Stability Analysis of Spike Yield of Winter Wheat
|
|
- Amberlynn Lewis
- 5 years ago
- Views:
Transcription
1 ORIGINAL SCIENTIFIC PAPER Stablty Analyss of Spke Yeld of Wnter Wheat Sorn CIULCA, G. NEDELEA, Emlan MADOŞĂ, S. CHIŞ, Adrana CIOROGA Hortculture Faculty, Banat s Unversty of Agrcultural Scences Tmşoara, Calea Aradulu 119, Tmsoara, Romana,(e-mal: c sorn@yahoo.com) Abstract Plant breeders nvarably encounter genotype x envronment nteracton (GEI) when testng dfferent cultvars across a number of envronments. An deal wheat varety should have a hgh mean yeld combned wth a low degree of fluctuaton under dfferent envronments. The obectve of ths study was to evaluate the spke yeld stablty of 25 wnter wheat cultvars n three locatons from the western part of Romana over three years, through Wrcke s ecovalence, Fnlay-Wlknson lnear regresson analyss models and Mur parttonng of the genotype-envronment nteracton. Fundulea 4, Alex, Turda 2000 and Areşan cultvars presented hgh statc (type I) stablty assocated wth values of spke gran weght superor to the experence mean. Farmec, Expres, Decan and Delabrad cultvars attaned values of gran weght/spke superor to the experence mean assocated wth a hgh genotype- envronment nteracton. Key words: wheat, spke yeld, stablty, G x E nteracton. Analza stablnost prnosa po klasu kod ozme pšence Sažetak Oplemenvač bla se stalno susreću s nterakcom genotp x okolna (GEI) kod testrana razlčth kultvara u većem brou okolna. Idealna sorta pšence b trebala mat vsok prosečn prnos kombnran s malm varranem u razlčtm okolnama. Cl stražvana e bo procent stablnost prnosa klasa kod 25 kultvara ozme pšence na tr lokace u rumunsko kroz tr godne, pomoću Wrckeove ekovalence, Fnlay-Wlknson-ovog modela lnearne regresske analze te Mur-ove podele nterakce genotp x okolna. Kultvar Fundulea 4, Alex, Turda 2000 Areşan pokazal su vsoku statčku (tp I) stablnost povezanu s vrednostma mase zrna klasa superornm skustvenom proseku. Kultvar Farmec, Expres, Decan Delabrad postgl su vrednost mase zrna po klasu znad skustvenog proseka uz vsoku nterakcu genotp x okolna. Klučne reč: pšenca, prnos klasa, stablnost, G x E nterakca. Introducton Hgh yeld stablty usually refers to a genotype ablty to perform consstently, whether at hgh or low yeld levels across a wde range of envronments (Tarakanovas and Ruzgas, 2006). An deal wheat varety should have a hgh mean yeld combned wth a low degree of fluctuaton under dfferent envronments (Annccharco, 2002). There are two contrastng concepts of stablty: statc (type I) and dynamc (type 2), (Becker and Leon 1988; Ln et.al. 1986). Statc stablty s analogous to the bologcal concept of homeostass: a stable genotype tends to mantan a constant yeld across envronments. Dynamc stablty mples for a stable genotype a yeld response n each Proceedngs. 43 rd Croatan and 3 rd Internatonal Symposum on Agrculture. Opata. Croata ( ) XXX) 340
2 Stablty Analyss of Spke Yeld of Wnter Wheat envronment that s always parallel to the mean response of the tested genotypes,.e. zero GE nteracton (Annccharco, 2002). A dynamc approach to the nterpretaton of varetals adaptaton was developed by Fnlay-Wlknson (1963), based to the concept of Yates and Cohran. It led to the dscovery that the components of genotypeenvronment nteractons are lnearly related to envronmental effects measured as the average performance of all test genotypes. Another meanng of stablty especally from the regresson analyss emanates from the magntude of unpredctable porton of G x E nteracton reflected as devaton mean square around the regresson coeffcent of a genotype (Chahal and Gosal, 2002). The obectve of ths study was to evaluate the spke yeld stablty of 25 wnter wheat cultvars n three locatons from the western part Romana over three years, through Wrcke s ecovalence, Fnlay-Wlknson lnear regresson analyss models and Mur parttonng of the genotype-envronment nteracton. Materal and methods The bologcal materal was represented by 25 wnter wheat cultvars, expermented over three locatons from west part Romana: Tmsoara, Lovrn and Pecu-Nou. Experments were organzed n 5 m 2 plots and three repettons usng the complete randomzed blocks desgn, durng Frst, spke yeld stablty of the studed cultvars has been establshed usng the regresson coeffcent followng Fnlay and Wlknson (1963) method. The lnear regresson coeffcent proposed by Fnlay and Wlknson s gven by the followng formula: n n b 2 2 ( ) -represents the genotype, ts yeld by repettons plots or by years, or by locatons; - envronment factors, the value gven by the cultvars yeld average by repettons, or by years or by locatons; n the number of repettons, or years, or locatons. Also, the Wrcke s ecovalence was used to estmate the stablty of spke yeld for dfferent cultvars n the three locatons. The ecologcal valence or ecovalence s the contrbuton of each genotype to the nteracton sum of square and s expressed: W Y Y Y Y 2 where: W- ecovalence of the genotype; Y - the mean performance o the genotype n the envronment; Y- the mean performance of genotype over envronments; Y- the mean of the envronment. The varety wth the hgher ecovalence (lower W) s consdered as most stable, whereas hgher resduals (low ecovalence) ndcates poor stablty. The genotype-by-envronment nteracton, for the studed cultvars was parttoned nto two types of nteracton: due to heterogeneous varances n scalng of genetcs effects, and due to mperfect correlatons, devatons from a perfect postve correlaton respectvely, accordng to the frst method of Mur et. al. (1992). Results and dscusson Durng the expermental perod hgher type I stablty was observed for Alex, Flamura 85, Areşan and Turda 2000 cultvars, whose productvty/spke attaned close values n the ecologcal condtons of the three expermental locatons. The cultvars Farmec, Expres and Decan attaned dfferent values dependng on locaton for the same trat, showng a low statc stablty. Regresson coeffcent values close to the unt that certfy a hgh dynamc stablty were regstered for the Bezostaa, Dela, Gret and Falnc cultvars, where spke yeld was proportonal wth the envronmental condtons for all three expermental locatons, hgher n favorable envronments and lower n unfavorable envronments, respectvely. n the wnter wheat cultvars studed n three locatons durng 2004/2007 Genetcs, Plant Breedng and Seed Producton 341
3 Hgh values for the genotype-envronment nteracton were observed for Farmec, Turda 95, Expres and Decan cultvars that ndcate a low dynamc stablty, attanng dfferent values for gran weght/spke, uncorrelated wth the ecologcal condtons from the testng locatons. 3 2,5 Expres Decan Farmec 2 GKGobe Dor GKOthalom Dropa Crna 1,5 Grua Ardeal Delabrad Regr.coeff Gret Falnc 1 Bezostaa Dela 0,5 Boema Lv34 Flamura85 Mean Turda Arean Alex Romulus F4 1,5 2-0,5 Glora 2,5 3-1 Turda95 Gran weght/spke (g) Fg.1. Dagram of mean values and regresson coeffcents for grans weght/spke Mnmal values of devaton from the regresson lne and hgh type III stablty were observed for GkOthalom, Ardeal, Falnc, Flamura 85 cultvars. Alex, Dor and GkGobe cultvars have regstered low type III stablty, spke gran yeld values ndcatng also hgh devatons from the regresson lne for all three locatons. Accordng to the Fgure 1, cultvars Fundulea, Alex, Turda 2000 and Areşan showed large statc stablty assocated wth values of gran weght/spke superor to the experence mean. Cultvars Farmec, Expres, Decan and Delabrad have regstered gran yeld/spke values superor to the experence mean beng strongly nfluenced by genotype- envronment nteracton. Accordng to the Table 2 data, the hghest spke gran weght stablty assocated wth low and sgnfcant ecovalence values for these expermental years were regstered for the cultvars: Ardeal, Falnc and Bezostaa. Decan, Alex and Dor cultvars regstered large nstablty for the trat yeld/spke whch s gven by the hgh values of the ecologcal valence. The genotype-envronment nteracton analyss (Table 3) ndcates that the hghest stablty and low genotype-envronment nteracton, respectvely (below 2.2 % from the total value) was regstered for Bezostaa, Falnc and Ardeal cultvars. Hgh genotype- envronment nteracton assocated wth hgh nstablty was observed for Decan, Alex and Dor cultvars. These cultvars acheved mostly superor values for ths trat. Wth regard to gran weght % of the genotype-envronment nteracton s due to the mperfect correlatons, therefore dfferent genotype stablty assessment based on mperfect correlatons mght be effcently used. As for mperfect correlatons, t has been observed that most stable values of the yeld/spke were regstered for the cultvars: Flamura 85, Bezostaa, Lovrn 34 and Falnc, that presented conspcuous close ranks comparng wth gran weght/spke obtaned n the three expermental locatons. Hgh values of devaton between ranks for ths trat ndcatng low stablty, were regstered for all expermental locatons n case of Decan, Fundulea 4 and Dor cultvars. 342
4 Stablty Analyss of Spke Yeld of Wnter Wheat Table 1. Grans weght/spke stablty through (Fnlay-Wlknson) lnear regresson for wnter wheat cultvars studed at three locatons durng No Cultvar Mean Regr. Tp I Tp II Regr. Rezdual Tp III (g) coeffcent Stablty (range) Stablty (range) constant varance Stablty (range) 1 FLAMURA FUNDULEA ARIESAN DROPIA ARDEAL BOEMA CRINA DELABRAD DOR DECAN EXPRES FARMEC FALNIC GLORIA GRUIA GRETI TURDA TURDA DELIA ALEX ROMULUS LOVRIN GK GOBE GK OTHALOM BEZOSTAIA Table 2. Grans weght/spke stablty through ecovalence for wnter wheat cultvars studed n three locatons durng 2004/2007 No. Cultvar Mean (g) Ecovalence Ecovalence var. F value Stablty range 1 FLAMURA FUNDULEA ARIESAN DROPIA ARDEAL ** 2 6 BOEMA CRINA DELABRAD ** DOR DECAN ** EXPRES ** FARMEC ** FALNIC ** 2 14 GLORIA GRUIA GRETI ** 4 17 TURDA TURDA DELIA ALEX ROMULUS LOVRIN GK GOBE GK OTHALOM ** 7 25 BEZOSTAIA ** 2 Genetcs, Plant Breedng and Seed Producton 343
5 Table 3. Grans weght/spke stablty through (Mur) heterogeneous varances (HV) and mperfect correlatons (IC) for wnter wheat cultvars studed n three locatons durng 2004/2007 No. Cultvar Mean SS SS SS (g) (HV) (%) (IC) (%) (GE) (%) 1 FLAMURA FUNDULEA ARIESAN DROPIA ARDEAL BOEMA CRINA DELABRAD DOR DECAN EXPRES FARMEC FALNIC GLORIA GRUIA GRETI TURDA TURDA DELIA ALEX ROMULUS LOVRIN GK GOBE GK OTHALOM BEZOSTAIA Sum Conclusons Farmec, Expres, Decan and Delabrad cultvars attaned values of gran weght/spke superor to the experence mean assocated wth a hgh genotype- envronment nteracton and well-suted for cultvaton n favorable condtons. Regardng the hgh stablty, Flamura 85, Romulus and Boema cultvars are recommended for cultvaton n less favorable envronments wthn the consdered regon. Fundulea 4, Alex, Turda 2000 and Areşan cultvars have demonstrated hgh statc (type I) stablty assocated wth values of spke gran weght superor to the experence mean. Moreover, these cultvars are best-suted for cultvaton n dfferent locatons from west part Romana. References Annccharco P. (2000): Genotype x envronment nteractons. FAO Plant Producton. and Protecton; Becker. H. B. and Leon. J. (1988): Stablty analyss n plant breedng. Plant Breed. 101:1-23; Chahal G.S.. Gosal S.S. (2002): Prncples and procedures of plant breedng. Alpha Scence. New Delh. Inda; Fnlay. K. W.. Wlknson. G. N. (1963): The analyss of adaptaton n a plant-breedng programme. Aust.J.Agrc.Res. 14: ; Ln C.C. et. al.(1986): Stablty Analyss: Where do you Stand. Crop. Sc. 26: ; Mur. W.. Nyqust. W. E.. Xu. S. (1992): Alternatve parttonng of the genotype- by - envronment nteracton. Theor.Appl.Genet. 84: ; Tarakanovas P.. Ruzgas V. (2006): Addtve man effect and multplcatve nteracton analyss of gran yeld of wheat varetes n Lthuana. Agronomy research. 4 (1) ; Wrcke G. (1962): Über ene Methode zur Erfassung der ökologschen Streubrete n Feldversuchen. Z. Pflanzenzüchtg.. 47: sa2008_
Basic concepts and definitions in multienvironment
Basc concepts and defntons n multenvronment data: G, E, and GxE Marcos Malosett, Danela Bustos-Korts, Fred van Eeuwk, Pter Bma, Han Mulder Contents The basc concepts: ntroducton and defntons Phenotype,
More informationChapter 13: Multiple Regression
Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to
More informationDr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur
Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed
More informationDepartment of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution
Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable
More informationDurban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications
Durban Watson for Testng the Lack-of-Ft of Polynomal Regresson Models wthout Replcatons Ruba A. Alyaf, Maha A. Omar, Abdullah A. Al-Shha ralyaf@ksu.edu.sa, maomar@ksu.edu.sa, aalshha@ksu.edu.sa Department
More informationDr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur
Analyss of Varance and Desgn of Experment-I MODULE VIII LECTURE - 34 ANALYSIS OF VARIANCE IN RANDOM-EFFECTS MODEL AND MIXED-EFFECTS EFFECTS MODEL Dr Shalabh Department of Mathematcs and Statstcs Indan
More informationStatistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models
Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 14 Multple Regresson Models 1999 Prentce-Hall, Inc. Chap. 14-1 Chapter Topcs The Multple Regresson Model Contrbuton of Indvdual Independent Varables
More informationScatter Plot x
Construct a scatter plot usng excel for the gven data. Determne whether there s a postve lnear correlaton, negatve lnear correlaton, or no lnear correlaton. Complete the table and fnd the correlaton coeffcent
More informationUNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours
UNIVERSITY OF TORONTO Faculty of Arts and Scence December 005 Examnatons STA47HF/STA005HF Duraton - hours AIDS ALLOWED: (to be suppled by the student) Non-programmable calculator One handwrtten 8.5'' x
More informationNumber of cases Number of factors Number of covariates Number of levels of factor i. Value of the dependent variable for case k
ANOVA Model and Matrx Computatons Notaton The followng notaton s used throughout ths chapter unless otherwse stated: N F CN Y Z j w W Number of cases Number of factors Number of covarates Number of levels
More informationLinear Correlation. Many research issues are pursued with nonexperimental studies that seek to establish relationships among 2 or more variables
Lnear Correlaton Many research ssues are pursued wth nonexpermental studes that seek to establsh relatonshps among or more varables E.g., correlates of ntellgence; relaton between SAT and GPA; relaton
More informationNegative Binomial Regression
STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...
More informationis the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors
Multple Lnear and Polynomal Regresson wth Statstcal Analyss Gven a set of data of measured (or observed) values of a dependent varable: y versus n ndependent varables x 1, x, x n, multple lnear regresson
More information/ n ) are compared. The logic is: if the two
STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence
More informationChapter 11: Simple Linear Regression and Correlation
Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests
More informationTurbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH
Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant
More informationPop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing
Advanced Scence and Technology Letters, pp.164-168 http://dx.do.org/10.14257/astl.2013 Pop-Clc Nose Detecton Usng Inter-Frame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,
More informationStatistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation
Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear
More informationRecall that quantitative genetics is based on the extension of Mendelian principles to polygenic traits.
BIOSTT/STT551, Statstcal enetcs II: Quanttatve Trats Wnter 004 Sources of varaton for multlocus trats and Handout Readng: Chapter 5 and 6. Extensons to Multlocus trats Recall that quanttatve genetcs s
More informationA Robust Method for Calculating the Correlation Coefficient
A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal
More informationHere is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y)
Secton 1.5 Correlaton In the prevous sectons, we looked at regresson and the value r was a measurement of how much of the varaton n y can be attrbuted to the lnear relatonshp between y and x. In ths secton,
More informationChapter 12 Analysis of Covariance
Chapter Analyss of Covarance Any scentfc experment s performed to know somethng that s unknown about a group of treatments and to test certan hypothess about the correspondng treatment effect When varablty
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased
More informationNow we relax this assumption and allow that the error variance depends on the independent variables, i.e., heteroskedasticity
ECON 48 / WH Hong Heteroskedastcty. Consequences of Heteroskedastcty for OLS Assumpton MLR. 5: Homoskedastcty var ( u x ) = σ Now we relax ths assumpton and allow that the error varance depends on the
More informationStatistical Evaluation of WATFLOOD
tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth
More informationAssessment of Parametric and Non-parametric Methods for Selecting Stable and Adapted Durum Wheat Genotypes in Multi-Environments
Avalable onlne at www.notulaebotancae.ro Prnt ISSN 055-965X; Electronc 184-4309 Not. Bot. Hort. Agrobot. Cluj 38 010 71-79 Notulae Botancae Hort Agrobotanc Cluj-Napoca Assessment of Parametrc and Non-parametrc
More informationJanuary Examinations 2015
24/5 Canddates Only January Examnatons 25 DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR STUDENT CANDIDATE NO.. Department Module Code Module Ttle Exam Duraton (n words)
More informationOn the correction of the h-index for career length
1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat
More informationTHE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD
Journal of Appled Mathematcs and Computatonal Mechancs 7, 6(3), 7- www.amcm.pcz.pl p-issn 99-9965 DOI:.75/jamcm.7.3. e-issn 353-588 THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS
More informationTopic 10: ANOVA models for random and mixed effects Fixed and Random Models in One-way Classification Experiments
Topc 10: ANOVA models for random and mxed effects eferences: ST&D Topc 7.5 (15-153), Topc 9.9 (5-7), Topc 15.5 (379-384); rules for expected on ST&D page 381 replaced by Chapter 8 from Montgomery, 1991.
More informationANALYSIS OF COVARIANCE
ANALYSIS OF COVARIANCE YOGITA GHARDE M.Sc. (Agrcultural Statstcs), Roll No. 4495 I.A.S.R.I., Lbrary Avenue, New Delh- 11 1 Charperson: Dr. V.K. Sharma Abstract: Analyss of covarance (ANCOVA) s a statstcal
More informationPanel Model for Wheat Prices
ISSN 1684 8403 Journal of Statstcs Vol: 1, No.1 (005) 59 Panel Model for Wheat Prces Tanveer Akhlaq* Muhammad Qaser Shahbaz** Abstract A forecast models for wheat prces for the country s developed by takng
More information2016 Wiley. Study Session 2: Ethical and Professional Standards Application
6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton
More informationEcon107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)
I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes
More informationSimulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests
Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth
More informationSIMPLE LINEAR REGRESSION
Smple Lnear Regresson and Correlaton Introducton Prevousl, our attenton has been focused on one varable whch we desgnated b x. Frequentl, t s desrable to learn somethng about the relatonshp between two
More informationUncertainty and auto-correlation in. Measurement
Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at
More informationCorrelation and Regression. Correlation 9.1. Correlation. Chapter 9
Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,
More informationBOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu
BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS M. Krshna Reddy, B. Naveen Kumar and Y. Ramu Department of Statstcs, Osmana Unversty, Hyderabad -500 007, Inda. nanbyrozu@gmal.com, ramu0@gmal.com
More informationANOVA. The Observations y ij
ANOVA Stands for ANalyss Of VArance But t s a test of dfferences n means The dea: The Observatons y j Treatment group = 1 = 2 = k y 11 y 21 y k,1 y 12 y 22 y k,2 y 1, n1 y 2, n2 y k, nk means: m 1 m 2
More informationEconomics 130. Lecture 4 Simple Linear Regression Continued
Economcs 130 Lecture 4 Contnued Readngs for Week 4 Text, Chapter and 3. We contnue wth addressng our second ssue + add n how we evaluate these relatonshps: Where do we get data to do ths analyss? How do
More information4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA
4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected
More informationGlobal Sensitivity. Tuesday 20 th February, 2018
Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values
More informationJAB Chain. Long-tail claims development. ASTIN - September 2005 B.Verdier A. Klinger
JAB Chan Long-tal clams development ASTIN - September 2005 B.Verder A. Klnger Outlne Chan Ladder : comments A frst soluton: Munch Chan Ladder JAB Chan Chan Ladder: Comments Black lne: average pad to ncurred
More informationColor Rendering Uncertainty
Australan Journal of Basc and Appled Scences 4(10): 4601-4608 010 ISSN 1991-8178 Color Renderng Uncertanty 1 A.el Bally M.M. El-Ganany 3 A. Al-amel 1 Physcs Department Photometry department- NIS Abstract:
More informationTopic 23 - Randomized Complete Block Designs (RCBD)
Topc 3 ANOVA (III) 3-1 Topc 3 - Randomzed Complete Block Desgns (RCBD) Defn: A Randomzed Complete Block Desgn s a varant of the completely randomzed desgn (CRD) that we recently learned. In ths desgn,
More informationStatistics II Final Exam 26/6/18
Statstcs II Fnal Exam 26/6/18 Academc Year 2017/18 Solutons Exam duraton: 2 h 30 mn 1. (3 ponts) A town hall s conductng a study to determne the amount of leftover food produced by the restaurants n the
More informationCOMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD
COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD Ákos Jósef Lengyel, István Ecsed Assstant Lecturer, Professor of Mechancs, Insttute of Appled Mechancs, Unversty of Mskolc, Mskolc-Egyetemváros,
More informationj) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1
Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons
More informationIntroduction to Analysis of Variance (ANOVA) Part 1
Introducton to Analss of Varance (ANOVA) Part 1 Sngle factor The logc of Analss of Varance Is the varance explaned b the model >> than the resdual varance In regresson models Varance explaned b regresson
More informationOrientation Model of Elite Education and Mass Education
Proceedngs of the 8th Internatonal Conference on Innovaton & Management 723 Orentaton Model of Elte Educaton and Mass Educaton Ye Peng Huanggang Normal Unversty, Huanggang, P.R.Chna, 438 (E-mal: yepeng@hgnc.edu.cn)
More informationSTATISTICS QUESTIONS. Step by Step Solutions.
STATISTICS QUESTIONS Step by Step Solutons www.mathcracker.com 9//016 Problem 1: A researcher s nterested n the effects of famly sze on delnquency for a group of offenders and examnes famles wth one to
More informationLecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management
Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 1 Chapters 14, 15 & 16 Professor Ahmad, Ph.D. Department of Management Revsed August 005 Chapter 14 Formulas Smple Lnear Regresson Model: y =
More information[The following data appear in Wooldridge Q2.3.] The table below contains the ACT score and college GPA for eight college students.
PPOL 59-3 Problem Set Exercses n Smple Regresson Due n class /8/7 In ths problem set, you are asked to compute varous statstcs by hand to gve you a better sense of the mechancs of the Pearson correlaton
More informationReduced slides. Introduction to Analysis of Variance (ANOVA) Part 1. Single factor
Reduced sldes Introducton to Analss of Varance (ANOVA) Part 1 Sngle factor 1 The logc of Analss of Varance Is the varance explaned b the model >> than the resdual varance In regresson models Varance explaned
More informationChapter 3 Describing Data Using Numerical Measures
Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The
More informationChapter 7 Generalized and Weighted Least Squares Estimation. In this method, the deviation between the observed and expected values of
Chapter 7 Generalzed and Weghted Least Squares Estmaton The usual lnear regresson model assumes that all the random error components are dentcally and ndependently dstrbuted wth constant varance. When
More informationDummy variables in multiple variable regression model
WESS Econometrcs (Handout ) Dummy varables n multple varable regresson model. Addtve dummy varables In the prevous handout we consdered the followng regresson model: y x 2x2 k xk,, 2,, n and we nterpreted
More informationComparison of Regression Lines
STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence
More informationChapter 9: Statistical Inference and the Relationship between Two Variables
Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More informationT E C O L O T E R E S E A R C H, I N C.
T E C O L O T E R E S E A R C H, I N C. B rdg n g En g neern g a nd Econo mcs S nce 1973 THE MINIMUM-UNBIASED-PERCENTAGE ERROR (MUPE) METHOD IN CER DEVELOPMENT Thrd Jont Annual ISPA/SCEA Internatonal Conference
More informationECE559VV Project Report
ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate
More information3) Surrogate Responses
1) Introducton Vsual neurophysology has benefted greatly for many years through the use of smple, controlled stmul lke bars and gratngs. One common characterzaton of the responses elcted by these stmul
More informationStatistics for Economics & Business
Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable
More informationLecture 6: Introduction to Linear Regression
Lecture 6: Introducton to Lnear Regresson An Manchakul amancha@jhsph.edu 24 Aprl 27 Lnear regresson: man dea Lnear regresson can be used to study an outcome as a lnear functon of a predctor Example: 6
More informationEvaluation of Validation Metrics. O. Polach Final Meeting Frankfurt am Main, September 27, 2013
Evaluaton of Valdaton Metrcs O. Polach Fnal Meetng Frankfurt am Man, September 7, 013 Contents What s Valdaton Metrcs? Valdaton Metrcs evaluated n DynoTRAIN WP5 Drawbacks of Valdaton Metrcs Conclusons
More informationUncertainty in measurements of power and energy on power networks
Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:
More informationHeterosis and Combining Ability Analysis Oil Content Seed Yield and its Component in Linseed
Internatonal Journal of Current Mcrobology and Appled Scences ISSN: 319-7706 Volume 6 Number 11 (017) pp. 1504-1516 Journal homepage: http://www.jcmas.com Orgnal Research Artcle https://do.org/10.0546/jcmas.017.611.178
More informationOperating conditions of a mine fan under conditions of variable resistance
Paper No. 11 ISMS 216 Operatng condtons of a mne fan under condtons of varable resstance Zhang Ynghua a, Chen L a, b, Huang Zhan a, *, Gao Yukun a a State Key Laboratory of Hgh-Effcent Mnng and Safety
More informationPHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University
PHYS 45 Sprng semester 7 Lecture : Dealng wth Expermental Uncertantes Ron Refenberger Brck anotechnology Center Purdue Unversty Lecture Introductory Comments Expermental errors (really expermental uncertantes)
More informationANALYSIS OF GENOTYPE X ENVIRONMENT INTERACTION BY GRAPHICAL TECHNIQUES
nsas State Unversty Lbrares tstcs n Agrculture 1991-3rd Annual Conference Proceedngs ANALYSS OF GENOTYPE X ENVRONMENT NTERACTON BY GRAPHCAL TECHNQUES George C.J. Fernandez Follow ths and addtonal works
More informationChapter 8 Indicator Variables
Chapter 8 Indcator Varables In general, e explanatory varables n any regresson analyss are assumed to be quanttatve n nature. For example, e varables lke temperature, dstance, age etc. are quanttatve n
More informationAGC Introduction
. Introducton AGC 3 The prmary controller response to a load/generaton mbalance results n generaton adjustment so as to mantan load/generaton balance. However, due to droop, t also results n a non-zero
More informationRegularized Discriminant Analysis for Face Recognition
1 Regularzed Dscrmnant Analyss for Face Recognton Itz Pma, Mayer Aladem Department of Electrcal and Computer Engneerng, Ben-Guron Unversty of the Negev P.O.Box 653, Beer-Sheva, 845, Israel. Abstract Ths
More informationwhere I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X).
11.4.1 Estmaton of Multple Regresson Coeffcents In multple lnear regresson, we essentally solve n equatons for the p unnown parameters. hus n must e equal to or greater than p and n practce n should e
More informationChapter 3. Two-Variable Regression Model: The Problem of Estimation
Chapter 3. Two-Varable Regresson Model: The Problem of Estmaton Ordnary Least Squares Method (OLS) Recall that, PRF: Y = β 1 + β X + u Thus, snce PRF s not drectly observable, t s estmated by SRF; that
More informationMD. LUTFOR RAHMAN 1 AND KALIPADA SEN 2 Abstract
ISSN 058-71 Bangladesh J. Agrl. Res. 34(3) : 395-401, September 009 PROBLEMS OF USUAL EIGHTED ANALYSIS OF VARIANCE (ANOVA) IN RANDOMIZED BLOCK DESIGN (RBD) ITH MORE THAN ONE OBSERVATIONS PER CELL HEN ERROR
More informationAn Improved multiple fractal algorithm
Advanced Scence and Technology Letters Vol.31 (MulGraB 213), pp.184-188 http://dx.do.org/1.1427/astl.213.31.41 An Improved multple fractal algorthm Yun Ln, Xaochu Xu, Jnfeng Pang College of Informaton
More informationA Hybrid Variational Iteration Method for Blasius Equation
Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method
More informationmodeling of equilibrium and dynamic multi-component adsorption in a two-layered fixed bed for purification of hydrogen from methane reforming products
modelng of equlbrum and dynamc mult-component adsorpton n a two-layered fxed bed for purfcaton of hydrogen from methane reformng products Mohammad A. Ebrahm, Mahmood R. G. Arsalan, Shohreh Fatem * Laboratory
More informationNON LINEAR ANALYSIS OF STRUCTURES ACCORDING TO NEW EUROPEAN DESIGN CODE
October 1-17, 008, Bejng, Chna NON LINEAR ANALYSIS OF SRUCURES ACCORDING O NEW EUROPEAN DESIGN CODE D. Mestrovc 1, D. Czmar and M. Pende 3 1 Professor, Dept. of Structural Engneerng, Faculty of Cvl Engneerng,
More informationChapter 6. Supplemental Text Material
Chapter 6. Supplemental Text Materal S6-. actor Effect Estmates are Least Squares Estmates We have gven heurstc or ntutve explanatons of how the estmates of the factor effects are obtaned n the textboo.
More informationEURAMET.M.D-S2 Final Report Final report
Fnal report on ERAMET blateral comparson on volume of mass standards Project number: 1356 (ERAMET.M.D-S2) Volume of mass standards of 10g, 20 g, 200 g, 1 kg Zoltan Zelenka 1 ; Stuart Davdson 2 ; Cslla
More informationDr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur
Analyss of Varance and Desgn of Experments- MODULE LECTURE - 6 EXPERMENTAL DESGN MODELS Dr. Shalabh Department of Mathematcs and Statstcs ndan nsttute of Technology Kanpur Two-way classfcaton wth nteractons
More informationUncertainty as the Overlap of Alternate Conditional Distributions
Uncertanty as the Overlap of Alternate Condtonal Dstrbutons Olena Babak and Clayton V. Deutsch Centre for Computatonal Geostatstcs Department of Cvl & Envronmental Engneerng Unversty of Alberta An mportant
More informationThe Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction
ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also
More informationExponential Type Product Estimator for Finite Population Mean with Information on Auxiliary Attribute
Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 193-9466 Vol. 10, Issue 1 (June 015), pp. 106-113 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) Exponental Tpe Product Estmator
More informationDeveloping a Data Validation Tool Based on Mendelian Sampling Deviations
Developng a Data Valdaton Tool Based on Mendelan Samplng Devatons Flppo Bscarn, Stefano Bffan and Fabola Canaves A.N.A.F.I. Italan Holsten Breeders Assocaton Va Bergamo, 292 Cremona, ITALY Abstract A more
More informationDr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur
Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models
More informationANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)
Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of
More informationAssessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion
Assessment of Ste Amplfcaton Effect from Input Energy Spectra of Strong Ground Moton M.S. Gong & L.L Xe Key Laboratory of Earthquake Engneerng and Engneerng Vbraton,Insttute of Engneerng Mechancs, CEA,
More informationEVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES
EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES Manuel J. C. Mnhoto Polytechnc Insttute of Bragança, Bragança, Portugal E-mal: mnhoto@pb.pt Paulo A. A. Perera and Jorge
More informationChapter 11: I = 2 samples independent samples paired samples Chapter 12: I 3 samples of equal size J one-way layout two-way layout
Serk Sagtov, Chalmers and GU, February 0, 018 Chapter 1. Analyss of varance Chapter 11: I = samples ndependent samples pared samples Chapter 1: I 3 samples of equal sze one-way layout two-way layout 1
More informationIntroduction to Dummy Variable Regressors. 1. An Example of Dummy Variable Regressors
ECONOMICS 5* -- Introducton to Dummy Varable Regressors ECON 5* -- Introducton to NOTE Introducton to Dummy Varable Regressors. An Example of Dummy Varable Regressors A model of North Amercan car prces
More informatione i is a random error
Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where + β + β e for,..., and are observable varables e s a random error How can an estmaton rule be constructed for the unknown
More informationx i1 =1 for all i (the constant ).
Chapter 5 The Multple Regresson Model Consder an economc model where the dependent varable s a functon of K explanatory varables. The economc model has the form: y = f ( x,x,..., ) xk Approxmate ths by
More informationLECTURE 9 CANONICAL CORRELATION ANALYSIS
LECURE 9 CANONICAL CORRELAION ANALYSIS Introducton he concept of canoncal correlaton arses when we want to quantfy the assocatons between two sets of varables. For example, suppose that the frst set of
More informationAn identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites
IOP Conference Seres: Materals Scence and Engneerng PAPER OPE ACCESS An dentfcaton algorthm of model knetc parameters of the nterfacal layer growth n fber compostes o cte ths artcle: V Zubov et al 216
More informationUSE OF ADDITIVE MAIN EFFECTS AND MULTIPLICATIVE INTERACTION (AMMI) MODEL IN CROP IMPROVEMENT
USE OF ADDITIVE MAIN EFFECTS AND MULTIPLICATIVE INTERACTION (AMMI) MODEL IN CROP IMPROVEMENT A.R.RAO Indan Agrcultural Statstcs Research Insttute Lbrary Avenue, New Delh - 0 0 arrao@asr.res.n. Introducton
More information