A general sectional volume equation for classical geometries of tree stem

Size: px
Start display at page:

Download "A general sectional volume equation for classical geometries of tree stem"

Transcription

1 Madera y Boque 6 (2), 2: NOTA TÉCNICA A geeral ectoal volume equato for clacal geometre of tree tem Ua ecuacó geeral para el volume de la eccó de la geometría cláca del troco de lo árbole Gldardo Cruz de Leó ABTRACT Th work refer to the clacal theory of tree tem form. It how the dervato of a geeral ectoal volume equato for frutum of old of revoluto geerated by the fucto y 2 p x where, p a potve cotat, ad ay potve teger. The cylder cae preet a gular tuato becaue of t ectoal volume equato caot be defed for a t kow for the geeratg fucto. However, that geometry mplct a a trval oluto of the derved equato. The kow ectoal volume equato for frutum of parabolod, cood ad elod are partcular cae of that equato for, 2, ad 3, repectvely. The geeral ectoal volume equato ha a uexpected tattcal ature. It gve a a arthmetc mea of geometrc mea The clacal theory of tree tem form cotue beg preet the foret meauremet teachg ad reearch. Th work could cotrbute to mprove the udertadg o that theory. PALABRA CLAE: Dedrometry, appled mathematc. REUMEN Ete trabajo e refere a la teoría cláca de la forma del troco del árbol. e muetra la dervacó de ua ecuacó de volume geeral de la eccó de óldo de revolucó trucado geerada por la fucó y 2 p x dode, p e ua cotate potva, y u etero potvo. El cao del cldro cottuye u cao gular pue u ecuacó de volume de la eccó o e puede defr para, ya que e coocdo por la fucó geeradora. embargo, ea geometría etá mplícta como ua olucó trval de la ecuacó dervada. La ecuacoe coocda de volume de eccoe trucada de parabolode, coode y elode o cao partculare de la ecuacó para, 2 y 3, repectvamete. La ecuacó geeral de volume de la eccó e de ua aturaleza etadítca eperada. e da como ua meda artmétca de meda geométrca. La teoría cláca de la forma del árbol gue etado preete e la eeñaza de medcó e vetgacó foretal. Ete trabajo podría cotrbur a mejorar la compreó de ea teoría. PALABRA CLAE: Dedrometría, matemátca aplcada. Facultad de Igeería e Tecología de la Madera. Uverdad Mchoacaa de a Ncolá de Hdalgo. Apartado Potal 58, 58. Morela Mchoacá, Méxco. Phoe: (443) E-mal: [email protected]

2 9 A ectoal volume equato for geometre of the tree tem clacal mode INTRODUCTION Tree tem ad log volume etmato are ome of the ma goal foret meauremet. It a dffcult tak becaue thoe volume deped o ther geometry (Brack, 999). For more tha a cetury reearcher have bee workg extevely o the problem of tree tem geometry from fudametal ad emprcal pot of vew. I the frt cae oe le of aemet ha bee by mea of mechacal theore. A quoted by Laro (963) ad Dea ad Log (986), aumg that tree tem develop a partcular form to mata the bedg tre cotat uder the fluece of wd, 893, Metzger foud a tem geometry where the cube dameter proportoal to heght. Regardg that gravty determe tree tem hape the quare dameter reult proportoal to the cube of heght (McMaho, 973). The emprcal pot of vew ca be dvded o clacal ad curret theore. I the clacal theory a tree tem modeled by part ug mple geometre of old of revoluto (Grave, 96). Curret theore ugget partcular geometrc model developed for each pece by mea of taper equato for the whole tree tem (Wet, 24). The clacal theory of tree tem form ha a gfcat poto the foret meauremet lterature. At leat for a cetury t ha bee cluded tadard book of that feld (Grave, 96; a Laar ad Akça, 27). The clacal theory codered a part of old reearch foret meauremet (Wet, 24). However, t cotue beg a ueful referece reearch problem that deped o tree tem geometry. For tace, t ha bee ued to derve the equato for the cetrod poto the developmet of the cetrod amplg method for tree tem volume etmato (Wood et al., 99) ad to derve a geeral relato betwee tem volume ad tem urface form-factor depedet of poto (Ioue, 26). Th work how the dervato of a geeral ectoal volume equato for the clacal tree tem geometre. It baed o the clacal theory of tem form, ectoal method of volume etmato, elemetary algebra, ad calculu. A ummary of that methodology provded below. MODEL AND METHOD Clacal geometre of tree tem Tree tem form ca be modeled by logtudal ecto ug elemetary geometre of old of revoluto geerated by the equato y 2 p x [] where p ad are cotat uch that p > ad (Grave, 96). Curretly, oly the cae,, 2, ad 3, related to cylder, cood, parabolod, ad elod, repectvely, are aged the clacal tree tem geometre (Déguez- Arada et al., 23). For th work, the orgal cocepto of equato [] wll repreet the clacal geometre of tree tem ad the tudy refer to ay potve teger. Tree tem volume etmato Dfferet type of method to etmate tree tem volume are ued foret meauremet. ome of thoe method cot : ) to propoe a taper equato ad the to etmate the volume by tegral calculu, ) to propoe a volume equato where the volume ca be drectly obtaed a a fucto of heght, ormal dameter, ad total heght, ad )

3 Madera y Boque 6 (2), 2: to ue a ectoal method where the volume ca be kow for logtudal ecto a fucto of cro ectoal area ad legth (Brack, 999). The problem of volume etmato crtcal whe the volume of log eed to be etmated at feld where o formato of the orgal tree tem tadard parameter or geometry at had. For thoe commo tuato the ue of ectoal method uavodable (Bruce, 982). Geeral defto of ectoal method For mplcty, tree tem logtudal ecto ad log wll be called here tree tem egmet. Ay ectoal method to etmate the volume of a tree tem egmet, of legth L, refereed to the volume of a cylder ad ca be wrtte a L [2] where a average cro ectoal area (Avery ad Burkhart, 22). What defe a partcular ectoal method the form of a fucto of elected cro ectoal area. Equato [2] wll be eetal th work. tadard ectoal method The geerally accepted ectoal method for volume etmato of tree tem egmet the foret meauremet lterature are the Huber, mala, ad Newto method. Followg the otato of Chapma ad Meyer (949), for a tree tem egmet, of legth L, ed cro ectoal area, B at the large ed, b at the mall ed, ad B /2 at the mddle, thoe tadard ectoal method ca be repectvely defed by N H B / 2 B + 2 b B + 4B / 2 + b 6 [3] [4] [5] The, H H L, L, ad N N L, are ther correpodg volume agreemet to equato [2]. Defto of ytem ad parameter for th work Uually, old of revoluto geerated by equato [] are placed a x y z Cartea coordate ytem wth ther ax of rotato alog the x ax ad ther tp at the org (Déguez-Arada et al., 23). The ytem for th work are defed a frutum of legth L of thoe old of revoluto for ay potve teger. The ed cro ectoal area wll be called here, for the mall ed, at x x, ad for the large ed, at x x 2. By defto, x <x 2, the, L x 2 -x. ectoal volume equato for the tree tem clacal form The volume for frutum of old of revoluto geerated by equato [], for,, 2, ad 3, a fucto of ed cro ectoal area ad legth, are L L + L 2 [6] [7]

4 92 A ectoal volume equato for geometre of the tree tem clacal mode L L 4 [8] [9] for cylder, parabolod, cood, ad elod, repectvely (Grave, 96). I agreemet to equato [2] ca be defed for equato [6]-[9]. For cylder,. For frutum of parabolod, cood ad elod, ther mea cro ectoal area are repectvely [] [] [2] 4 I the followg, t wll be how that equato [7]-[9] are partcular cae of a geeral ectoal volume equato related to the geeratg equato [] for ay potve teger. The cae deerve a partcular dcuo gve at the ed. REULT Dervato of a geeral ectoal volume equato for clacal geometre of tree tem The ma reult of th work how the form of a mathematcal theorem. Theorem If the volume for a old of revoluto frutum, of legth L, ad ed cro ectoal area at x x, ad at x x 2, geerated by the equato y 2 p x, where a potve teger, the, ca be expreed the form + [3] Proof If the geeratg fucto for a old of revoluto gve by y 2 p x the the volume for t frutum from x x to x x 2, gve by tegral calculu, π p (tewart, 22). The relato [4] [5] well kow the mathematcal lterature (pegel ad Moyer, 27). Gve that L x 2 - x ad, from equato [], equato [5] take the form x + 2 x + L ( x2 x ) ( x2 x) x2 x / ( ) / ( ) x2 / π p x /π p + + L ( ) / / ( x2 x ) πp [6]

5 Madera y Boque 6 (2), 2: ubttutg equato [6] equato [4], reult ( ) / + whch exactly equato [3]. DICUION L [7] The geeral ectoal volume equato [3] ha bee derved for old of revoluto geerated by the equato y 2 p x for ay potve teger. It ca be ealy how that, for, 2, ad 3, the ectoal volume equato [7]-[9] for frutum of parabolod, cood ad elod, are repectvely recovered. Alo, for, baal area, ad LH, ther total volume, H/(+), are obtaed. However, a t ca be ee equato [3] a ectoal volume equato for the cylder cae caot be defed. Neverthele, t ot eceary becaue the geeral equato reduce to the cylder volume equato whe for ay. That codto of cylder volume recovery eem to be eceary for ay ectoal volume equato. I a propoal to evaluate varou ectoal method to etmate butt log volume Bruce (982) take that codto a a gude to elect them. Let u udertad the meag of equato [3]. I agreemet to equato [2] t ca be wrtte the form L where hould correpod to a geeral mea cro ectoal area. I partcular, for / /( + ) equato [],, the arthmetc average of the ed cro ectoal area ad. For equato [], 2 the arthmetc average of ed cro ectoal area,,, ad ther geometrc mea (Uraga-aleca, 28). I geeral, f X, X 2,... X, are the poble value take by a varable X, for a ample of ze, ther geometrc mea a meaure of cetral tedecy defed by X X 2 X (Chapma ad Meyer, 949; a Laar ad Akça, 27). The term for,,2,...,, repreet the geometrc mea cro ectoal area for, X X 2... X- ad X -+ X -+2. X -+2 X -+ X. The, the geeral mea cro ectoal area /( + ) correpod to the arthmetc average of thoe geometrc mea. Although the problem for th work wa ot orgally defed a of tattcal ature t tured o a tattcal oe whoe complete aaly progre. A addtoal property of equato [3] that t repreet a ymmetrcal fucto o ad, for ay, becaue of what mea that egmet volume depedet of t oretato alog t ax of rotato. Alo, the equato for the tadard ectoal method obey that ymmetry property. That codto could be aother feature to take accout the propoal of ew ectoal method for volume etmato. The mot mportat cotrbuto of th work the geeral ectoal volume equato [3] from what kow reult of the foret meauremet feld, ce more

6 94 A ectoal volume equato for geometre of the tree tem clacal mode tha a cetury (Grave, 96), are partcular cae. ACKNOWLEDGEMENT The author deeply ackowledge Profeor Adra Goodw for h helpful uggeto. Th work wa upported by Coordacó de la Ivetgacó Cetífca de la Uverdad Mchoacaa de a Ncolá de Hdalgo. Morela Mchoacá, Méxco. REFERENCE Avery, T.E. ad Burkhart, H.E. 22. Foret Meauremet. Mc Graw Hll. UA. Brack, C Foret Meauremet ad Modelg [ole]. Avalable from rea o c a t e d. a u. e d u. a u / meurato/volume.htm. Bruce, D Butt Log olume Etmator. Foret c. 28(3): Chapma, H.H. ad Meyer, W.H Foret Meurato. McGraw-Hll. Edto. UA. Dea, T.J. ad Log, J.N aldty of Cotat-tre ad Elatc-tablty Prcple of tem Formato Pu cotorta ad Trfolum pratee. Aal of Botay. 58: Déguez-Arada, U., Barro-Ata, M., Catedo-Dorado, F., Ruíz-Gozález, A.D., Álvarez-Taboada, M.F., Álvarez- Gozález, J.G. y Rojo-Albareca, A. 23. Dedrometría. Mud-Prea. Madrd, Epaña. Grave, H Foret Meurato. Joh Wley & o. UA. Ioue, A. 26. A model for the relatohp betwee form-factor for tem volume ad thoe for tem urface area coferou pece. J. For. Re. : Laro, P.R tem Form Developmet of Foret Tree. For. c. Mo. 5. McMaho, T ze ad hape Bology. cece. 79: pegel, M.R. ad Moyer, R.E. 27. Algebra uperor. McGraw-Hll. Méxco. tewart, J. 22. Cálculo Tracedete Tempraa. Thompo Learg. Méxco. Uraga-aleca, L.P. 28. Aplcacó del Modelo Cóco egmetado a lo Tpo Dedrométrco Cláco como Fucó del Número de egmeto. M.c. the, Facultad de Igeería e Tecología de la Madera, Uverdad Mchoacáa de a Ncolá de Hdalgo, Morela Mchoacá, Méxco. a Laar, A. ad Akça, A. 27. Foret Meurato. prger. Netherlad. Wet, P.W. 24. Tree ad Foret Meauremet. prger-erlag. Berlí. Wood, G.B., Wat, H.. Jr., Loy, R.J. ad Mle, J.A. 99. Cetrod amplg: A varat of mportace amplg for etmatg the volume of ample tree of radate pe. Foret Ecology ad Maagemet. 36: Maucrto recbdo el 3 de julo de 29 Aceptado el 8 de eero de 2 Ete documeto e debe ctar como: Cruz de Leó, G. 2. A geeral ectoal volume equato for clacal geometre of tree tem. Madera y Boque 6(2):89-94.

Conversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes

Conversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes Covero of No-Lear Stregth Evelope to Geeralzed Hoek-Brow Evelope Itroducto The power curve crtero commoly ued lmt-equlbrum lope tablty aaly to defe a o-lear tregth evelope (relatohp betwee hear tre, τ,

More information

Average Price Ratios

Average Price Ratios Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

More information

GRADUATION PROJECT REPORT

GRADUATION PROJECT REPORT SPAM Flter School of Publc Admtrato Computer Stude Program GRADUATION PROJECT REPORT 2007-I-A02 SPAM Flter Project group leader: Project group member: Supervor: Aeor: Academc year (emeter): MCCS390 Graduato

More information

Data Analysis Toolkit #10: Simple linear regression Page 1

Data Analysis Toolkit #10: Simple linear regression Page 1 Data Aaly Toolkt #0: mple lear regreo Page mple lear regreo the mot commoly ued techque f determg how oe varable of teret the repoe varable affected by chage aother varable the explaaty varable. The term

More information

Supply Chain Management Chapter 5: Application of ILP. Unified optimization methodology. Beun de Haas

Supply Chain Management Chapter 5: Application of ILP. Unified optimization methodology. Beun de Haas Supply Cha Maagemet Chapter 5: Ufed Optmzato Methodology for Operatoal Plag Problem What to do whe ILP take too much computato tme? Applcato of ILP Tmetable Dutch Ralway (NS) Bu ad drver chedulg at Coeo,

More information

Simple Linear Regression

Simple Linear Regression Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8

More information

Basic statistics formulas

Basic statistics formulas Wth complmet of tattcmetor.com, the te for ole tattc help Set De Morga Law Bac tattc formula Meaure of Locato Sample mea (AUB) c A c B c Commutatvty & (A B) c A c U B c A U B B U A ad A B B A Aocatvty

More information

In the UC problem, we went a step further in assuming we could even remove a unit at any time if that would lower cost.

In the UC problem, we went a step further in assuming we could even remove a unit at any time if that would lower cost. uel Schedulg (Chapter 6 of W&W.0 Itroducto I ecoomc dpatch we aumed the oly lmtato were o the output of the geerator: m g. h aumed that we could et ge to ay value we dered wth the rage, at ay tme, to acheve

More information

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira [email protected],

More information

Swarm Based Truck-Shovel Dispatching System in Open Pit Mine Operations

Swarm Based Truck-Shovel Dispatching System in Open Pit Mine Operations Swarm Baed Truck-Shovel Dpatchg Sytem Ope Pt Me Operato Yaah Br, W. Scott Dubar ad Alla Hall Departmet of Mg ad Meral Proce Egeerg Uverty of Brth Columba, Vacouver, B.C., Caada Emal: [email protected] Abtract

More information

3.6. Metal-Semiconductor Field Effect Transistor (MESFETs)

3.6. Metal-Semiconductor Field Effect Transistor (MESFETs) .6. Metal-Semcouctor Fel Effect rator (MESFE he Metal-Semcouctor-Fel-Effect-rator (MESFE cot of a couctg chael potoe betwee a ource a ra cotact rego a how the Fgure.6.1. he carrer flow from ource to ra

More information

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there

More information

Confidence Intervals for Linear Regression Slope

Confidence Intervals for Linear Regression Slope Chapter 856 Cofidece Iterval for Liear Regreio Slope Itroductio Thi routie calculate the ample ize eceary to achieve a pecified ditace from the lope to the cofidece limit at a tated cofidece level for

More information

APPENDIX III THE ENVELOPE PROPERTY

APPENDIX III THE ENVELOPE PROPERTY Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful

More information

Chapter Eight. f : R R

Chapter Eight. f : R R Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,

More information

CH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID

CH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID CH. ME56 STTICS Ceter of Gravt, Cetrod, ad Momet of Ierta CENTE OF GITY ND CENTOID 5. CENTE OF GITY ND CENTE OF MSS FO SYSTEM OF PTICES Ceter of Gravt. The ceter of gravt G s a pot whch locates the resultat

More information

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis 6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces

More information

The simple linear Regression Model

The simple linear Regression Model The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg

More information

On formula to compute primes and the n th prime

On formula to compute primes and the n th prime Joural's Ttle, Vol., 00, o., - O formula to compute prmes ad the th prme Issam Kaddoura Lebaese Iteratoal Uversty Faculty of Arts ad ceces, Lebao Emal: [email protected] amh Abdul-Nab Lebaese Iteratoal

More information

Analysis of Two-Echelon Perishable Inventory System with Direct and Retrial demands

Analysis of Two-Echelon Perishable Inventory System with Direct and Retrial demands O Joural of Mathematc (O-JM) e-: 78-578 p-: 9-765X. Volume 0 ue 5 Ver. (ep-oct. 04) 5-57 www.oroural.org aly of Two-chelo erhable vetory ytem wth rect ad etral demad M. amehpad C.eryaamy K. Krha epartmet

More information

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time. Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E

More information

Curve Fitting and Solution of Equation

Curve Fitting and Solution of Equation UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed

More information

Credibility Premium Calculation in Motor Third-Party Liability Insurance

Credibility Premium Calculation in Motor Third-Party Liability Insurance Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53

More information

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN Wojcech Zelńsk Departmet of Ecoometrcs ad Statstcs Warsaw Uversty of Lfe Sceces Nowoursyowska 66, -787 Warszawa e-mal: wojtekzelsk@statystykafo Zofa Hausz,

More information

CHAPTER 2. Time Value of Money 6-1

CHAPTER 2. Time Value of Money 6-1 CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show

More information

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected

More information

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute

More information

Numerical Methods with MS Excel

Numerical Methods with MS Excel TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how

More information

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1 akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of

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

Load and Resistance Factor Design (LRFD)

Load and Resistance Factor Design (LRFD) 53:134 Structural Desg II Load ad Resstace Factor Desg (LRFD) Specfcatos ad Buldg Codes: Structural steel desg of buldgs the US s prcpally based o the specfcatos of the Amerca Isttute of Steel Costructo

More information

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh

More information

ON SLANT HELICES AND GENERAL HELICES IN EUCLIDEAN n -SPACE. Yusuf YAYLI 1, Evren ZIPLAR 2. [email protected]. evrenziplar@yahoo.

ON SLANT HELICES AND GENERAL HELICES IN EUCLIDEAN n -SPACE. Yusuf YAYLI 1, Evren ZIPLAR 2. yayli@science.ankara.edu.tr. evrenziplar@yahoo. ON SLANT HELICES AND ENERAL HELICES IN EUCLIDEAN -SPACE Yusuf YAYLI Evre ZIPLAR Departmet of Mathematcs Faculty of Scece Uversty of Akara Tadoğa Akara Turkey yayl@sceceakaraedutr Departmet of Mathematcs

More information

Performance Attribution. Methodology Overview

Performance Attribution. Methodology Overview erformace Attrbuto Methodology Overvew Faba SUAREZ March 2004 erformace Attrbuto Methodology 1.1 Itroducto erformace Attrbuto s a set of techques that performace aalysts use to expla why a portfolo's performace

More information

Topic 5: Confidence Intervals (Chapter 9)

Topic 5: Confidence Intervals (Chapter 9) Topic 5: Cofidece Iterval (Chapter 9) 1. Itroductio The two geeral area of tatitical iferece are: 1) etimatio of parameter(), ch. 9 ) hypothei tetig of parameter(), ch. 10 Let X be ome radom variable with

More information

Relaxation Methods for Iterative Solution to Linear Systems of Equations

Relaxation Methods for Iterative Solution to Linear Systems of Equations Relaxato Methods for Iteratve Soluto to Lear Systems of Equatos Gerald Recktewald Portlad State Uversty Mechacal Egeerg Departmet [email protected] Prmary Topcs Basc Cocepts Statoary Methods a.k.a. Relaxato

More information

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has

More information

1. The Time Value of Money

1. The Time Value of Money Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg

More information

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are : Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of

More information

Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems

Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems Fto: A Fater, Permutable Icremetal Gradet Method or Bg ata Problem Aaro J. eazo Tbéro S. Caetao Jut omke NICTA ad Autrala Natoal Uverty [email protected] [email protected] [email protected]

More information

Bayesian Network Representation

Bayesian Network Representation Readgs: K&F 3., 3.2, 3.3, 3.4. Bayesa Network Represetato Lecture 2 Mar 30, 20 CSE 55, Statstcal Methods, Sprg 20 Istructor: Su-I Lee Uversty of Washgto, Seattle Last tme & today Last tme Probablty theory

More information

On Error Detection with Block Codes

On Error Detection with Block Codes BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 3 Sofa 2009 O Error Detecto wth Block Codes Rostza Doduekova Chalmers Uversty of Techology ad the Uversty of Gotheburg,

More information

THE EQUILIBRIUM MODELS IN OLIGOPOLY ELECTRICITY MARKET

THE EQUILIBRIUM MODELS IN OLIGOPOLY ELECTRICITY MARKET Iteratoal Coferee The Euroea Eletrty Market EEM-4 etember -, 4, Lodz, Polad Proeedg Volume,. 35-4 THE EQUILIBRIUM MODEL IN OLIGOPOLY ELECTRICITY MARKET Agezka Wyłomańka Wrolaw Uverty of Tehology Wrolaw

More information

Credit Risk Evaluation of Online Supply Chain Finance Based on Third-party B2B E-commerce Platform: an Exploratory Research Based on China s Practice

Credit Risk Evaluation of Online Supply Chain Finance Based on Third-party B2B E-commerce Platform: an Exploratory Research Based on China s Practice Iteratoal Joural of u- ad e- Servce, Scece ad Techology Vol.8, No.5 (2015, pp.93-104 http://dx.do.org/10.14257/juet.2015.8.5.09 Credt Rk Evaluato of Ole Supply Cha Face Baed o Thrd-party B2B E-commerce

More information

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50

More information

Report 52 Fixed Maturity EUR Industrial Bond Funds

Report 52 Fixed Maturity EUR Industrial Bond Funds Rep52, Computed & Prted: 17/06/2015 11:53 Report 52 Fxed Maturty EUR Idustral Bod Fuds From Dec 2008 to Dec 2014 31/12/2008 31 December 1999 31/12/2014 Bechmark Noe Defto of the frm ad geeral formato:

More information

STATIC ANALYSIS OF TENSEGRITY STRUCTURES

STATIC ANALYSIS OF TENSEGRITY STRUCTURES SI NYSIS O ENSEGIY SUUES JUIO ES OE HESIS PESENED O HE GDUE SHOO O HE UNIVESIY O OID IN PI UIEN O HE EQUIEENS O HE DEGEE O SE O SIENE UNIVESIY O OID o m mother for her fte geerost. KNOWEDGENS I wat to

More information

10.5 Future Value and Present Value of a General Annuity Due

10.5 Future Value and Present Value of a General Annuity Due Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the

More information

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R = Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are

More information

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl

More information

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral

More information

TI-83, TI-83 Plus or TI-84 for Non-Business Statistics

TI-83, TI-83 Plus or TI-84 for Non-Business Statistics TI-83, TI-83 Plu or TI-84 for No-Buie Statitic Chapter 3 Eterig Data Pre [STAT] the firt optio i already highlighted (:Edit) o you ca either pre [ENTER] or. Make ure the curor i i the lit, ot o the lit

More information

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom. UMEÅ UNIVERSITET Matematsk-statstska sttutoe Multvarat dataaalys för tekologer MSTB0 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multvarat dataaalys för tekologer B, 5 poäg.

More information

MDM 4U PRACTICE EXAMINATION

MDM 4U PRACTICE EXAMINATION MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths

More information

Fundamentals of Mass Transfer

Fundamentals of Mass Transfer Chapter Fudametals of Mass Trasfer Whe a sgle phase system cotas two or more speces whose cocetratos are ot uform, mass s trasferred to mmze the cocetrato dffereces wth the system. I a mult-phase system

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

A particle swarm optimization to vehicle routing problem with fuzzy demands

A particle swarm optimization to vehicle routing problem with fuzzy demands A partcle swarm optmzato to vehcle routg problem wth fuzzy demads Yag Peg, Ye-me Qa A partcle swarm optmzato to vehcle routg problem wth fuzzy demads Yag Peg 1,Ye-me Qa 1 School of computer ad formato

More information

Automated Event Registration System in Corporation

Automated Event Registration System in Corporation teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee

More information

An Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information

An Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog, Frst ad Correspodg Author

More information

α 2 α 1 β 1 ANTISYMMETRIC WAVEFUNCTIONS: SLATER DETERMINANTS (08/24/14)

α 2 α 1 β 1 ANTISYMMETRIC WAVEFUNCTIONS: SLATER DETERMINANTS (08/24/14) ANTISYMMETRI WAVEFUNTIONS: SLATER DETERMINANTS (08/4/4) Wavefuctos that descrbe more tha oe electro must have two characterstc propertes. Frst, scll electros are detcal partcles, the electros coordates

More information

Performance of Store Brands: A Cross-Country Analysis of Consumer Store Brand Preferences, Perceptions, and Risk. Tülin Erdem*

Performance of Store Brands: A Cross-Country Analysis of Consumer Store Brand Preferences, Perceptions, and Risk. Tülin Erdem* Performace of Store Brad: Cro-Coutry aly of Coumer Store Brad Preferece, Percepto, ad Rk Tül Erdem* Haa School of Bue Uverty of Calfora, Berkeley Berkeley, C 9470-1900 Tel: 510-64-463, Fax: 510 643 140

More information

Near Neighbor Distribution in Sets of Fractal Nature

Near Neighbor Distribution in Sets of Fractal Nature Iteratoal Joural of Computer Iformato Systems ad Idustral Maagemet Applcatos. ISS 250-7988 Volume 5 (202) 3 pp. 59-66 MIR Labs, www.mrlabs.et/jcsm/dex.html ear eghbor Dstrbuto Sets of Fractal ature Marcel

More information

TI-89, TI-92 Plus or Voyage 200 for Non-Business Statistics

TI-89, TI-92 Plus or Voyage 200 for Non-Business Statistics Chapter 3 TI-89, TI-9 Plu or Voyage 00 for No-Buie Statitic Eterig Data Pre [APPS], elect FlahApp the pre [ENTER]. Highlight Stat/Lit Editor the pre [ENTER]. Pre [ENTER] agai to elect the mai folder. (Note:

More information

3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS

3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS 3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS George Voelma ad Sader Dijkma Departmet of Geodey Delft Uiverity of Techology The Netherlad [email protected] KEY WORDS: Buildig

More information

Measuring the Quality of Credit Scoring Models

Measuring the Quality of Credit Scoring Models Measur the Qualty of Credt cor Models Mart Řezáč Dept. of Matheatcs ad tatstcs, Faculty of cece, Masaryk Uversty CCC XI, Edurh Auust 009 Cotet. Itroducto 3. Good/ad clet defto 4 3. Measur the qualty 6

More information

Methods and Data Analysis

Methods and Data Analysis Fudametal Numercal Methods ad Data Aalyss by George W. Colls, II George W. Colls, II Table of Cotets Lst of Fgures...v Lst of Tables... Preface... Notes to the Iteret Edto...v. Itroducto ad Fudametal Cocepts....

More information

Regression Analysis. 1. Introduction

Regression Analysis. 1. Introduction . Itroducto Regresso aalyss s a statstcal methodology that utlzes the relato betwee two or more quattatve varables so that oe varable ca be predcted from the other, or others. Ths methodology s wdely used

More information

Compressive Sensing over Strongly Connected Digraph and Its Application in Traffic Monitoring

Compressive Sensing over Strongly Connected Digraph and Its Application in Traffic Monitoring Compressve Sesg over Strogly Coected Dgraph ad Its Applcato Traffc Motorg Xao Q, Yogca Wag, Yuexua Wag, Lwe Xu Isttute for Iterdscplary Iformato Sceces, Tsghua Uversty, Bejg, Cha {qxao3, kyo.c}@gmal.com,

More information

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0 Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may

More information

Projection model for Computer Network Security Evaluation with interval-valued intuitionistic fuzzy information. Qingxiang Li

Projection model for Computer Network Security Evaluation with interval-valued intuitionistic fuzzy information. Qingxiang Li Iteratoal Joural of Scece Vol No7 05 ISSN: 83-4890 Proecto model for Computer Network Securty Evaluato wth terval-valued tutostc fuzzy formato Qgxag L School of Software Egeerg Chogqg Uversty of rts ad

More information

A Parallel Transmission Remote Backup System

A Parallel Transmission Remote Backup System 2012 2d Iteratoal Coferece o Idustral Techology ad Maagemet (ICITM 2012) IPCSIT vol 49 (2012) (2012) IACSIT Press, Sgapore DOI: 107763/IPCSIT2012V495 2 A Parallel Trasmsso Remote Backup System Che Yu College

More information

10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof [email protected] http://people.virginia.

10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof AnaNEvans@virginia.edu http://people.virginia. Math 40 Lecture 24 Autes Facal Mathematcs How ready do you feel for the quz o Frday: A) Brg t o B) I wll be by Frday C) I eed aother week D) I eed aother moth Aa NoraEvas 403 Kerchof [email protected] http://people.vrga.edu/~as5k/

More information

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK Fractal-Structured Karatsuba`s Algorthm for Bary Feld Multplcato: FK *The authors are worg at the Isttute of Mathematcs The Academy of Sceces of DPR Korea. **Address : U Jog dstrct Kwahadog Number Pyogyag

More information

AP Statistics 2006 Free-Response Questions Form B

AP Statistics 2006 Free-Response Questions Form B AP Statstcs 006 Free-Respose Questos Form B The College Board: Coectg Studets to College Success The College Board s a ot-for-proft membershp assocato whose msso s to coect studets to college success ad

More information

Polyphase Filters. Section 12.4 Porat 1/39

Polyphase Filters. Section 12.4 Porat 1/39 Polyphase Flters Secto.4 Porat /39 .4 Polyphase Flters Polyphase s a way of dog saplg-rate coverso that leads to very effcet pleetatos. But ore tha that, t leads to very geeral vewpots that are useful

More information

RUSSIAN ROULETTE AND PARTICLE SPLITTING

RUSSIAN ROULETTE AND PARTICLE SPLITTING RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate

More information

Reinsurance and the distribution of term insurance claims

Reinsurance and the distribution of term insurance claims Resurace ad the dstrbuto of term surace clams By Rchard Bruyel FIAA, FNZSA Preseted to the NZ Socety of Actuares Coferece Queestow - November 006 1 1 Itroducto Ths paper vestgates the effect of resurace

More information

Forecasting Trend and Stock Price with Adaptive Extended Kalman Filter Data Fusion

Forecasting Trend and Stock Price with Adaptive Extended Kalman Filter Data Fusion 2011 Iteratoal Coferece o Ecoomcs ad Face Research IPEDR vol.4 (2011 (2011 IACSIT Press, Sgapore Forecastg Tred ad Stoc Prce wth Adaptve Exteded alma Flter Data Fuso Betollah Abar Moghaddam Faculty of

More information

Sequences and Series

Sequences and Series Secto 9. Sequeces d Seres You c thk of sequece s fucto whose dom s the set of postve tegers. f ( ), f (), f (),... f ( ),... Defto of Sequece A fte sequece s fucto whose dom s the set of postve tegers.

More information

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,

More information

Statistical Techniques for Sampling and Monitoring Natural Resources

Statistical Techniques for Sampling and Monitoring Natural Resources Uted States Departmet of Agrculture Forest Servce Statstcal Techques for Samplg ad Motorg Natural Resources Rocky Mouta Research Stato Geeral Techcal Report RMRS-GTR-6 Has T. Schreuder, Rchard Erst, ad

More information

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve

More information

Lecture 7. Norms and Condition Numbers

Lecture 7. Norms and Condition Numbers Lecture 7 Norms ad Codto Numbers To dscuss the errors umerca probems vovg vectors, t s usefu to empo orms. Vector Norm O a vector space V, a orm s a fucto from V to the set of o-egatve reas that obes three

More information

How To Make A Supply Chain System Work

How To Make A Supply Chain System Work Iteratoal Joural of Iformato Techology ad Kowledge Maagemet July-December 200, Volume 2, No. 2, pp. 3-35 LATERAL TRANSHIPMENT-A TECHNIQUE FOR INVENTORY CONTROL IN MULTI RETAILER SUPPLY CHAIN SYSTEM Dharamvr

More information

FEDERATION OF ARAB SCIENTIFIC RESEARCH COUNCILS

FEDERATION OF ARAB SCIENTIFIC RESEARCH COUNCILS Aignment Report RP/98-983/5/0./03 Etablihment of cientific and technological information ervice for economic and ocial development FOR INTERNAL UE NOT FOR GENERAL DITRIBUTION FEDERATION OF ARAB CIENTIFIC

More information

A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS

A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS Kwe-Jay L Departmet of Electrcal Egeerg ad Computer Scece

More information

CSSE463: Image Recognition Day 27

CSSE463: Image Recognition Day 27 CSSE463: Image Recogto Da 27 Ths week Toda: Alcatos of PCA Suda ght: roject las ad relm work due Questos? Prcal Comoets Aalss weght grth c ( )( ) ( )( ( )( ) ) heght sze Gve a set of samles, fd the drecto(s)

More information