Basic concepts and definitions in multienvironment
|
|
- Theodora Allen
- 5 years ago
- Views:
Transcription
1 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, genotype, and envronment Reacton norms Termnology: plastcty, envronmental senstvty, adaptablty Concepts of stablty n plant breedng 2 1
2 Learnng outcomes To understand and apply the basc concepts of GxE and the dfferent termnology n dfferent dscplnes To understand and apply the dfferent concepts of stablty n plant breedng 3 Some basc defntons... Phenotype... Genotype... Envronment... and GxE! 4 2
3 What s the phenotype? P t = t f G, E dt 0 5 The phenotype, the outcome of... Genotype : P t = t f G, E dt 0 DNA consttuton of the organsm o Alleles present and ther ntra and nter-loc combnatons o... but much more than that! Envronment : External stmul provded by the surroundng where the organsm develops / lves: o Temperature / lght / humdty / nutrents / management / etc Development : Tme span when the lfe cycle of the organsm occurs o Condtons both the genotype and the envronment 6 3
4 Phenotype a hgh-dmensonal problem... Genotypes Envronments Genotypes Envronments G-E landscape: complex outcome of multple genotypc and envronmental factors nteractng wth each other... Genotypc dmenson: populaton or sample of a populaton. Envronmental dmenson: Target Populaton of Envronments (TPE), set of condtons that the genotypes or the populaton under study are lkely to experence. 7 Indvdual genotype response: A slce from ths landscape... Genotypes Envronments The red lne s the path that represents the phenotype of a partcular genotype across the envronmental gradent... That path related wth the reacton norm of that partcular genotype. 8 4
5 Reacton norms Reacton norm: Genotype-specfc functonal relatonshp between phenotypc response and envronmental gradent(s). Whch factor(s) drve the envronmental gradent? Here gradent expressed n terms of envronmental means. 9 GxE and reacton norms Genotypes can react dfferently to the same envronmental gradent. Reacton norms change from genotype to genotype... Wth dfferent reacton norms G x E! Whch factor(s) drve the envronmental gradent? Important component n GxE research... Why genotypes respond dfferently? And to what? 10 5
6 GxE occurs when reacton norms are not parallel! Non-parallel reacton norms reveals GxE GxE: dfferental reacton of genotypes to envronmental changes Varaton n adaptaton / plastcty / envronmental senstvty / stablty / Examples of pars of reacton norms Addtvty No GxE Convergence Dvergence Cross-over nteracton 12 6
7 G x E consdered as nteracton Dfferent genotypes respond dfferently to a change n the envronment y GE 22 G 2 E 2 Int. E1 E2 Model: y = nt + G + E + GE y 11 = nt y 12 = nt + E 2 y 21 = nt + G 2 y 22 = nt + G 2 + E 2 + GE 22 GE = 0 no GxE-nteracton 13 G x E and (phenotypc) Plastcty A genotype changes ts phenotype when the envronment changes Plastcty does not necessarly mply GxE-nteracton! y Plastc y Non-plastc E1 E2 E1 E2 14 7
8 G x E and envronmental senstvty Refers to the slope of the reacton norm Measures degree of plastcty y Senstve Non-senstve E1 E2 15 General versus specfc adaptaton Adapted genotype a genotype whose reacton norm s above certan standard or reference genotype. General or wde adaptaton: superor across the entre TPE Specfc or narrow adaptaton: superor but only over a range of the TPE 16 8
9 G x E, adaptaton, plastcty and envronmental senstvty - Non-parallel reacton norms - Genetc varaton n adaptaton - Genetc varaton n plastcty - Genetc varaton n envronmental senstvty G x E P Wth many genotypes, rankng dffers between envronments. Correlaton of the ranks between envronments 1 E1 E2 Varance among genotypes may dffer between envronments 17 GxE and heterogenety of varaton addtvty GxE Addtvty: constant varance across envronments. If GxE: varablty changes from envronment to envronment typcally low varance n poor envronments, hgh n good envronments. No effect on rankng of genotypes, therefore no consequences for selecton 18 9
10 G x E and rerankng Treat the trat n each envronment as a genetcally dstnct trat Genetc correlaton between trat n dfferent envronments s a measure of G x E (Falconer, 1952) r g < 1 G x E Model: y k = + G + k r g = corr(g,g ) Trat 2 Trat 2 y Trat 1 y Trat 1 E1 E2 No GxE: r g = 1 E1 GxE: r g < 1 E2 19 Typcal research questons regardng GxE n plant breedng Related wth the genotypes: Adaptaton: are partcular genotypes adapted to certan envronmental range? Adaptablty / senstvty: are partcular genotypes able to be adapted to mprovements n the envronment? Stablty: s the performance of partcular genotypes consstent? Related wth the envronments: Groupng of trals nto mega-envronments: fndng structure n the TPE. Gven a structure of the TPE optmze the choce of trals to represent the TPE
11 Summary G x E = Dfferent genotypes respond dfferently to a change n the envronment G x E may result n heterogenety of varance and rerankng Reacton norm s an mportant concept Non-parallel reactons norms = G x E = genetc varaton n envronmental senstvty/plastcty/adaptablty 21 Concepts of stablty 22 11
12 Dfferent concepts of stablty Stablty s a measure of varablty n performance across envronments Constant performance s better than no performance (food securty) Predctable response to mprovement of envronment s desrable, E.g. fertlzer Dfferent defntons needed 23 Stablty and predctablty Desrable: Low senstvty to unpredctable changes n E E.g. Temporal fluctuaton n E, such as the weather Hgh senstvty to predctable changes n E E.g. Good response to fertlzer 24 12
13 Key dfferences n stablty concepts Slope of the reacton norm Varablty around reacton norm 25 Defntons of stablty Statc and dynamc stablty (Becker and Leon, 1988) Type 1 to 4 (Ln et al., 1986; Ln and Bnns, 1988) Macro-envronmental, mcro-envronmental senstvty and unformty (Falconer and Mackay, 1996; Mulder et al. 2013) 26 13
14 Statc stablty = Bologcal concept of stablty Measures the overall varablty of a genotype (or a lne ) over envronments General model: μ μ G E GE Statc (n)stablty: var( μ.) across envronments 27 Dynamc stablty Agronomc concept of stablty Does not nclude the predctable varablty n performance across envronments General model: μ μ G E GE Dynamc (n)stablty: σ 2 G E Does not penalze genotypes for varaton due to 2 a predctable response to the envronment ( ) σ E 28 14
15 Dynamc versus statc stablty n fgures Not statc Not dynamc stable b=1.5 Statc Dynamc stable b= Fnlay-Wlknson regresson & stablty measures FW-regresson = Lnear reacton-norm model For the average performance of lne n envronment () μ μ G E (1 β ) δ = overall mean G = overall value of genotype E = overall value of envronment = (lnear) senstvty of genotype to envronment > 0: above average senstve < 0: below average senstve E s the average performance n envronment = measure of qualty of envronment 30 15
16 Fnlay-Wlknson Regresson μ μ G E (1 β ) δ μ μ G E β E δ GE β E δ FW-regresson tres to capture GxE-nteracton as a lnear functon of the envronment Idea: - Some genotypes are generally more responsve to envronmental change ( >0) - Other genotypes are generally less responsve to envronmental change ( <0) 31 Type 1 stablty = statc stablty FW-regresson: μ μ G E (1 β ) δ Lttle change of phenotype over envronments Lttle plastcty Slope of reacton norm of ~0-1 Dynamc stable, b=1 Statc stable, b=
17 Type 2 stablty = dynamc stablty FW-regresson: Expected response to envronment Slope of reacton norm ~1 0 and var( ) s small GE 0 μ μ G E (1 β ) δ Dynamc stable, b=1 Statc stable, b=0 33 Type 3 stablty FW-regresson: μ μ G E (1 β ) δ Predctable change of phenotype over envronments Lnearly predctable GxE-nteracton can take any value, but var( ) s small Stable genotype has low resdual varance or hgh R 2 Dynamc stablty measure Eberhart and Russell (1966) 34 17
18 Stablty type 4 FW-regresson: μ μ G E (1 β ) δ Consders locaton vs yearly varaton Good response to locaton varaton Lttle response to temporal varaton ( weather ) Responsve to predctable changes, robust aganst unpredctable changes Dynamc stablty Refnement of type 3 stablty Ln and Bnns (1988) 35 Macro- and mcro-envronmental senstvty Macro-envronment: known envronmental factor or the envronmental mean (=Fnlay-Wlknson) Dfferences n slope of reacton norm Type 2 stablty Mcro-envronment: unknown envronmental factor Dfferences n resdual varance Type 3/4 stablty Envronmental canalzaton 36 18
19 Unformty = less varablty Usually wthn an envronment But can be hdden varaton n reacton norm P = A + E; Unformty = lttle varaton n E Type 3/4 stablty In evoluton called envronmental canalzaton Lectures Wednesday 37 Summary Dfference n slope of reacton norm Dfference n resdual varance Type 1 and type 2 stablty Macro-envronmental senstvty Plastcty Adaptaton Type 3 and type 4 stablty Mcro-envronmental senstvty Unformty Canalzaton 38 19
Recall 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 informationThe Ordinary Least Squares (OLS) Estimator
The Ordnary Least Squares (OLS) Estmator 1 Regresson Analyss Regresson Analyss: a statstcal technque for nvestgatng and modelng the relatonshp between varables. Applcatons: Engneerng, the physcal and chemcal
More information3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X
Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number
More informationLinear Regression Analysis: Terminology and Notation
ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented
More information1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands
Content. Inference on Regresson Parameters a. Fndng Mean, s.d and covarance amongst estmates.. Confdence Intervals and Workng Hotellng Bands 3. Cochran s Theorem 4. General Lnear Testng 5. Measures of
More informationFeature Selection: Part 1
CSE 546: Machne Learnng Lecture 5 Feature Selecton: Part 1 Instructor: Sham Kakade 1 Regresson n the hgh dmensonal settng How do we learn when the number of features d s greater than the sample sze n?
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 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 informationKernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
More informationSome basic statistics and curve fitting techniques
Some basc statstcs and curve fttng technques Statstcs s the dscplne concerned wth the study of varablty, wth the study of uncertanty, and wth the study of decsonmakng n the face of uncertanty (Lndsay et
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 informationBIO Lab 2: TWO-LEVEL NORMAL MODELS with school children popularity data
Lab : TWO-LEVEL NORMAL MODELS wth school chldren popularty data Purpose: Introduce basc two-level models for normally dstrbuted responses usng STATA. In partcular, we dscuss Random ntercept models wthout
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 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 informationLecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding
Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an
More informationLecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding
Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 008 Recall: man dea of lnear regresson Lnear regresson can be used to study
More informationn α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0
MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector
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 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 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 informationPHYS 705: Classical Mechanics. Calculus of Variations II
1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary
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 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 informationLecture 6 Heritability and Field Design. Lucia Gutierrez lecture notes Tucson Winter Institute
Lecture 6 Hertablty and Feld Desgn Luca Guterre lecture notes Tucson Wnter Insttute Selecton Response c µ 0 µ S S µ mean of te ntal Random Matng populaton 0 µ mean of S µ mean of a progeny of selected
More informationChapter 5 Multilevel Models
Chapter 5 Multlevel Models 5.1 Cross-sectonal multlevel models 5.1.1 Two-level models 5.1.2 Multple level models 5.1.3 Multple level modelng n other felds 5.2 Longtudnal multlevel models 5.2.1 Two-level
More informationAnalysis of general and specific combining abilities of popcorn populations, including selfed parents
Research Artcle Genetcs and Molecular Bology, 6, 4, 465-471 (003) Copyrght by the Brazlan Socety of Genetcs. Prnted n Brazl www.sbg.org.br Analyss of general and specfc combnng abltes of popcorn populatons,
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 information28. SIMPLE LINEAR REGRESSION III
8. SIMPLE LINEAR REGRESSION III Ftted Values and Resduals US Domestc Beers: Calores vs. % Alcohol To each observed x, there corresponds a y-value on the ftted lne, y ˆ = βˆ + βˆ x. The are called ftted
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 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 informationx yi In chapter 14, we want to perform inference (i.e. calculate confidence intervals and perform tests of significance) in this setting.
The Practce of Statstcs, nd ed. Chapter 14 Inference for Regresson Introducton In chapter 3 we used a least-squares regresson lne (LSRL) to represent a lnear relatonshp etween two quanttatve explanator
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 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 informationChapter 4: Regression With One Regressor
Chapter 4: Regresson Wth One Regressor Copyrght 2011 Pearson Addson-Wesley. All rghts reserved. 1-1 Outlne 1. Fttng a lne to data 2. The ordnary least squares (OLS) lne/regresson 3. Measures of ft 4. Populaton
More informationOn Epigenomic Privacy: Tracking Personal MicroRNA Expression Profiles over Time
On Epgenomc Prvacy: Trackng Personal McroRNA Expresson Profles over Tme Mchael Backes, Pascal Berrang, Anne Hecksteden, Mathas Humbert, Andreas Keller and Tm Meyer 21st February 2016 On Epgenomc Prvacy:
More informationEconometrics of Panel Data
Econometrcs of Panel Data Jakub Mućk Meetng # 8 Jakub Mućk Econometrcs of Panel Data Meetng # 8 1 / 17 Outlne 1 Heterogenety n the slope coeffcents 2 Seemngly Unrelated Regresson (SUR) 3 Swamy s random
More information18. SIMPLE LINEAR REGRESSION III
8. SIMPLE LINEAR REGRESSION III US Domestc Beers: Calores vs. % Alcohol Ftted Values and Resduals To each observed x, there corresponds a y-value on the ftted lne, y ˆ ˆ = α + x. The are called ftted values.
More informationLab 4: Two-level Random Intercept Model
BIO 656 Lab4 009 Lab 4: Two-level Random Intercept Model Data: Peak expratory flow rate (pefr) measured twce, usng two dfferent nstruments, for 17 subjects. (from Chapter 1 of Multlevel and Longtudnal
More informationInner Product. Euclidean Space. Orthonormal Basis. Orthogonal
Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,
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 information10-701/ Machine Learning, Fall 2005 Homework 3
10-701/15-781 Machne Learnng, Fall 2005 Homework 3 Out: 10/20/05 Due: begnnng of the class 11/01/05 Instructons Contact questons-10701@autonlaborg for queston Problem 1 Regresson and Cross-valdaton [40
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 informationChecking Pairwise Relationships. Lecture 19 Biostatistics 666
Checkng Parwse Relatonshps Lecture 19 Bostatstcs 666 Last Lecture: Markov Model for Multpont Analyss X X X 1 3 X M P X 1 I P X I P X 3 I P X M I 1 3 M I 1 I I 3 I M P I I P I 3 I P... 1 IBD states along
More informationLimited Dependent Variables
Lmted Dependent Varables. What f the left-hand sde varable s not a contnuous thng spread from mnus nfnty to plus nfnty? That s, gven a model = f (, β, ε, where a. s bounded below at zero, such as wages
More informationStatistics Chapter 4
Statstcs Chapter 4 "There are three knds of les: les, damned les, and statstcs." Benjamn Dsrael, 1895 (Brtsh statesman) Gaussan Dstrbuton, 4-1 If a measurement s repeated many tmes a statstcal treatment
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 informationCathy Walker March 5, 2010
Cathy Walker March 5, 010 Part : Problem Set 1. What s the level of measurement for the followng varables? a) SAT scores b) Number of tests or quzzes n statstcal course c) Acres of land devoted to corn
More informationTopic- 11 The Analysis of Variance
Topc- 11 The Analyss of Varance Expermental Desgn The samplng plan or expermental desgn determnes the way that a sample s selected. In an observatonal study, the expermenter observes data that already
More informationLectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix
Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could
More informationMarkov Chain Monte Carlo Lecture 6
where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways
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 informationDifference Equations
Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1
More informationIII. Econometric Methodology Regression Analysis
Page Econ07 Appled Econometrcs Topc : An Overvew of Regresson Analyss (Studenmund, Chapter ) I. The Nature and Scope of Econometrcs. Lot s of defntons of econometrcs. Nobel Prze Commttee Paul Samuelson,
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
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 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 informationQuantitative Genetic Models Least Squares Genetic Model. Hardy-Weinberg (1908) Principle. change of allele & genotype frequency over generations
Quanttatve Genetc Models Least Squares Genetc Model Hardy-Wenberg (1908) Prncple partton of effects P = G + E + G E P s phenotypc effect G s genetc effect E s envronmental effect G E s nteracton effect
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 informationLaboratory 1c: Method of Least Squares
Lab 1c, Least Squares Laboratory 1c: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly
More informationSTAT 3008 Applied Regression Analysis
STAT 3008 Appled Regresson Analyss Tutoral : Smple Lnear Regresson LAI Chun He Department of Statstcs, The Chnese Unversty of Hong Kong 1 Model Assumpton To quantfy the relatonshp between two factors,
More informationLaboratory 3: Method of Least Squares
Laboratory 3: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly they are correlated wth
More informationStability Analysis of Spike Yield of Winter Wheat
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,
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have
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 informationEcon Statistical Properties of the OLS estimator. Sanjaya DeSilva
Econ 39 - Statstcal Propertes of the OLS estmator Sanjaya DeSlva September, 008 1 Overvew Recall that the true regresson model s Y = β 0 + β 1 X + u (1) Applyng the OLS method to a sample of data, we estmate
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 informationFinancing Innovation: Evidence from R&D Grants
Fnancng Innovaton: Evdence from R&D Grants Sabrna T. Howell Onlne Appendx Fgure 1: Number of Applcants Note: Ths fgure shows the number of losng and wnnng Phase 1 grant applcants over tme by offce (Energy
More informationResource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis
Resource Allocaton and Decson Analss (ECON 800) Sprng 04 Foundatons of Regresson Analss Readng: Regresson Analss (ECON 800 Coursepak, Page 3) Defntons and Concepts: Regresson Analss statstcal technques
More informationEXST7015 : Statistical Techniques II ANOVA Design Identification Page 1
NOV Desgn Identfcaton Page 1 Expermental Desgn Identfcaton To correctly desgn an experment, or to analyze a desgned experment, you must be able to look at a desgn stuaton and correctly assess the salent
More informationβ0 + β1xi. You are interested in estimating the unknown parameters β
Revsed: v3 Ordnar Least Squares (OLS): Smple Lnear Regresson (SLR) Analtcs The SLR Setup Sample Statstcs Ordnar Least Squares (OLS): FOCs and SOCs Back to OLS and Sample Statstcs Predctons (and Resduals)
More informationEEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming
EEL 6266 Power System Operaton and Control Chapter 3 Economc Dspatch Usng Dynamc Programmng Pecewse Lnear Cost Functons Common practce many utltes prefer to represent ther generator cost functons as sngle-
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 informationThe Study of Teaching-learning-based Optimization Algorithm
Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute
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 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 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 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 informationSTAT 511 FINAL EXAM NAME Spring 2001
STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte
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 informationThis column is a continuation of our previous column
Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard
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 informationLecture 3: Probability Distributions
Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the
More informationOutline. Zero Conditional mean. I. Motivation. 3. Multiple Regression Analysis: Estimation. Read Wooldridge (2013), Chapter 3.
Outlne 3. Multple Regresson Analyss: Estmaton I. Motvaton II. Mechancs and Interpretaton of OLS Read Wooldrdge (013), Chapter 3. III. Expected Values of the OLS IV. Varances of the OLS V. The Gauss Markov
More informationGENETIC ARCHITECTURE AND POSTZYGOTIC REPRODUCTIVE ISOLATION: EVOLUTION OF BATESON DOBZHANSKY MULLER INCOMPATIBILITIES IN A POLYGENIC MODEL
ORIGINAL ARTICLE do:./.558-5646.9.86.x GENETIC ARCHITECTURE AND POSTZYGOTIC REPRODUCTIVE ISOLATION: EVOLUTION OF BATESON DOBZHANSKY MULLER INCOMPATIBILITIES IN A POLYGENIC MODEL Janna L. Ferst,,3 and Thomas
More informationAssessing inter-annual and seasonal variability Least square fitting with Matlab: Application to SSTs in the vicinity of Cape Town
Assessng nter-annual and seasonal varablty Least square fttng wth Matlab: Applcaton to SSTs n the vcnty of Cape Town Francos Dufos Department of Oceanography/ MARE nsttute Unversty of Cape Town Introducton
More informationDesign and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm
Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:
More informationFirst Year Examination Department of Statistics, University of Florida
Frst Year Examnaton Department of Statstcs, Unversty of Florda May 7, 010, 8:00 am - 1:00 noon Instructons: 1. You have four hours to answer questons n ths examnaton.. You must show your work to receve
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 informationDepartment of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6
Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.
More informationGene library based Resource Allocation in Time Sensitive Large Scale Networked Control Systems. Preliminary Exam Presentation Unnati Ojha
Gene lbrary based Resource Allocaton n Tme Senstve Large Scale Networked Control Systems Prelmnary Exam Presentaton Unnat Ojha ADAC Lab, NCSU Outlne Objectves, Motvaton System Descrpton Gene Lbrary Bologcal
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 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 informationLearning Objectives for Chapter 11
Chapter : Lnear Regresson and Correlaton Methods Hldebrand, Ott and Gray Basc Statstcal Ideas for Managers Second Edton Learnng Objectves for Chapter Usng the scatterplot n regresson analyss Usng the method
More informationHomework Assignment 3 Due in class, Thursday October 15
Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.
More informationU-Pb Geochronology Practical: Background
U-Pb Geochronology Practcal: Background Basc Concepts: accuracy: measure of the dfference between an expermental measurement and the true value precson: measure of the reproducblty of the expermental result
More informationLossy Compression. Compromise accuracy of reconstruction for increased compression.
Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost
More informationSupporting Information
Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to
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 information