ANOVA with Summary Statistics: A STATA Macro

Size: px
Start display at page:

Download "ANOVA with Summary Statistics: A STATA Macro"

Transcription

1 ANOVA wth Summary Stattc: A STATA Macro Nadeem Shafque Butt Departmet of Socal ad Prevetve Pedatrc Kg Edward Medcal College, Lahore, Pata Shahd Kamal Ittute of Stattc, Uverty of the Puab Lahore, Pata Muhammad Qaer Shahbaz GC Uverty, Lahore, Pata Abtract Almot all avalable tattcal pacage are capable of performg Aaly of Varace (ANOVA) from raw data. Some of tattcal pacage have capablty to perform depedet ample t-tet, ad ome other tet of gfcace o ummary data, but eldom would you come acro a oftware that ha the capablty to perform ANOVA drectly o ummary data. However ome pacage ca perform oe-way ANOVA after geeratg urrogate data from ummary tattc. I th hort ote we have gve STATA program to perform oe-way ANOVA o ummary data; addto th program alo perform Bartllet tet of equalty of varace. The dea ca be exteded to two-way ad hgher way ANOVA. Example have bee gve for llutrato. Key Word: ANOVA, Summary data.. Itroducto I ome cae, where oly ummary data avalable, a reearcher may wat to perform Aaly of Varace (ANOVA) from the avalable formato. Mot tattcal program are deged to perform ANOVA o raw data oly. Davd A. Laro (99) decrbe a method to geerate urrogate data from the ummary tattc that ca be ued to perform oe way ANOVA. Accordg to Laro oe ha to geerate two ew colum amely X ' ad X ' a X ' X + ad ( ) X ' X X ' Ad after ome data mapulato data ready to perform ANOVA uual way. We ue a alteratve method that ca be ued to perform oe way, two way ad hgher way ANOVA by ug ummary meaure, x ad for,,...,, where, x ad are, repectvely, the ze, mea ad tadard devato of - th treatmet. I th ote we have gve a STATA program that ca be ued otly to perform oe way ANOVA ad equalty of varace f oly ummary meaure are avalable. However, th program ca be ealy exteded for twoway ad hgher way ANOVA. Pa.. tat. oper. re. Vol.II No. 006 pp57-6

2 Nadeem Shafque Butt, Shahd Kamal, Muhammad Qaer Shahbaz. Methodology The oe-way, two-way ad hgher-way ANOVA ue certa tattcal model for operato. Specfcally, the oe-way fxed effect ANOVA model gve a:,,..., y µ + τ + ε (.),,..., Th model ca be ued to compare the gfcace of treatmet or to tet the equalty of everal mea. The ull hypothe of teret th model ca be tated ay oe of the followg way: H : τ 0 for,,..., (.) or 0 0 H : µ µ... µ Th hypothe ca be teted by ug the F rato gve a: where ( ) ( ) SST / F SSE / SST x x wth x x.... ; (.3) (.4) SSE (.5) The gfcace of the group mea ca be teted by ug the p value of computed F tattc. STATA program to perform thee aalye gve Appedx - A. We have alo gve the STATA commad to perform Bartlett Tet of equalty of varace o ummary data. The tet tattc to be ued th tet gve a: χ MC (.6) M l ˆ σ ad C + 3 ( ) ˆ σ ˆ σ wth ˆ σ alo The two-way fxed effect ANOVA model gve a: y µ + β + τ + ε,,..., r,,..., c (.7) The ull hypothee of teret two-way ANOVA are: H : β 0 for,,..., r or H : µ µ... µ / / r. H : τ 0 for,,..., c or H : µ µ... µ // // c (.8) 58 Pa.. tat. oper. re. Vol.II No. 006 pp57-6

3 ANOVA wth Summary Stattc: A STATA Macro but geerally we are more cocered wth the hypothe of colum (treatmet). The eceary um of quare to tet the above hypothee two-way ANOVA are gve a: c c r SSTr r x. x.. wth x.. x. x. (.9) c r... (.0) SSB c x x c or SSE r SSB SSE c SST r (.) where x. mea of -th row (bloc), x. mea of -th colum (treatmet), varace of -th row ad varace of -th colum (treatmet). The F rato to tet the gfcace of bloc ad treatmet are gve a: SSB ( r ) SSTr ( c ) F ad F (.) SSE r c SSE r c The dea ca be ealy exteded to three-way ANOVA wth model: y µ + α + β + τ + ε,,,,... p (.3) The ull hypothee three -way ANOVA are gve a: H : α 0 for,,..., p or H : µ µ... µ / / p.. H : β 0 for,,..., p or H : µ µ... µ // // p. H : τ 0 for,,..., p or H : µ µ... µ /// /// ( ) ( p) (.4) but aga we are more cocer wth the lat hypothe of treatmet. The um of quare to tet the gfcace of above hypothee are gve a: p p p p SSB p x.. x... wth x... x.. x.. x.. ; p (.5) p p p p..... (.6) SSC p x x p..... (.7) SSTr p x x p p p (.8) SSE p SSR SSC p SSC SSTr p SSTr SSC the above um of quare x.., x.. ad x.. are, repectvely, the row, colum ad treatmet mea. Alo, ad are the row, colum ad treatmet varace, repectvely. Pa.. tat. oper. re. Vol.II No. 006 pp

4 Nadeem Shafque Butt, Shahd Kamal, Muhammad Qaer Shahbaz The F rato to tet the gfcace of hypothee three-way ANOVA are gve a: SSR p SSC p SSTr p F, F, F3 SSE p p SSE p p SSE p p (.9) 3. Numercal Example I th ecto we have gve two umercal example to demotrate the uefule of the program to perform ANOVA o ummary tattc. Example : Data ha bee tae from Moore ad McCabe (998). The varable of teret lead cocetrato recorded a mllgram per quare meter ad reearch queto to ee gfcat mea dfferece over the year. Here are ome ummary data for fve year: Year The reult of the example by ug the STATA program aova are gve below:. aova mea d SOV df SS MS F P Betwee Group Wth Group Total Bartllet' tet for equalty of varace Ch-Sq df 4 P Example : Data ha bee tae from Haf et al (004). Study to compare three dfferet drug, groupg the ubect to bloc o the ba of age (becaue t ow that age affect ytolc blood preure ytematcally). Summary data gve the followg table. x 60 Pa.. tat. oper. re. Vol.II No. 006 pp57-6

5 ANOVA wth Summary Stattc: A STATA Macro Drug Mea SD Drug A Drug B Drug C Age Group > The reult of the example by ug the STATA program aova are gve below:. aova c cmea rmea cd SOV df SS MS F P Treatmet Bloc Error Total Appedx A: STATA program to perform to perform oe-way ANOVA I th ecto we have gve a STATA program amed aova.ado that ca be ued to perform the ANOVA o ummary data. The program aova.ado requre three colum a put, colum of ample ze, colum of mea ad colum of tadard devato repectvely. Th program ca be ealy modfed for two-way ad hgher-way ANOVA, further th methodology ca be ealy corporated ay pread-heet baed oftware (e.g. MS excel, SPSS, Stattca etc) aova.ado program defe aova /* Requre three col a put ample ze mea ad d */ arg `' `' `3' ge v`'*`' ge v`'*`'^ ge v3 (`'-)*`3'^ ege umum(`') ege umvum(v) ege umvum(v) ege umv3um(v3) ge gmeaumv/um ge bumv-um*gmea^ ge eumv3 ge dfum- Pa.. tat. oper. re. Vol.II No. 006 pp57-6 6

6 Nadeem Shafque Butt, Shahd Kamal, Muhammad Qaer Shahbaz ge df[_n]- ge dfdf-df ge f(b/df)/(e/df) ge p- F(df,df,f) /*calculato for Bartllet' tet*/ ge v/(`'-) ge m`'- ege ummum(m) ege umvum(v) ge c+((umv-(/umm)))/(3*(_n+)) ge qumv3/umm ge t(`'-)*l(q) ge t (`'-)*l(`3'^) ege umtum(t) ege umtum(t) ge b(umt-umt)/c ge pch chtal(_n-,b) /*commad to cotrol output for aova*/ dplay gree "" dplay gree " " dplay gree " SOV df SS MS F P" dplay gree " " dplay yellow" Betwee Group" %6.0f df[] %0.f b[] %0.f b[]/df[] %9.f f[] %.4f p[] dplay yellow" Wth Group" %7.0f df[] %0.f e[] %0.f e[]/df[] dplay gree " " dplay gree " Total" %5.0f df[] %0.f b[]+e[] dplay gree " " dplay yellow" " dplay yellow"bartllet' tet for equalty of varace" dplay gree "Ch-Sq " %4.3f b[] " df " %.0f df[] " P " %5.4f pch[] /*drop all geerated col*/ drop v - p ed aova.ado Referece. Bartlett, M. S., (937), Properte of uffcecy ad tattcal tet, Proceedg of Royal Socety, Sere A 60: Haf, M., Ahmad M. ad Ahmad A. M., (004), Botattc for Health Studet, ISOSS Publcato. 3. Laro, Davd A., (99), Aaly of Varace wth ut Summary Stattc a Iput, Amerca Stattca, 46, Moore, D. S. ad McCabe, G. P., (998), Itroducto to the practce of Stattc, Freema Publher. 6 Pa.. tat. oper. re. Vol.II No. 006 pp57-6

Simple Linear Regression Analysis

Simple Linear Regression Analysis LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such

More information

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN Europea Joural of Mathematc ad Computer Scece Vol. 5 o., 018 ISS 059-9951 APPLICATIO OF ASYMPTOTIC DISTRIBUTIO OF MA-HITEY STATISTIC TO DETERMIE THE DIFFERECE BETEE THE SYSTOLIC BLOOD PRESSURE OF ME AD

More information

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN Europea Joural of Mathematc ad Computer Scece Vol. 5 o., 018 ISS 059-9951 APPLICATIO OF ASYMPTOTIC DISTRIBUTIO OF MA-HITEY STATISTIC TO DETERMIE THE DIFFERECE BETEE THE SYSTOLIC BLOOD PRESSURE OF ME AD

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I lear regreo, we coder the frequecy dtrbuto of oe varable (Y) at each of everal level of a ecod varable (X). Y kow a the depedet varable. The

More information

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4 CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.

More information

KR20 & Coefficient Alpha Their equivalence for binary scored items

KR20 & Coefficient Alpha Their equivalence for binary scored items KR0 & Coeffcet Alpha Ther equvalece for bary cored tem Jue, 007 http://www.pbarrett.et/techpaper/r0.pdf f of 7 Iteral Cotecy Relablty for Dchotomou Item KR 0 & Alpha There apparet cofuo wth ome dvdual

More information

8 The independence problem

8 The independence problem Noparam Stat 46/55 Jame Kwo 8 The depedece problem 8.. Example (Tua qualty) ## Hollader & Wolfe (973), p. 87f. ## Aemet of tua qualty. We compare the Huter L meaure of ## lghte to the average of coumer

More information

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss

More information

Simple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation.

Simple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation. Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6. A Smple Regreo Problem I there relato betwee umber of power boat the area ad umber of maatee klled? Year NPB( )

More information

r y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation

r y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation Maatee Klled Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6.11 A Smple Regreo Problem 1 I there relato betwee umber of power boat the area ad umber of maatee klled?

More information

Linear Approximating to Integer Addition

Linear Approximating to Integer Addition Lear Approxmatg to Iteger Addto L A-Pg Bejg 00085, P.R. Cha apl000@a.com Abtract The teger addto ofte appled cpher a a cryptographc mea. I th paper we wll preet ome reult about the lear approxmatg for

More information

Regression. Chapter 11 Part 4. More than you ever wanted to know about how to interpret the computer printout

Regression. Chapter 11 Part 4. More than you ever wanted to know about how to interpret the computer printout Regreo Chapter Part 4 More tha you ever wated to kow about how to terpret the computer prtout February 7, 009 Let go back to the etrol/brthweght problem. We are ug the varable bwt00 for brthweght o brthweght

More information

1. a. Houston Chronicle, Des Moines Register, Chicago Tribune, Washington Post

1. a. Houston Chronicle, Des Moines Register, Chicago Tribune, Washington Post Homework Soluto. Houto Chrocle, De Moe Regter, Chcago Trbue, Wahgto Pot b. Captal Oe, Campbell Soup, Merrll Lych, Pultzer c. Bll Japer, Kay Reke, Hele Ford, Davd Meedez d..78,.44, 3.5, 3.04 5. No, the

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:

More information

x z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed.

x z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed. ] z-tet for the mea, μ If the P-value i a mall or maller tha a pecified value, the data are tatitically igificat at igificace level. Sigificace tet for the hypothei H 0: = 0 cocerig the ukow mea of a populatio

More information

Linear Regression. Can height information be used to predict weight of an individual? How long should you wait till next eruption?

Linear Regression. Can height information be used to predict weight of an individual? How long should you wait till next eruption? Iter-erupto Tme Weght Correlato & Regreo 1 1 Lear Regreo 0 80 70 80 Heght 1 Ca heght formato be ued to predct weght of a dvdual? How log hould ou wat tll et erupto? Weght: Repoe varable (Outcome, Depedet)

More information

On a Truncated Erlang Queuing System. with Bulk Arrivals, Balking and Reneging

On a Truncated Erlang Queuing System. with Bulk Arrivals, Balking and Reneging Appled Mathematcal Scece Vol. 3 9 o. 3 3-3 O a Trucated Erlag Queug Sytem wth Bul Arrval Balg ad Reegg M. S. El-aoumy ad M. M. Imal Departmet of Stattc Faculty Of ommerce Al- Azhar Uverty. Grl Brach Egypt

More information

Handout #4. Statistical Inference. Probability Theory. Data Generating Process (i.e., Probability distribution) Observed Data (i.e.

Handout #4. Statistical Inference. Probability Theory. Data Generating Process (i.e., Probability distribution) Observed Data (i.e. Hadout #4 Ttle: FAE Coure: Eco 368/01 Sprg/015 Itructor: Dr. I-Mg Chu Th hadout ummarze chapter 3~4 from the referece PE. Relevat readg (detaled oe) ca be foud chapter 6, 13, 14, 19, 3, ad 5 from MPS.

More information

Trignometric Inequations and Fuzzy Information Theory

Trignometric Inequations and Fuzzy Information Theory Iteratoal Joural of Scetfc ad Iovatve Mathematcal Reearch (IJSIMR) Volume, Iue, Jauary - 0, PP 00-07 ISSN 7-07X (Prt) & ISSN 7- (Ole) www.arcjoural.org Trgometrc Iequato ad Fuzzy Iformato Theory P.K. Sharma,

More information

COMPARISONS INVOLVING TWO SAMPLE MEANS. Two-tail tests have these types of hypotheses: H A : 1 2

COMPARISONS INVOLVING TWO SAMPLE MEANS. Two-tail tests have these types of hypotheses: H A : 1 2 Tetig Hypothee COMPARISONS INVOLVING TWO SAMPLE MEANS Two type of hypothee:. H o : Null Hypothei - hypothei of o differece. or 0. H A : Alterate Hypothei hypothei of differece. or 0 Two-tail v. Oe-tail

More information

A note on testing the covariance matrix for large dimension

A note on testing the covariance matrix for large dimension A ote o tetg the covarace matrx for large dmeo Melae Brke Ruhr-Uvertät Bochum Fakultät für Mathematk 44780 Bochum, Germay e-mal: melae.brke@ruhr-u-bochum.de Holger ette Ruhr-Uvertät Bochum Fakultät für

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I liear regreio, we coider the frequecy ditributio of oe variable (Y) at each of everal level of a ecod variable (X). Y i kow a the depedet variable.

More information

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14)

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14) Quz - Lear Regreo Aaly (Baed o Lecture -4). I the mple lear regreo model y = β + βx + ε, wth Tme: Hour Ε ε = Ε ε = ( ) 3, ( ), =,,...,, the ubaed drect leat quare etmator ˆβ ad ˆβ of β ad β repectvely,

More information

Chapter 11 The Analysis of Variance

Chapter 11 The Analysis of Variance Chapter The Aalyss of Varace. Oe Factor Aalyss of Varace. Radomzed Bloc Desgs (ot for ths course) NIPRL . Oe Factor Aalyss of Varace.. Oe Factor Layouts (/4) Suppose that a expermeter s terested populatos

More information

Formulas and Tables from Beginning Statistics

Formulas and Tables from Beginning Statistics Fmula ad Table from Begg Stattc Chater Cla Mdot Relatve Frequecy Chater 3 Samle Mea Poulato Mea Weghted Mea Rage Lower Lmt Uer Lmt Cla Frequecy Samle Se µ ( w) w f Mamum Data Value - Mmum Data Value Poulato

More information

Tools Hypothesis Tests

Tools Hypothesis Tests Tool Hypothei Tet The Tool meu provide acce to a Hypothei Tet procedure that calculate cofidece iterval ad perform hypothei tet for mea, variace, rate ad proportio. It i cotrolled by the dialog box how

More information

Chapter 8. Inferences about More Than Two Population Central Values

Chapter 8. Inferences about More Than Two Population Central Values Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha

More information

Summarizing Bivariate Data. Correlation. Scatter Plot. Pearson s Sample Correlation. Summarizing Bivariate Data SBD - 1

Summarizing Bivariate Data. Correlation. Scatter Plot. Pearson s Sample Correlation. Summarizing Bivariate Data SBD - 1 Summarzg Bvarate Data Summarzg Bvarate Data - Eamg relato betwee two quattatve varable I there relato betwee umber of hadgu regtered the area ad umber of people klled? Ct NGR ) Nkll ) 447 3 4 3 48 4 4

More information

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the

More information

Lecture 8: Linear Regression

Lecture 8: Linear Regression Lecture 8: Lear egresso May 4, GENOME 56, Sprg Goals Develop basc cocepts of lear regresso from a probablstc framework Estmatg parameters ad hypothess testg wth lear models Lear regresso Su I Lee, CSE

More information

MINITAB Stat Lab 3

MINITAB Stat Lab 3 MINITAB Stat 20080 Lab 3. Statitical Inference In the previou lab we explained how to make prediction from a imple linear regreion model and alo examined the relationhip between the repone and predictor

More information

Contents. Introduction to Bayesian methods. Introduction to Bayesian methods Meta Analysis. Models and Methods. Mantel-Haenzel methods for 2x2 tables

Contents. Introduction to Bayesian methods. Introduction to Bayesian methods Meta Analysis. Models and Methods. Mantel-Haenzel methods for 2x2 tables Cotet Meta-aalye: Modeller og metoder Itroducto to Bayea method Meta Aaly. Model ad Method. Matel-Haezel method for x table Sta Lydere KLMED8006 Avedt med tatt St303 med tatt 8 Aprl 00 3 4 Frequett v Bayea

More information

Statistics and Chemical Measurements: Quantifying Uncertainty. Normal or Gaussian Distribution The Bell Curve

Statistics and Chemical Measurements: Quantifying Uncertainty. Normal or Gaussian Distribution The Bell Curve Statitic ad Chemical Meauremet: Quatifyig Ucertaity The bottom lie: Do we trut our reult? Should we (or ayoe ele)? Why? What i Quality Aurace? What i Quality Cotrol? Normal or Gauia Ditributio The Bell

More information

LINEAR REGRESSION ANALYSIS

LINEAR REGRESSION ANALYSIS LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for

More information

On Two-stage Seamless Adaptive Design in Clinical Trials

On Two-stage Seamless Adaptive Design in Clinical Trials ORIGINAL ARICLE O wo-tage Seamle Adaptve Deg Clcal ral She-Chug Chow, * Y-Hua u I recet year, the ue of adaptve deg method clcal reearch ad developmet baed o accrued data ha become very popular becaue

More information

SIMPLE LINEAR REGRESSION

SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION In linear regreion, we conider the frequency ditribution of one variable (Y) at each of everal level of a econd variable (). Y i known a the dependent variable. The variable for

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

S T A T R a c h e l L. W e b b, P o r t l a n d S t a t e U n i v e r s i t y P a g e 1. = Population Variance

S T A T R a c h e l L. W e b b, P o r t l a n d S t a t e U n i v e r s i t y P a g e 1. = Population Variance S T A T 4 - R a c h e l L. W e b b, P o r t l a d S t a t e U i v e r i t y P a g e Commo Symbol = Sample Size x = Sample Mea = Sample Stadard Deviatio = Sample Variace pˆ = Sample Proportio r = Sample

More information

Comparing Means: t-tests for Two Independent Samples

Comparing Means: t-tests for Two Independent Samples Comparing ean: t-tet for Two Independent Sample Independent-eaure Deign t-tet for Two Independent Sample Allow reearcher to evaluate the mean difference between two population uing data from two eparate

More information

Suggested Answers To Exercises. estimates variability in a sampling distribution of random means. About 68% of means fall

Suggested Answers To Exercises. estimates variability in a sampling distribution of random means. About 68% of means fall Beyond Significance Teting ( nd Edition), Rex B. Kline Suggeted Anwer To Exercie Chapter. The tatitic meaure variability among core at the cae level. In a normal ditribution, about 68% of the core fall

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

STA302/1001-Fall 2008 Midterm Test October 21, 2008

STA302/1001-Fall 2008 Midterm Test October 21, 2008 STA3/-Fall 8 Mdterm Test October, 8 Last Name: Frst Name: Studet Number: Erolled (Crcle oe) STA3 STA INSTRUCTIONS Tme allowed: hour 45 mutes Ads allowed: A o-programmable calculator A table of values from

More information

An Unbiased Class of Ratio Type Estimator for Population Mean Using an Attribute and a Variable

An Unbiased Class of Ratio Type Estimator for Population Mean Using an Attribute and a Variable Advace Comutatoal Scece ad Techology ISS 973-67 Volume, umber 7). 39-46 Reearch Ida Publcato htt://www.rublcato.com A Ubaed Cla of Rato Tye Etmator for Poulato Mea Ug a Attrbute ad a Varable Shah Bhuha,

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

Social Studies 201 Notes for March 18, 2005

Social Studies 201 Notes for March 18, 2005 1 Social Studie 201 Note for March 18, 2005 Etimation of a mean, mall ample ize Section 8.4, p. 501. When a reearcher ha only a mall ample ize available, the central limit theorem doe not apply to the

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

TESTS OF SIGNIFICANCE

TESTS OF SIGNIFICANCE TESTS OF SIGNIFICANCE Seema Jaggi I.A.S.R.I., Library Aveue, New Delhi eema@iari.re.i I applied ivetigatio, oe i ofte itereted i comparig ome characteritic (uch a the mea, the variace or a meaure of aociatio

More information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

Predicting the eruption time after observed an eruption of 4 minutes in duration.

Predicting the eruption time after observed an eruption of 4 minutes in duration. Lear Regreo ad Correlato 00 Predctg the erupto tme after oberved a erupto of 4 mute durato. 90 80 70 Iter-erupto Tme.5.0.5 3.0 3.5 4.0 4.5 5.0 5.5 Durato A ample of tererupto tme wa take durg Augut -8,

More information

Wu-Hausman Test: But if X and ε are independent, βˆ. ECON 324 Page 1

Wu-Hausman Test: But if X and ε are independent, βˆ. ECON 324 Page 1 Wu-Hausma Test: Detectg Falure of E( ε X ) Caot drectly test ths assumpto because lack ubased estmator of ε ad the OLS resduals wll be orthogoal to X, by costructo as ca be see from the momet codto X'

More information

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n 0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke

More information

On the energy of complement of regular line graphs

On the energy of complement of regular line graphs MATCH Coucato Matheatcal ad Coputer Chetry MATCH Cou Math Coput Che 60 008) 47-434 ISSN 0340-653 O the eergy of copleet of regular le graph Fateeh Alaghpour a, Baha Ahad b a Uverty of Tehra, Tehra, Ira

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Correlation. Pearson s Sample Correlation. Correlation and Linear Regression. Scatter Plot

Correlation. Pearson s Sample Correlation. Correlation and Linear Regression. Scatter Plot Correlato ad Lear Regreo Dr. Thoma Smotzer Eame Relato Betwee Two Quattatve Varable I there a relato betwee the umber of hadgu regtered ad the umber of people klled b gu? ear #reg #kll 77 447 78 4 79 48

More information

8.6 Order-Recursive LS s[n]

8.6 Order-Recursive LS s[n] 8.6 Order-Recurive LS [] Motivate ti idea wit Curve Fittig Give data: 0,,,..., - [0], [],..., [-] Wat to fit a polyomial to data.., but wic oe i te rigt model?! Cotat! Quadratic! Liear! Cubic, Etc. ry

More information

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing Commet o Dicuio Sheet 18 ad Workheet 18 ( 9.5-9.7) A Itroductio to Hypothei Tetig Dicuio Sheet 18 A Itroductio to Hypothei Tetig We have tudied cofidece iterval for a while ow. Thee are method that allow

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

Multivariate Transformation of Variables and Maximum Likelihood Estimation Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty

More information

COV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic.

COV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic. c Pogsa Porchawseskul, Faculty of Ecoomcs, Chulalogkor Uversty olato of costat varace of s but they are stll depedet. C,, he error term s sad to be heteroscedastc. c Pogsa Porchawseskul, Faculty of Ecoomcs,

More information

Introduction to Matrices and Matrix Approach to Simple Linear Regression

Introduction to Matrices and Matrix Approach to Simple Linear Regression Itroducto to Matrces ad Matrx Approach to Smple Lear Regresso Matrces Defto: A matrx s a rectagular array of umbers or symbolc elemets I may applcatos, the rows of a matrx wll represet dvduals cases (people,

More information

M227 Chapter 9 Section 1 Testing Two Parameters: Means, Variances, Proportions

M227 Chapter 9 Section 1 Testing Two Parameters: Means, Variances, Proportions M7 Chapter 9 Sectio 1 OBJECTIVES Tet two mea with idepedet ample whe populatio variace are kow. Tet two variace with idepedet ample. Tet two mea with idepedet ample whe populatio variace are equal Tet

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Simple Linear Regression and Correlation.

Simple Linear Regression and Correlation. Smple Lear Regresso ad Correlato. Correspods to Chapter 0 Tamhae ad Dulop Sldes prepared b Elzabeth Newto (MIT) wth some sldes b Jacquele Telford (Johs Hopks Uverst) Smple lear regresso aalss estmates

More information

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data

More information

Ratio-Type Estimators in Stratified Random Sampling using Auxiliary Attribute

Ratio-Type Estimators in Stratified Random Sampling using Auxiliary Attribute roceedg of te Iteratoal Multoferece of Egeer ad omuter cett 0 Vol I IME 0 Marc - 0 Hog Kog Rato-ye Etmator tratfed Radom amlg ug Auxlary Attrbute R V K g A Amed Member IAEG Abtract ome rato-tye etmator

More information

Lecture 4 Sep 9, 2015

Lecture 4 Sep 9, 2015 CS 388R: Radomzed Algorthms Fall 205 Prof. Erc Prce Lecture 4 Sep 9, 205 Scrbe: Xagru Huag & Chad Voegele Overvew I prevous lectures, we troduced some basc probablty, the Cheroff boud, the coupo collector

More information

σ σ r = x i x N Statistics Formulas Sample Mean Population Mean Interquartile Range Population Variance Population Standard Deviation

σ σ r = x i x N Statistics Formulas Sample Mean Population Mean Interquartile Range Population Variance Population Standard Deviation Stattc Formula Samle Mea Poulato Mea µ Iterquartle Rae IQR Q 3 Q Samle Varace ( ) Samle Stadard Devato ( ) Poulato Varace Poulato Stadard Devato ( µ ) Coecet o Varato Stadard Devato CV 00% Mea ( µ ) -Score

More information

Bootstrap Method for Testing of Equality of Several Coefficients of Variation

Bootstrap Method for Testing of Equality of Several Coefficients of Variation Cloud Publcatos Iteratoal Joural of Advaced Mathematcs ad Statstcs Volume, pp. -6, Artcle ID Sc- Research Artcle Ope Access Bootstrap Method for Testg of Equalty of Several Coeffcets of Varato Dr. Navee

More information

Sampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING)

Sampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING) Samplg Theory ODULE X LECTURE - 35 TWO STAGE SAPLIG (SUB SAPLIG) DR SHALABH DEPARTET OF ATHEATICS AD STATISTICS IDIA ISTITUTE OF TECHOLOG KAPUR Two stage samplg wth uequal frst stage uts: Cosder two stage

More information

Statistical Properties of the OLS Coefficient Estimators. 1. Introduction

Statistical Properties of the OLS Coefficient Estimators. 1. Introduction ECOOMICS 35* -- OTE 4 ECO 35* -- OTE 4 Stattcal Properte of the OLS Coeffcent Etmator Introducton We derved n ote the OLS (Ordnary Leat Square etmator ˆβ j (j, of the regreon coeffcent βj (j, n the mple

More information

A Result of Convergence about Weighted Sum for Exchangeable Random Variable Sequence in the Errors-in-Variables Model

A Result of Convergence about Weighted Sum for Exchangeable Random Variable Sequence in the Errors-in-Variables Model AMSE JOURNALS-AMSE IIETA publcato-17-sere: Advace A; Vol. 54; N ; pp 3-33 Submtted Mar. 31, 17; Reved Ju. 11, 17, Accepted Ju. 18, 17 A Reult of Covergece about Weghted Sum for Exchageable Radom Varable

More information

Confidence Intervals. Confidence Intervals

Confidence Intervals. Confidence Intervals A overview Mot probability ditributio are idexed by oe me parameter. F example, N(µ,σ 2 ) B(, p). I igificace tet, we have ued poit etimat f parameter. F example, f iid Y 1,Y 2,...,Y N(µ,σ 2 ), Ȳ i a poit

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose

More information

ρ < 1 be five real numbers. The

ρ < 1 be five real numbers. The Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace

More information

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line HUR Techcal Report 000--9 verso.05 / Frak Borg (borgbros@ett.f) A Study of the Reproducblty of Measuremets wth HUR Leg Eteso/Curl Research Le A mportat property of measuremets s that the results should

More information

Some Wgh Inequalities for Univalent Harmonic Analytic Functions

Some Wgh Inequalities for Univalent Harmonic Analytic Functions ppled Mathematc 464-469 do:436/am66 Publhed Ole December (http://wwwscrporg/joural/am Some Wgh Ieualte for Uvalet Harmoc alytc Fucto btract Pooam Sharma Departmet of Mathematc ad troomy Uverty of Lucow

More information

ε. Therefore, the estimate

ε. Therefore, the estimate Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

Continuous Distributions

Continuous Distributions 7//3 Cotuous Dstrbutos Radom Varables of the Cotuous Type Desty Curve Percet Desty fucto, f (x) A smooth curve that ft the dstrbuto 3 4 5 6 7 8 9 Test scores Desty Curve Percet Probablty Desty Fucto, f

More information

Chapter 11 Systematic Sampling

Chapter 11 Systematic Sampling Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of

More information

Chapter 13, Part A Analysis of Variance and Experimental Design

Chapter 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 information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

4. Standard Regression Model and Spatial Dependence Tests

4. Standard Regression Model and Spatial Dependence Tests 4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

Chapter 4 Multiple Random Variables

Chapter 4 Multiple Random Variables Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:

More information

M2S1 - EXERCISES 8: SOLUTIONS

M2S1 - EXERCISES 8: SOLUTIONS MS - EXERCISES 8: SOLUTIONS. As X,..., X P ossoλ, a gve that T ˉX, the usg elemetary propertes of expectatos, we have E ft [T E fx [X λ λ, so that T s a ubase estmator of λ. T X X X Furthermore X X X From

More information

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS SPECIAL CONSIDERAIONS FOR VOLUMERIC Z-ES FOR PROPORIONS Oe s stctve reacto to the questo of whether two percetages are sgfcatly dfferet from each other s to treat them as f they were proportos whch the

More information

Exam-style practice: A Level

Exam-style practice: A Level Exa-tye practce: A Leve a Let X dete the dtrbut ae ad X dete the dtrbut eae The dee the rad varabe Y X X j j The expected vaue Y : E( Y) EX X j j EX EX j j EX E X 7 The varace : Var( Y) VarX VarX j j Var(

More information

TI-83/84 Calculator Instructions for Math Elementary Statistics

TI-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

Handout #1. Title: Foundations of Econometrics. POPULATION vs. SAMPLE

Handout #1. Title: Foundations of Econometrics. POPULATION vs. SAMPLE Hadout #1 Ttle: Foudatos of Ecoometrcs Course: Eco 367 Fall/015 Istructor: Dr. I-Mg Chu POPULATION vs. SAMPLE From the Bureau of Labor web ste (http://www.bls.gov), we ca fd the uemploymet rate for each

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

ENGI 3423 Simple Linear Regression Page 12-01

ENGI 3423 Simple Linear Regression Page 12-01 ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable

More information

AP Statistics Ch 3 Examining Relationships

AP Statistics Ch 3 Examining Relationships Introducton To tud relatonhp between varable, we mut meaure the varable on the ame group of ndvdual. If we thnk a varable ma eplan or even caue change n another varable, then the eplanator varable and

More information

Recall MLR 5 Homskedasticity error u has the same variance given any values of the explanatory variables Var(u x1,...,xk) = 2 or E(UU ) = 2 I

Recall MLR 5 Homskedasticity error u has the same variance given any values of the explanatory variables Var(u x1,...,xk) = 2 or E(UU ) = 2 I Chapter 8 Heterosedastcty Recall MLR 5 Homsedastcty error u has the same varace gve ay values of the eplaatory varables Varu,..., = or EUU = I Suppose other GM assumptos hold but have heterosedastcty.

More information