval Estimatio Problems Suppose we have a populatio with some ukow parameter(s). Example: Normal(,) ad are parameters. We eed to draw coclusios (make ifereces) about the ukow parameters. We
More informationChapter 13, Part A Analysis of Variance and Experimental Design
Slides Prepared by JOHN S. LOUCKS St. Edward s Uiversity Slide 1 Chapter 13, Part A Aalysis of Variace ad Eperimetal Desig Itroductio to Aalysis of Variace Aalysis of Variace: Testig for the Equality of
More informationRecall the study where we estimated the difference between mean systolic blood pressure levels of users of oral contraceptives and non-users, x - y.
Testig Statistical Hypotheses Recall the study where we estimated the differece betwee mea systolic blood pressure levels of users of oral cotraceptives ad o-users, x - y. Such studies are sometimes viewed
More informationGeneral Instructions:
Eamiatio Number: Last Name First Sig the Hoor Pledge Below PID # Write Your Sectio Number here: Uiversity of North Carolia Ecoomics 400: Ecoomic Statistics Secod Midterm Eamiatio Prof. B. Turchi Aril 6,
More informationtests 17.1 Simple versus compound
PAS204: Lecture 17. tests UMP ad asymtotic I this lecture, we will idetify UMP tests, wherever they exist, for comarig a simle ull hyothesis with a comoud alterative. We also look at costructig tests based
More informationChapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc.
Chapter 22 Comparig Two Proportios Copyright 2010, 2007, 2004 Pearso Educatio, Ic. Comparig Two Proportios Read the first two paragraphs of pg 504. Comparisos betwee two percetages are much more commo
More informationClass 27. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 7 Daiel B. Rowe, Ph.D. Departmet of Mathematics, Statistics, ad Computer Sciece Copyright 013 by D.B. Rowe 1 Ageda: Skip Recap Chapter 10.5 ad 10.6 Lecture Chapter 11.1-11. Review Chapters 9 ad 10
More informationThis chapter focuses on two experimental designs that are crucial to comparative studies: (1) independent samples and (2) matched pair samples.
Chapter 9 & : Comparig Two Treatmets: This chapter focuses o two eperimetal desigs that are crucial to comparative studies: () idepedet samples ad () matched pair samples Idepedet Radom amples from Two
More informationHypothesis Testing. Evaluation of Performance of Learned h. Issues. Trade-off Between Bias and Variance
Hypothesis Testig Empirically evaluatig accuracy of hypotheses: importat activity i ML. Three questios: Give observed accuracy over a sample set, how well does this estimate apply over additioal samples?
More informationComparing Two Populations. Topic 15 - Two Sample Inference I. Comparing Two Means. Comparing Two Pop Means. Background Reading
Topic 15 - Two Sample Iferece I STAT 511 Professor Bruce Craig Comparig Two Populatios Research ofte ivolves the compariso of two or more samples from differet populatios Graphical summaries provide visual
More informationExpectation and Variance of a random variable
Chapter 11 Expectatio ad Variace of a radom variable The aim of this lecture is to defie ad itroduce mathematical Expectatio ad variace of a fuctio of discrete & cotiuous radom variables ad the distributio
More informationChapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc.
Chapter 22 Comparig Two Proportios Copyright 2010 Pearso Educatio, Ic. Comparig Two Proportios Comparisos betwee two percetages are much more commo tha questios about isolated percetages. Ad they are more
More informationHypothesis Testing. H 0 : θ 1 1. H a : θ 1 1 (but > 0... required in distribution) Simple Hypothesis - only checks 1 value
Hyothesis estig ME's are oit estimates of arameters/coefficiets really have a distributio Basic Cocet - develo regio i which we accet the hyothesis ad oe where we reject it H - reresets all ossible values
More information- E < p. ˆ p q ˆ E = q ˆ = 1 - p ˆ = sample proportion of x failures in a sample size of n. where. x n sample proportion. population proportion
1 Chapter 7 ad 8 Review for Exam Chapter 7 Estimates ad Sample Sizes 2 Defiitio Cofidece Iterval (or Iterval Estimate) a rage (or a iterval) of values used to estimate the true value of the populatio parameter
More information1 Constructing and Interpreting a Confidence Interval
Itroductory Applied Ecoometrics EEP/IAS 118 Sprig 2014 WARM UP: Match the terms i the table with the correct formula: Adrew Crae-Droesch Sectio #6 5 March 2014 ˆ Let X be a radom variable with mea µ ad
More informationBIOS 4110: Introduction to Biostatistics. Breheny. Lab #9
BIOS 4110: Itroductio to Biostatistics Brehey Lab #9 The Cetral Limit Theorem is very importat i the realm of statistics, ad today's lab will explore the applicatio of it i both categorical ad cotiuous
More informationSample Size Determination (Two or More Samples)
Sample Sie Determiatio (Two or More Samples) STATGRAPHICS Rev. 963 Summary... Data Iput... Aalysis Summary... 5 Power Curve... 5 Calculatios... 6 Summary This procedure determies a suitable sample sie
More informationContinuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised
Questio 1. (Topics 1-3) A populatio cosists of all the members of a group about which you wat to draw a coclusio (Greek letters (μ, σ, Ν) are used) A sample is the portio of the populatio selected for
More informationThis is an introductory course in Analysis of Variance and Design of Experiments.
1 Notes for M 384E, Wedesday, Jauary 21, 2009 (Please ote: I will ot pass out hard-copy class otes i future classes. If there are writte class otes, they will be posted o the web by the ight before class
More informationA quick activity - Central Limit Theorem and Proportions. Lecture 21: Testing Proportions. Results from the GSS. Statistics and the General Population
A quick activity - Cetral Limit Theorem ad Proportios Lecture 21: Testig Proportios Statistics 10 Coli Rudel Flip a coi 30 times this is goig to get loud! Record the umber of heads you obtaied ad calculate
More informationSTAC51: Categorical data Analysis
STAC51: Categorical data Aalysis Mahida Samarakoo Jauary 28, 2016 Mahida Samarakoo STAC51: Categorical data Aalysis 1 / 35 Table of cotets Iferece for Proportios 1 Iferece for Proportios Mahida Samarakoo
More informationSTA Learning Objectives. Population Proportions. Module 10 Comparing Two Proportions. Upon completing this module, you should be able to:
STA 2023 Module 10 Comparig Two Proportios Learig Objectives Upo completig this module, you should be able to: 1. Perform large-sample ifereces (hypothesis test ad cofidece itervals) to compare two populatio
More informationLecture 5: Parametric Hypothesis Testing: Comparing Means. GENOME 560, Spring 2016 Doug Fowler, GS
Lecture 5: Parametric Hypothesis Testig: Comparig Meas GENOME 560, Sprig 2016 Doug Fowler, GS (dfowler@uw.edu) 1 Review from last week What is a cofidece iterval? 2 Review from last week What is a cofidece
More informationBIOSTATISTICAL METHODS FOR TRANSLATIONAL & CLINICAL RESEARCH
BIOSAISICAL MEHODS FOR RANSLAIONAL & CLINICAL RESEARCH Direct Bioassays: REGRESSION APPLICAIONS COMPONENS OF A BIOASSAY he subject is usually a aimal, a huma tissue, or a bacteria culture, he aget is usually
More informationStat 200 -Testing Summary Page 1
Stat 00 -Testig Summary Page 1 Mathematicias are like Frechme; whatever you say to them, they traslate it ito their ow laguage ad forthwith it is somethig etirely differet Goethe 1 Large Sample Cofidece
More informationSection 9.2. Tests About a Population Proportion 12/17/2014. Carrying Out a Significance Test H A N T. Parameters & Hypothesis
Sectio 9.2 Tests About a Populatio Proportio P H A N T O M S Parameters Hypothesis Assess Coditios Name the Test Test Statistic (Calculate) Obtai P value Make a decisio State coclusio Sectio 9.2 Tests
More information1 Constructing and Interpreting a Confidence Interval
Itroductory Applied Ecoometrics EEP/IAS 118 Sprig 2014 WARM UP: Match the terms i the table with the correct formula: Adrew Crae-Droesch Sectio #6 5 March 2014 ˆ Let X be a radom variable with mea µ ad
More informationChapter 23: Inferences About Means
Chapter 23: Ifereces About Meas Eough Proportios! We ve spet the last two uits workig with proportios (or qualitative variables, at least) ow it s time to tur our attetios to quatitative variables. For
More informationPubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH
PubH 7470: AIIC FOR RANLAIONAL & CLINICAL REEARCH ulemet for Aalysis: Use of FIELLER HEOREM for HE EIMAION OF RAIO HE GAP Most teachig ad learig rograms i tatistics ad Biostatistics ours icluded - focus
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationChapter 1 (Definitions)
FINAL EXAM REVIEW Chapter 1 (Defiitios) Qualitative: Nomial: Ordial: Quatitative: Ordial: Iterval: Ratio: Observatioal Study: Desiged Experimet: Samplig: Cluster: Stratified: Systematic: Coveiece: Simple
More informationGoodness-of-Fit Tests and Categorical Data Analysis (Devore Chapter Fourteen)
Goodess-of-Fit Tests ad Categorical Data Aalysis (Devore Chapter Fourtee) MATH-252-01: Probability ad Statistics II Sprig 2019 Cotets 1 Chi-Squared Tests with Kow Probabilities 1 1.1 Chi-Squared Testig................
More informationFrequentist Inference
Frequetist Iferece The topics of the ext three sectios are useful applicatios of the Cetral Limit Theorem. Without kowig aythig about the uderlyig distributio of a sequece of radom variables {X i }, for
More information7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals
7-1 Chapter 4 Part I. Samplig Distributios ad Cofidece Itervals 1 7- Sectio 1. Samplig Distributio 7-3 Usig Statistics Statistical Iferece: Predict ad forecast values of populatio parameters... Test hypotheses
More informationLinear Regression Models
Liear Regressio Models Dr. Joh Mellor-Crummey Departmet of Computer Sciece Rice Uiversity johmc@cs.rice.edu COMP 528 Lecture 9 15 February 2005 Goals for Today Uderstad how to Use scatter diagrams to ispect
More information= p x (1 p) 1 x. Var (X) =p(1 p) M X (t) =1+p(e t 1).
Prob. fuctio:, =1 () = 1, =0 = (1 ) 1 E(X) = Var (X) =(1 ) M X (t) =1+(e t 1). 1.1.2 Biomial distributio Parameter: 0 1; >0; MGF: M X (t) ={1+(e t 1)}. Cosider a sequece of ideedet Ber() trials. If X =
More informationClass 23. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 23 Daiel B. Rowe, Ph.D. Departmet of Mathematics, Statistics, ad Computer Sciece Copyright 2017 by D.B. Rowe 1 Ageda: Recap Chapter 9.1 Lecture Chapter 9.2 Review Exam 6 Problem Solvig Sessio. 2
More informationInstructor: Judith Canner Spring 2010 CONFIDENCE INTERVALS How do we make inferences about the population parameters?
CONFIDENCE INTERVALS How do we make ifereces about the populatio parameters? The samplig distributio allows us to quatify the variability i sample statistics icludig how they differ from the parameter
More information1 Inferential Methods for Correlation and Regression Analysis
1 Iferetial Methods for Correlatio ad Regressio Aalysis I the chapter o Correlatio ad Regressio Aalysis tools for describig bivariate cotiuous data were itroduced. The sample Pearso Correlatio Coefficiet
More informationChapter 11: Asking and Answering Questions About the Difference of Two Proportions
Chapter 11: Askig ad Aswerig Questios About the Differece of Two Proportios These otes reflect material from our text, Statistics, Learig from Data, First Editio, by Roxy Peck, published by CENGAGE Learig,
More information2 1. The r.s., of size n2, from population 2 will be. 2 and 2. 2) The two populations are independent. This implies that all of the n1 n2
Chapter 8 Comparig Two Treatmets Iferece about Two Populatio Meas We wat to compare the meas of two populatios to see whether they differ. There are two situatios to cosider, as show i the followig examples:
More informationApril 18, 2017 CONFIDENCE INTERVALS AND HYPOTHESIS TESTING, UNDERGRADUATE MATH 526 STYLE
April 18, 2017 CONFIDENCE INTERVALS AND HYPOTHESIS TESTING, UNDERGRADUATE MATH 526 STYLE TERRY SOO Abstract These otes are adapted from whe I taught Math 526 ad meat to give a quick itroductio to cofidece
More informationHYPOTHESIS TESTS FOR ONE POPULATION MEAN WORKSHEET MTH 1210, FALL 2018
HYPOTHESIS TESTS FOR ONE POPULATION MEAN WORKSHEET MTH 1210, FALL 2018 We are resposible for 2 types of hypothesis tests that produce ifereces about the ukow populatio mea, µ, each of which has 3 possible
More informationSuccessful HE applicants. Information sheet A Number of applicants. Gender Applicants Accepts Applicants Accepts. Age. Domicile
Successful HE applicats Sigificace tests use data from samples to test hypotheses. You will use data o successful applicatios for courses i higher educatio to aswer questios about proportios, for example,
More informationEcon 325 Notes on Point Estimator and Confidence Interval 1 By Hiro Kasahara
Poit Estimator Eco 325 Notes o Poit Estimator ad Cofidece Iterval 1 By Hiro Kasahara Parameter, Estimator, ad Estimate The ormal probability desity fuctio is fully characterized by two costats: populatio
More informationSTATISTICAL INFERENCE
STATISTICAL INFERENCE POPULATION AND SAMPLE Populatio = all elemets of iterest Characterized by a distributio F with some parameter θ Sample = the data X 1,..., X, selected subset of the populatio = sample
More informationGG313 GEOLOGICAL DATA ANALYSIS
GG313 GEOLOGICAL DATA ANALYSIS 1 Testig Hypothesis GG313 GEOLOGICAL DATA ANALYSIS LECTURE NOTES PAUL WESSEL SECTION TESTING OF HYPOTHESES Much of statistics is cocered with testig hypothesis agaist data
More informationAgreement of CI and HT. Lecture 13 - Tests of Proportions. Example - Waiting Times
Sigificace level vs. cofidece level Agreemet of CI ad HT Lecture 13 - Tests of Proportios Sta102 / BME102 Coli Rudel October 15, 2014 Cofidece itervals ad hypothesis tests (almost) always agree, as log
More informationBecause it tests for differences between multiple pairs of means in one test, it is called an omnibus test.
Math 308 Sprig 018 Classes 19 ad 0: Aalysis of Variace (ANOVA) Page 1 of 6 Itroductio ANOVA is a statistical procedure for determiig whether three or more sample meas were draw from populatios with equal
More informationMA238 Assignment 4 Solutions (part a)
(i) Sigle sample tests. Questio. MA38 Assigmet 4 Solutios (part a) (a) (b) (c) H 0 : = 50 sq. ft H A : < 50 sq. ft H 0 : = 3 mpg H A : > 3 mpg H 0 : = 5 mm H A : 5mm Questio. (i) What are the ull ad alterative
More informationMathacle. PSet Stats, Concepts In Statistics Level Number Name: Date:
PSet ----- Stats, Cocepts I Statistics 7.3. Cofidece Iterval for a Mea i Oe Sample [MATH] The Cetral Limit Theorem. Let...,,, be idepedet, idetically distributed (i.i.d.) radom variables havig mea µ ad
More informationStatistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.
Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized
More information1 Models for Matched Pairs
1 Models for Matched Pairs Matched pairs occur whe we aalyse samples such that for each measuremet i oe of the samples there is a measuremet i the other sample that directly relates to the measuremet i
More informationSampling Distributions, Z-Tests, Power
Samplig Distributios, Z-Tests, Power We draw ifereces about populatio parameters from sample statistics Sample proportio approximates populatio proportio Sample mea approximates populatio mea Sample variace
More informationChapter 6 Sampling Distributions
Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to
More informationMath 140 Introductory Statistics
8.2 Testig a Proportio Math 1 Itroductory Statistics Professor B. Abrego Lecture 15 Sectios 8.2 People ofte make decisios with data by comparig the results from a sample to some predetermied stadard. These
More informationBasics of Inference. Lecture 21: Bayesian Inference. Review - Example - Defective Parts, cont. Review - Example - Defective Parts
Basics of Iferece Lecture 21: Sta230 / Mth230 Coli Rudel Aril 16, 2014 U util this oit i the class you have almost exclusively bee reseted with roblems where we are usig a robability model where the model
More informationParameter, Statistic and Random Samples
Parameter, Statistic ad Radom Samples A parameter is a umber that describes the populatio. It is a fixed umber, but i practice we do ot kow its value. A statistic is a fuctio of the sample data, i.e.,
More informationRead through these prior to coming to the test and follow them when you take your test.
Math 143 Sprig 2012 Test 2 Iformatio 1 Test 2 will be give i class o Thursday April 5. Material Covered The test is cummulative, but will emphasize the recet material (Chapters 6 8, 10 11, ad Sectios 12.1
More informationST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n.
ST 305: Exam 3 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad the basic
More information1036: Probability & Statistics
036: Probability & Statistics Lecture 0 Oe- ad Two-Sample Tests of Hypotheses 0- Statistical Hypotheses Decisio based o experimetal evidece whether Coffee drikig icreases the risk of cacer i humas. A perso
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationStatistics Definition: The science of assembling, classifying, tabulating, and analyzing data or facts:
8. Statistics Statistics Defiitio: The sciece of assemblig, classifyig, tabulatig, ad aalyzig data or facts: Descritive statistics The collectig, grouig ad resetig data i a way that ca be easily uderstood
More informationPower and Type II Error
Statistical Methods I (EXST 7005) Page 57 Power ad Type II Error Sice we do't actually kow the value of the true mea (or we would't be hypothesizig somethig else), we caot kow i practice the type II error
More informationChapter 20. Comparing Two Proportions. BPS - 5th Ed. Chapter 20 1
Chapter 0 Comparig Two Proportios BPS - 5th Ed. Chapter 0 Case Study Machie Reliability A study is performed to test of the reliability of products produced by two machies. Machie A produced 8 defective
More informationLecture 8: Non-parametric Comparison of Location. GENOME 560, Spring 2016 Doug Fowler, GS
Lecture 8: No-parametric Compariso of Locatio GENOME 560, Sprig 2016 Doug Fowler, GS (dfowler@uw.edu) 1 Review What do we mea by oparametric? What is a desirable locatio statistic for ordial data? What
More informationChapter two: Hypothesis testing
: Hypothesis testig - Some basic cocepts: - Data: The raw material of statistics is data. For our purposes we may defie data as umbers. The two kids of umbers that we use i statistics are umbers that result
More informationChapter 8: Estimating with Confidence
Chapter 8: Estimatig with Cofidece Sectio 8.2 The Practice of Statistics, 4 th editio For AP* STARNES, YATES, MOORE Chapter 8 Estimatig with Cofidece 8.1 Cofidece Itervals: The Basics 8.2 8.3 Estimatig
More informationStat 139 Homework 7 Solutions, Fall 2015
Stat 139 Homework 7 Solutios, Fall 2015 Problem 1. I class we leared that the classical simple liear regressio model assumes the followig distributio of resposes: Y i = β 0 + β 1 X i + ɛ i, i = 1,...,,
More informationENGI 4421 Confidence Intervals (Two Samples) Page 12-01
ENGI 44 Cofidece Itervals (Two Samples) Page -0 Two Sample Cofidece Iterval for a Differece i Populatio Meas [Navidi sectios 5.4-5.7; Devore chapter 9] From the cetral limit theorem, we kow that, for sufficietly
More information5. A formulae page and two tables are provided at the end of Part A of the examination PART A
Istructios: 1. You have bee provided with: (a) this questio paper (Part A ad Part B) (b) a multiple choice aswer sheet (for Part A) (c) Log Aswer Sheet(s) (for Part B) (d) a booklet of tables. (a) I PART
More informationConfidence Interval for Standard Deviation of Normal Distribution with Known Coefficients of Variation
Cofidece Iterval for tadard Deviatio of Normal Distributio with Kow Coefficiets of Variatio uparat Niwitpog Departmet of Applied tatistics, Faculty of Applied ciece Kig Mogkut s Uiversity of Techology
More informationExam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234
STA 291 Lecture 19 Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Locatio CB 234 STA 291 - Lecture 19 1 Exam II Covers Chapter 9 10.1; 10.2; 10.3; 10.4; 10.6
More informationStatistics 511 Additional Materials
Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability
More informationOctober 25, 2018 BIM 105 Probability and Statistics for Biomedical Engineers 1
October 25, 2018 BIM 105 Probability ad Statistics for Biomedical Egieers 1 Populatio parameters ad Sample Statistics October 25, 2018 BIM 105 Probability ad Statistics for Biomedical Egieers 2 Ifereces
More information(7 One- and Two-Sample Estimation Problem )
34 Stat Lecture Notes (7 Oe- ad Two-Sample Estimatio Problem ) ( Book*: Chapter 8,pg65) Probability& Statistics for Egieers & Scietists By Walpole, Myers, Myers, Ye Estimatio 1 ) ( ˆ S P i i Poit estimate:
More informationIntroduction to Econometrics (3 rd Updated Edition) Solutions to Odd- Numbered End- of- Chapter Exercises: Chapter 3
Itroductio to Ecoometrics (3 rd Updated Editio) by James H. Stock ad Mark W. Watso Solutios to Odd- Numbered Ed- of- Chapter Exercises: Chapter 3 (This versio August 17, 014) 015 Pearso Educatio, Ic. Stock/Watso
More informationComputing Confidence Intervals for Sample Data
Computig Cofidece Itervals for Sample Data Topics Use of Statistics Sources of errors Accuracy, precisio, resolutio A mathematical model of errors Cofidece itervals For meas For variaces For proportios
More informationPH 425 Quantum Measurement and Spin Winter SPINS Lab 1
PH 425 Quatum Measuremet ad Spi Witer 23 SPIS Lab Measure the spi projectio S z alog the z-axis This is the experimet that is ready to go whe you start the program, as show below Each atom is measured
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationRegression, Inference, and Model Building
Regressio, Iferece, ad Model Buildig Scatter Plots ad Correlatio Correlatio coefficiet, r -1 r 1 If r is positive, the the scatter plot has a positive slope ad variables are said to have a positive relatioship
More informationTI-83/84 Calculator Instructions for Math Elementary Statistics
TI-83/84 Calculator Itructio for Math 34- Elemetary Statitic. Eterig Data: Data oit are tored i Lit o the TI-83/84. If you have't ued the calculator before, you may wat to erae everythig that wa there.
More information