Predicting α-amylase yield and malt quality of some sprouting cereals using 2 nd order polynomial model

Size: px
Start display at page:

Download "Predicting α-amylase yield and malt quality of some sprouting cereals using 2 nd order polynomial model"

Transcription

1 frca Jural f Bchemstry Research Vl (8), pp 88-9, ugust, 9 valable le at ISSN cademc Jurals Full Legth Research Paper Predctg α-amylase yeld ad malt qualty f sme sprutg cereals usg d rder plymal mdel C Egwm Evas ad O dem Mday Bchemstry Departmet, Federal Uversty f Techlgy, P M B 65, Ma, Nger State, Ngera Departmet f Mathematcs, Statstcs ad Cmputer Studes, Federal Plytechc, Bda, Nger State, Ngera ccepted 7 July, 9 lpha amylase yeld sprutg Maze, cha, Rce ad Srghum were studed fr 8 h The result was aalyzed usg d rder plymal mdel The result shwed that the rate f - amylase secret wth grwth perd s sgfcatly hgh (p < 5) ad the R fr each raged frm 67-9%, whle the R fr sprutg vgur raged wth 99% fr all the cereals studed The predct fr amylase actvty frm sprutg vgur was sgfcat (p < 5) fr all the cereals studed, the R fr all the cereals raged betwee 6-9% The results cclude that α-amylase ad malt qualty ca be predcted sprutg cereals frm the grwth vgur Key wrds: Cereals, amylase, grwth vgur, mdel INTRODUCTION lpha amylase s sytheszed durg cereal develpmet ad stred matured edsperms (Evas, et al, ) lpha-amylase, as ther amylases crease markedly durg germat It has bee shw that alpha amylase yeld wll peak wth - days f cereal germat (Egwm ad Olyede, 6) Gerge-Kraemer et al () have shw that amylase actvty s a gd predctr f Dastatc Pwer (DP) whch s requred brewg prcesses ad a mprtat characterstc fr estmatg the qualty f malt fr beer prduct (Evas, et al, 995) Maltg frms a crtcal stage the prduct f cereal-based-beverages whch amylase ad prteases heretly embedded the cereal gra are actvated fr the purpse f hydrlyss f starch ad prte t sugars ad am acds respectvely (Okafr, 987) The determat f alpha-amylase yeld sprutg cereals s a rgrus e vlvg several stages f chemcal reacts ad calculats quck methd f predctg alpha-amylase yeld frm sprutg cereals s vestgated usg d rder plymal mdel The gal f predct s t determe ether the value f a ew bservat f the respse varable, r the values f a specfed prprt f all future bservats f the respse varable (wwwtstgv/dv898/hadbk) *Crrespdg authr E-mal: evaschd@gmalcm I ths paper, ths gal wll be acheved usg plymal mdel Plymals are partcularly mprtat the expermetal sceces sce they fte gve a smple theretcal descrpt f expermetal results (Eas et al, 989) MTERILS ND METHODS Cereals (Maze, cha, Rce ad Srghum) used were btaed frm Natal Cereal Research Isttute (NCRI) Badegg, Nger State, Ngera The cereals were spruted fr 8 h Cereal ascrspre was measured wth a meter rule whle alpha amylase actvty was assayed as reprted earler (Egwm ad Olyede, 6) Brefly, a alqut (ml) f crude ezyme was ppetted t clea test tube, ad 9ml f % starch slut was added ad cubated a shakg water bath at 5 C fr m The react was stpped by addg ml f DNS reaget ad bled fr m fr clur develpmet bsrbace was read at 55 M agast reaget blak Ezyme actvty was thereafter cmputed frm a stadard glucse curve ( mm glucse ml - ) The data mea was aalyzed usg the d rder plymal regress mdel MODEL SPECIFICTION The plymal respse mdel y β + β x + β x + β x + s ather example f lear mdel despte the fact that y s descrbed by lear fuct f the explaatry varable x (Evertt ad Du,

2 Egwm ad dem 89 99) plymal mdel may be a,,,, etc rder, ths preset study; a d rder plymal mdel was adpted gve by y β + β x + β x, where β The methd f least squares s used t estmate the mdel ceffcets The devats arud the regress le (- errr term) are assumed t be rmally ad depedetly dstrbuted wth mea f ad a stadard devat sgma whch des t deped x (wwwtstgv/dv 898/hadbk) The estmate f the mdel ceffcet s btaed frm the rmal equats: Y ˆ β + Y Y + + matrx was the frmed + ˆ + β + l et ˆ β () () () Y Y The determat methd ca be used t estmate the mdel parameters Y Y Y Y, Y Y The crrelat ceffcet ca be btaed usg the frmular belw r Y Y ( ( ) ) Y ( Y ) ( ) Whle the ceffcet f determat s gve as R (r) x % ad the k djusted ( ) x % R R The t statstc ca be used t test whether r t the ceffcet s sgfcatly dfferet frm The t-statstc s gve as: t c βˆ S ( ˆ ) β β The hypthess f terest H : Ceffcet equal verses H : Ceffcet t equal t c > t k We reject H f α Lastly, the F-test s used t test hw sutable s the mdel btaed It s gve as R /( k ) F c, F > F c 5,( k )( T k) where T s ( R ) /( T k) the umber f bservat, k s the umber f parameter estmated The mdel btaed s sutable fr frecastg f F > F (Omtsh, ) Fr ths study SPSS c 5,( k )( T k ) sftware, vers was emplyed fr plymal regress aalyss RESULTS ND DISCUSSION The summary f cereal acrspre vgur relat wth tme s shw Table The result shwed that tme was sgfcat t the acrspre vgur; ths mples that the grwth f acrspre creases sgfcatly wth tme The relatshp betwee vgur ad tme s lear as shw Fgure The degree f relatshp betwee acrspre vgur ad tme s very strg (crrelat (r) s wth 998) The R fr the relatshp s wth 99% fr all the cereals studes, whch further reveals that the mdels ca be used t predct future value f acrspre vgur fr ay kw tme The summary f alpha amylase yeld relat wth tme s shw Table The result shws that tme was sgfcat t alpha amylase yeld fr all cereals studed, that s, as tme creases, alpha-amylase yeld als crease It further shws that amylase yeld ca be predcted usg d rder plymal mdel sce all (t * * ) were sgfcat the mdels fr all the cereals studed, ths fdg s further expressed Fgure The

3 9 fr J Bchem Res Table Summary f Results the crspre Vgur wth Respect t Tme (Plymal Regress Mdel, ) Cereal crspre (mm) Rate f Icrease t Rate f Chage t** R R (djusted) r Maze cha Rce Srghum Table Summary f Results the mylase ctvty sprutg Cereal wth Respect t Tme (Plymal Regres-s Mdel, ) Surce f mylase Rate f Icrease t Rate f Chage t** R R (djusted) Maze-amy x cha-amy 5-6 x Rce-amy x Srghum-amy 7-86 x r CROSPIRE LENGTH (mm) MIZE CH RICE SORGHUM 5 5 TIME (Hrs) Fgure crspre vgur wth tme Mdel: Maze-6986* + 69t** - (75t**)* cha -685** + 99t** + (95t**)* Rce 68879* t** + (t**)* Srghum -78** + 55t** + (t**)* Remark: ** - Sgfcat, * - Nt Sgfcat at 5% level f Sgfcace MYLSE CTIVITY TIME(Hrs) Maze - my cha- my Rce- my Srghum- my Fgure mylase actvty sprutg cereals wth tme Mdel Maze-amy -6* + 9t** - (5667 x -5 t**)** cha-amy -65** + 5t** - (6 x -5 t**)** Rce-amy 95* + t** - (7578 x -6 t**)** Srghum-amy 76* + 7t** - (868 x - 5 t**)** Remark: ** - Sgfcat, * - Nt Sgfcat at 5% level f Sgfcace result als reveals that the degree f relatshp betwee amylase yeld ad tme s very strg (crrelat (r) rages frm 8-97) The R fr each cereal rages frm 67-9%, whch further reveals that the mdels ca be used t predct future value f amylase yeld fr ay kw tme The mplcat f ths fdg s qute terestg because t gve predctve frmat f the yeld f alpha amylase wth tme sprutg cereal The summary f alpha amylase yeld relat wth acrspre vgur s shw Table The result shws that amylase yeld s sgfcat wth acrspre vgur fr all the cereals studed, that s, as acrspre vgur creases, amylase yeld als crease The result further reveals that the degree f relatshp betwee amylase

4 Egwm ad dem 9 Table Summary Results the mylase ctvty Sprutg Wth Respect t crspre Vgur f the Cereals (Plymal Regress Mdel, ) Surce f amylase Rate f crease t Rate f chage t** R R (djusted) r Maze-amy x cha-amy x Rce-amy 9-67 x Srghum-amy x MYLSE CTVITY Maze-my cha-my Rce-my Srghum-my SCROSPIRE LENGTH (mm) Fgure mylase actvty wth acrspre legth Mdel Maze-amy -668* + 75Maze** - (958 x - 5 Maze**)** cha-amy 956** + 755cha** - (886 x - 5 cha**)** Rce-amy 869** + 9Rce** - (668 x - 5 Rce**)** Srghum-amy 56** + 59Srghum** - (985 x -6 Srghum**)** Remark: ** - Sgfcat, * - Nt Sgfcat at 5% level f Sgfcace yeld ad acrspre vgur s very strg (crrelat (r) rages frm 87 t 96) The predcts mdel fr amylase yeld frm acrspre vgur was sgfcat (p < 5) fr all the cereals studed, ad the result further reveals that d plymal mdel s very sutable fr mdelg amylase yeld frm acrspre vgur because the mdel relatg alpha amylase yeld wth acrspre vgur was sgfcat (p < 5) fr bth st ad d rder plymal mdels The d rder plymal mdel therefre wuld be a gd predctve mdel The R fr all the cereal studed rages betwee 6-9% whch reveal that the mdels btaed ca be used t predct future value f amylase yeld frm ay kw acrspre vgur the cereal studed (Fgure ) The preset fdg suggests that the d rder plymal mdel s sutable fr predctg amylase yeld sprutg cereals The mplcat f ths fdg s that the yeld f alpha-amylase s a gd dcatr f maltg qualty Ths fdg agrees wth the reprt f J- et al (6) wh have shw a strg relatshp betwee amylase actvty ad maltg qualty The preset wrk therefre cclude that t s pssble t predct alpha-amylase yeld as well as maltg qualty f sprutg cereals by measurg the acrspre vgur usg a d rder plymal mdel REFERENCES Eas G, Cles CW, Gettby G (989) Mathematcs ad Statstcs fr the B-Sc New Yrk: Jh Wley & Ss Egwm EC, Olyede OB (6) Cmpars f -mylase Yeld Sprutg Ngera Cereals Bchem 8(): 5- Evas DE, Va Weger B, Ma YF, Eght J () The Impact f the Thermstablty f -amylase, -amylase ad lmt Dextrase Ptetal wrk Fermetablty J m Scety f Brewg Chem 6: -8 Evas DE, Lace RCM, Elgt JK (995) The Ifluece f -mylase Isfrm Patter -mylase ctvty Barley ad Malt Prc 5 th ustr Cer Chem Cf delad pp 57-6 Evertt BS, Du G (99) ppled Multvarate Data alyss Great Brta: Edward rld Gerge-Kraemer JE, Mudstck EC, Cawall-Mha S () Develpmetal Express f mylase durg Barley Maltg J Cereal Sc : J- C, Fe D, Kag W, Gu-pg Z (6) Relatshp Betwee Malt Qualtes ad -amylase ctvtes ad Prte Ctet as ffected by Tmg f Ntrge fertlzer pplcat J Zhejag Uversty Sc B 7(): 79-8 Okafr N (987) Prcessg f Ngera Idgegeus Fds: Chace f Ivat, Ng Fd J : - Omtsh MY () Ecmetrcs Practcal pprach Ibada: Ysde Bk publshers

5 9 fr J Bchem Res ppedx: mylase actvty sprutg cereals (M glucse m - ) Tme(h) Maze-my cha-my Rce-my Srghum-my crspre vgur Tme(hrs) Maze cha Rce Srghum

The Simple Linear Regression Model: Theory

The Simple Linear Regression Model: Theory Chapter 3 The mple Lear Regress Mdel: Ther 3. The mdel 3.. The data bservats respse varable eplaatr varable : : Plttg the data.. Fgure 3.: Dsplag the cable data csdered b Che at al (993). There are 79

More information

Basics of heteroskedasticity

Basics of heteroskedasticity Sect 8 Heterskedastcty ascs f heterskedastcty We have assumed up t w ( ur SR ad MR assumpts) that the varace f the errr term was cstat acrss bservats Ths s urealstc may r mst ecmetrc applcats, especally

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Data Mg: cepts ad Techques 3 rd ed. hapter 10 1 Evaluat f lusterg lusterg evaluat assesses the feasblty f clusterg aalyss a data set ad the qualty f the results geerated by a clusterg methd. Three mar

More information

Goal of the Lecture. Lecture Structure. FWF 410: Analysis of Habitat Data I: Definitions and Descriptive Statistics

Goal of the Lecture. Lecture Structure. FWF 410: Analysis of Habitat Data I: Definitions and Descriptive Statistics FWF : Aalyss f Habtat Data I: Defts ad Descrptve tatstcs Number f Cveys A A B Bur Dsk Bur/Dsk Habtat Treatmet Matthew J. Gray, Ph.D. Cllege f Agrcultural ceces ad Natural Resurces Uversty f Teessee-Kvlle

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

ENGI 3423 Simple Linear Regression Page 12-01

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

More information

Analysis of Variance with Weibull Data

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

More information

MEASURES OF DISPERSION

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

More information

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y. .46. a. The frst varable (X) s the frst umber the par ad s plotted o the horzotal axs, whle the secod varable (Y) s the secod umber the par ad s plotted o the vertcal axs. The scatterplot s show the fgure

More information

Multiple Linear Regression Analysis

Multiple Linear Regression Analysis LINEA EGESSION ANALYSIS MODULE III Lecture - 4 Multple Lear egresso Aalyss Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Cofdece terval estmato The cofdece tervals multple

More information

Summary of the lecture in Biostatistics

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

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Lecture Notes Types of economic variables

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

More information

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

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

More information

Regression. Linear Regression. A Simple Data Display. A Batch of Data. The Mean is 220. A Value of 474. STAT Handout Module 15 1 st of June 2009

Regression. Linear Regression. A Simple Data Display. A Batch of Data. The Mean is 220. A Value of 474. STAT Handout Module 15 1 st of June 2009 STAT Hadout Module 5 st of Jue 9 Lear Regresso Regresso Joh D. Sork, M.D. Ph.D. Baltmore VA Medcal Ceter GRCC ad Uversty of Marylad School of Medce Claude D. Pepper Older Amercas Idepedece Ceter Reducg

More information

Simple Linear Regression

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

More information

: At least two means differ SST

: At least two means differ SST Formula Card for Eam 3 STA33 ANOVA F-Test: Completely Radomzed Desg ( total umber of observatos, k = Number of treatmets,& T = total for treatmet ) Step : Epress the Clam Step : The ypotheses: :... 0 A

More information

Quantitative analysis requires : sound knowledge of chemistry : possibility of interferences WHY do we need to use STATISTICS in Anal. Chem.?

Quantitative analysis requires : sound knowledge of chemistry : possibility of interferences WHY do we need to use STATISTICS in Anal. Chem.? Ch 4. Statstcs 4.1 Quattatve aalyss requres : soud kowledge of chemstry : possblty of terfereces WHY do we eed to use STATISTICS Aal. Chem.? ucertaty ests. wll we accept ucertaty always? f ot, from how

More information

Chapter 13 Student Lecture Notes 13-1

Chapter 13 Student Lecture Notes 13-1 Chapter 3 Studet Lecture Notes 3- Basc Busess Statstcs (9 th Edto) Chapter 3 Smple Lear Regresso 4 Pretce-Hall, Ic. Chap 3- Chapter Topcs Types of Regresso Models Determg the Smple Lear Regresso Equato

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

Linear Regression with One Regressor

Linear Regression with One Regressor Lear Regresso wth Oe Regressor AIM QA.7. Expla how regresso aalyss ecoometrcs measures the relatoshp betwee depedet ad depedet varables. A regresso aalyss has the goal of measurg how chages oe varable,

More information

PY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid

PY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid The Ide Ellpsd M.P. Vaugha Learg bjectves Wave prpagat astrpc meda Ptg walk-ff The de ellpsd Brefrgece 1 Wave prpagat astrpc meda The wave equat Relatve permttvt I E. Assumg free charges r currets E. Substtutg

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

Simulation Output Analysis

Simulation Output Analysis Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information

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

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

More information

Exergy Analysis of Large ME-TVC Desalination System

Exergy Analysis of Large ME-TVC Desalination System Exergy Aalyss f arge ME-V esalat System Awar O. Bamer Water & Eergy Prgram\Research rectrate Kuwat udat fr the Advacemet f Sceces (KAS) he 0 th Gulf Water ferece, -4 Aprl 0, ha- Qatar Outles Itrduct Prcess

More information

What regression does. so β. β β β

What regression does. so β. β β β Sect Smple Regress What regress des Relatshp Ofte ecmcs we beleve that there s a (perhaps causal) relatshp betwee tw varables Usually mre tha tw, but that s deferred t ather day Frm Is the relatshp lear?

More information

Econometric Methods. Review of Estimation

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

More information

arxiv:math/ v1 [math.gm] 8 Dec 2005

arxiv:math/ v1 [math.gm] 8 Dec 2005 arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

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

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

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

More information

Chapter 11 The Analysis of Variance

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

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL DISTRIBUTION

BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL DISTRIBUTION Iteratoal Joural of Mathematcs ad Statstcs Studes Vol.4, No.3, pp.5-39, Jue 06 Publshed by Europea Cetre for Research Trag ad Developmet UK (www.eajourals.org BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL

More information

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

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

More information

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn: Chapter 3 3- Busess Statstcs: A Frst Course Ffth Edto Chapter 2 Correlato ad Smple Lear Regresso Busess Statstcs: A Frst Course, 5e 29 Pretce-Hall, Ic. Chap 2- Learg Objectves I ths chapter, you lear:

More information

Bounds for the Connective Eccentric Index

Bounds for the Connective Eccentric Index It. J. Cotemp. Math. Sceces, Vol. 7, 0, o. 44, 6-66 Bouds for the Coectve Eccetrc Idex Nlaja De Departmet of Basc Scece, Humates ad Socal Scece (Mathematcs Calcutta Isttute of Egeerg ad Maagemet Kolkata,

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

Objectives of Multiple Regression

Objectives of Multiple Regression Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of

More information

Bootstrap Method for Testing of Equality of Several Coefficients of Variation

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

More information

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model 1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

Bayesian Inferences for Two Parameter Weibull Distribution Kipkoech W. Cheruiyot 1, Abel Ouko 2, Emily Kirimi 3

Bayesian Inferences for Two Parameter Weibull Distribution Kipkoech W. Cheruiyot 1, Abel Ouko 2, Emily Kirimi 3 IOSR Joural of Mathematcs IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume, Issue Ver. II Ja - Feb. 05, PP 4- www.osrjourals.org Bayesa Ifereces for Two Parameter Webull Dstrbuto Kpkoech W. Cheruyot, Abel

More information

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR Pot Patter Aalyss Part I Outle Revst IRP/CSR, frst- ad secod order effects What s pot patter aalyss (PPA)? Desty-based pot patter measures Dstace-based pot patter measures Revst IRP/CSR Equal probablty:

More information

CLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs

CLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs CLASS NOTES for PBAF 58: Quattatve Methods II SPRING 005 Istructor: Jea Swaso Dael J. Evas School of Publc Affars Uversty of Washgto Ackowledgemet: The structor wshes to thak Rachel Klet, Assstat Professor,

More information

Lecture 8: Linear Regression

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

More information

MATHEMATICAL PROGRAMMING-BASED PERTURBATION ANALYSIS FOR GI/G/1 QUEUES. He Zhang Wai Kin (Victor) Chan

MATHEMATICAL PROGRAMMING-BASED PERTURBATION ANALYSIS FOR GI/G/1 QUEUES. He Zhang Wai Kin (Victor) Chan Prceedgs f the 007 Wter Smulat Cferece S. G. Heders,. ller, M.-H. Hseh, J. Shrtle, J. D. ew, ad R. R. art, eds. MAHEMAICAL PROGRAMMING-ASED PERURAION ANALYSIS FOR GI/G/ QUEUES He Zhag Wa K (Vctr Cha Departmet

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67. Ecoomcs 3 Itroducto to Ecoometrcs Sprg 004 Professor Dobk Name Studet ID Frst Mdterm Exam You must aswer all the questos. The exam s closed book ad closed otes. You may use your calculators but please

More information

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s). CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The

More information

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Statistics MINITAB - Lab 5

Statistics MINITAB - Lab 5 Statstcs 10010 MINITAB - Lab 5 PART I: The Correlato Coeffcet Qute ofte statstcs we are preseted wth data that suggests that a lear relatoshp exsts betwee two varables. For example the plot below s of

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Byeong-Joo Lee

Byeong-Joo Lee yeg-j Lee OSECH - MSE calphad@pstech.ac.r yeg-j Lee www.pstech.ac.r/~calphad Fudametals Mcrscpc vs. Macrscpc Vew t State fuct vs. rcess varable Frst Law f hermdyamcs Specal prcesses 1. Cstat-Vlume rcess:

More information

Simple Linear Regression - Scalar Form

Simple Linear Regression - Scalar Form Smple Lear Regresso - Scalar Form Q.. Model Y X,..., p..a. Derve the ormal equatos that mmze Q. p..b. Solve for the ordary least squares estmators, p..c. Derve E, V, E, V, COV, p..d. Derve the mea ad varace

More information

Abstract. Introduction

Abstract. Introduction THE IMPACT OF USING LOG-ERROR CERS OUTSIDE THE DATA RANGE AND PING FACTOR By Dr. Shu-Pg Hu Teclte Research Ic. 566 Hllster Ave. Ste. 30 Sata Barbara, CA 93 Abstract Ths paper dscusses the prs ad cs f usg

More information

Continuous Distributions

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

More information

Statistics Descriptive and Inferential Statistics. Instructor: Daisuke Nagakura

Statistics Descriptive and Inferential Statistics. Instructor: Daisuke Nagakura Statstcs Descrptve ad Iferetal Statstcs Istructor: Dasuke Nagakura (agakura@z7.keo.jp) 1 Today s topc Today, I talk about two categores of statstcal aalyses, descrptve statstcs ad feretal statstcs, ad

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

LINEAR REGRESSION ANALYSIS

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

More information

(Monte Carlo) Resampling Technique in Validity Testing and Reliability Testing

(Monte Carlo) Resampling Technique in Validity Testing and Reliability Testing Iteratoal Joural of Computer Applcatos (0975 8887) (Mote Carlo) Resamplg Techque Valdty Testg ad Relablty Testg Ad Setawa Departmet of Mathematcs, Faculty of Scece ad Mathematcs, Satya Wacaa Chrsta Uversty

More information

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter LOGISTIC REGRESSION Notato Model Logstc regresso regresses a dchotomous depedet varable o a set of depedet varables. Several methods are mplemeted for selectg the depedet varables. The followg otato s

More information

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers

More information

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

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

More information

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

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

More information

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar

More information

The fuzzy decision of transformer economic operation

The fuzzy decision of transformer economic operation The fuzzy decs f trasfrmer ecmc perat WENJUN ZHNG, HOZHONG CHENG, HUGNG XIONG, DEXING JI Departmet f Electrcal Egeerg hagha Jatg Uversty 954 Huasha Rad, 3 hagha P. R. CHIN bstract: - Ths paper presets

More information

Correlation and Regression Analysis

Correlation and Regression Analysis Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the

More information

Bias Correction in Estimation of the Population Correlation Coefficient

Bias Correction in Estimation of the Population Correlation Coefficient Kasetsart J. (Nat. Sc.) 47 : 453-459 (3) Bas Correcto Estmato of the opulato Correlato Coeffcet Juthaphor Ssomboothog ABSTRACT A estmator of the populato correlato coeffcet of two varables for a bvarate

More information

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

Mean is only appropriate for interval or ratio scales, not ordinal or nominal. Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot

More information

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

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

More information

MECH6661 lectures 10/1 Dr. M. Medraj Mech. Eng. Dept. - Concordia University

MECH6661 lectures 10/1 Dr. M. Medraj Mech. Eng. Dept. - Concordia University Outle Revew ublattce Mdel Example ublattce Mdel Thermdyamc Mdelg Itrduct Example lly Desg Terary Phase Dagrams Gbbs Phase Rule Thermdyamcs f Multcmpet ystems Mech. Eg. Dept. - crda Uversty Revew: ublattce

More information

Dr. Shalabh. Indian Institute of Technology Kanpur

Dr. Shalabh. Indian Institute of Technology Kanpur Aalyss of Varace ad Desg of Expermets-I MODULE -I LECTURE - SOME RESULTS ON LINEAR ALGEBRA, MATRIX THEORY AND DISTRIBUTIONS Dr. Shalabh Departmet t of Mathematcs t ad Statstcs t t Ida Isttute of Techology

More information

Chapter 8: Statistical Analysis of Simulated Data

Chapter 8: Statistical Analysis of Simulated Data Marquette Uversty MSCS600 Chapter 8: Statstcal Aalyss of Smulated Data Dael B. Rowe, Ph.D. Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 08 by Marquette Uversty MSCS600 Ageda 8. The Sample

More information

1. BLAST (Karlin Altschul) Statistics

1. BLAST (Karlin Altschul) Statistics Parwse seuece algmet global ad local Multple seuece algmet Substtuto matrces Database searchg global local BLAST Seuece statstcs Evolutoary tree recostructo Gee Fdg Prote structure predcto RNA structure

More information

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018 /3/08 Sstems & Bomedcal Egeerg Departmet SBE 304: Bo-Statstcs Smple Lear Regresso ad Correlato Dr. Ama Eldeb Fall 07 Descrptve Orgasg, summarsg & descrbg data Statstcs Correlatoal Relatoshps Iferetal Geeralsg

More information

Evaluation of uncertainty in measurements

Evaluation of uncertainty in measurements Evaluato of ucertaty measuremets Laboratory of Physcs I Faculty of Physcs Warsaw Uversty of Techology Warszawa, 05 Itroducto The am of the measuremet s to determe the measured value. Thus, the measuremet

More information

STRESS TRANSFER IN CARBON NANOTUBE REINFORCED POLYMER COMPOSITES

STRESS TRANSFER IN CARBON NANOTUBE REINFORCED POLYMER COMPOSITES STESS TANSFE IN CABON NANOTUBE EINFOCED POLYME COMPOSITES A. Haque ad A. aasetty Departet f Aerspace Egeerg ad Mechacs, The Uversty f Alabaa, Tuscalsa, AL 35487, USA ABSTACT A aalytcal del has bee develped

More information

Simple Linear Regression and Correlation. Applied Statistics and Probability for Engineers. Chapter 11 Simple Linear Regression and Correlation

Simple Linear Regression and Correlation. Applied Statistics and Probability for Engineers. Chapter 11 Simple Linear Regression and Correlation 4//6 Appled Statstcs ad Probablty for Egeers Sth Edto Douglas C. Motgomery George C. Ruger Chapter Smple Lear Regresso ad Correlato CHAPTER OUTLINE Smple Lear Regresso ad Correlato - Emprcal Models -8

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

PROPERTIES OF GOOD ESTIMATORS

PROPERTIES OF GOOD ESTIMATORS ESTIMATION INTRODUCTION Estmato s the statstcal process of fdg a appromate value for a populato parameter. A populato parameter s a characterstc of the dstrbuto of a populato such as the populato mea,

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

ln( weekly earn) age age

ln( weekly earn) age age Problem Set 4, ECON 3033 (Due at the start of class, Wedesday, February 4, 04) (Questos marked wth a * are old test questos) Bll Evas Sprg 08. Cosder a multvarate regresso model of the form y 0 x x. Wrte

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

Application of Matrix Iteration for Determining the Fundamental Frequency of Vibration of a Continuous Beam

Application of Matrix Iteration for Determining the Fundamental Frequency of Vibration of a Continuous Beam Iteratal Jural f Egeerg Research ad Develpet e-issn: 78-67, p-issn : 78-8, www.jerd.c Vlue 4, Issue (Nveber ), PP. -6 Applcat f Matrx Iterat fr Deterg the Fudaetal Frequecy f Vbrat f a Ctuus Bea S. Sule,

More information

Extend the Borel-Cantelli Lemma to Sequences of. Non-Independent Random Variables

Extend the Borel-Cantelli Lemma to Sequences of. Non-Independent Random Variables ppled Mathematcal Sceces, Vol 4, 00, o 3, 637-64 xted the Borel-Catell Lemma to Sequeces of No-Idepedet Radom Varables olah Der Departmet of Statstc, Scece ad Research Campus zad Uversty of Tehra-Ira der53@gmalcom

More information

Francis Galton ( ) The Inventor of Modern Regression Analysis

Francis Galton ( ) The Inventor of Modern Regression Analysis Decrptve Stattc The Cure S Far: Prbablty Thery Prbablty Dtrbut Samplg Dtrbut Smple Radm Samplg Symbl f Caual Aaly: Crcle repreetg theretcal (r latet) varable that may be caue r effect (r bth) ur thery.

More information

CHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol

CHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol CAPER 5 ENROPY GENERAION Istructr: Pr. Dr. Uğur Atkl Chapter 5 Etrpy Geerat (Exergy Destruct Outle st Avalable rk Cycles eat ege cycles Rergerat cycles eat pump cycles Nlw Prcesses teady-flw Prcesses Exergy

More information

VOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved.

VOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved. VOL., NO., November 0 ISSN 5-77 ARPN Joural of Scece ad Techology 0-0. All rghts reserved. http://www.ejouralofscece.org Usg Square-Root Iverted Gamma Dstrbuto as Pror to Draw Iferece o the Raylegh Dstrbuto

More information

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..

More information