Quantitative Assessment on Fitting of Gumbel and Frechet Distributions for Extreme Value Analysis of Rainfall

Size: px
Start display at page:

Download "Quantitative Assessment on Fitting of Gumbel and Frechet Distributions for Extreme Value Analysis of Rainfall"

Transcription

1 015 IJSRST Volume 1 Issue Prt ISSN: Ole ISSN: X Themed Secto: Scece Quattatve Assessmet o Fttg of umbel ad Frechet Dstrbutos for Extreme Value Aalyss of Rafall ABSTRACT N. Vvekaada Cetral Water ad Power Research Stato, Pue, aharashtra, Ida Rafall frequecy aalyss plays a mportat role hydrologc ad ecoomc evaluato of water resources projects. It helps to estmate the retur perods ad ther correspodg evet magtudes thereby creatg reasoable desg crtera. Depedg o the sze, lfe tme ad desg crtera of the structure, dfferet retur perods are geerally stpulated for adoptg Extreme Value Aalyss (EVA) results. Ths paper llustrates the use of quattatve assessmet o fttg of umbel (EV1) ad Frechet (EV) probablty dstrbutos to the seres of aual 1-day maxmum rafall (AR) data usg oodess-of-ft (of) ad dagostc tests. Order Statstcs Approach (OSA) s used for determato of parameters of the dstrbutos. Based o of (usg Aderso- Darlg ad Kolmogorov-Smrov) ad dagostc (usg D-dex) test results, the study detfes the EV1 dstrbuto s better suted for EVA of rafall for Fatehabad ad Hssar. Keywords: Aderso-Darlg, D-Idex, Frechet, umbel, Kolmogorov-Smrov, Rafall I. INTRODUCTION Estmato of rafall for a desred retur perod s a prerequste for plag, desg ad operato of varous hydraulc structures such as dams, brdges, barrages ad storm water draage systems. Depedg o the sze, lfe tme ad desg crtera of the structure, dfferet retur perods are geerally stpulated for adoptg Extreme Value Aalyss (EVA) results. For arrvg at such desg values, a stadard procedure s to aalyse hstorcal aual 1-day maxmum rafall (AR) data over a perod of tme (yr) ad arrve at statstcal estmates. I probablstc theory, the Extreme Value Dstrbutos (EVDs) clude eeralsed Extreme Value (EV), umbel (EV1), Frechet (EV) ad Webull (EV3) s geerally adopted for EVA of rafall [1-3]. EVDs arse as lmtg dstrbutos for the sample of depedet, detcally dstrbuted radom varables, as the sample sze creases. Out of umber of parameter estmato methods, Order Statstcs Approach (OSA) s appled for determato of dstrbutoal parameters because of the OSA estmators are havg mmum varace. I ths paper, EV ad EV3 dstrbutos are ot cosdered for EVA of rafall due to o-exstece of OSA for determato of dstrbutoal parameters. Number of studes carred out dfferet researchers llustrated that there s o uque dstrbuto s avalable for EVA of rafall for a rego or coutry [4-10]. Ths apart, whe dfferet dstrbutos are used for estmato of rafall, a commo problem s ecoutered as regards the ssue of best model fts for a gve set of data. Ths ca be aswered by quattatve assessmet usg oodess-of-ft (of) ad dagostc tests; ad the results are quatfable ad relable [11]. For quattatve assessmet o rafall wth the recorded rage, Aderso-Darlg (A ) ad Kolmogorov-Smrov (KS) tests are appled for checkg the adequacy of fttg of EV1 ad EV dstrbutos to the seres of AR data. A dagostc test of D-dex s used for the selecto of sutable probablty dstrbuto for estmato of rafall. I ths paper, quattatve assessmet o fttg of EV1 ad EV probablty dstrbutos s made to detfy the best sutable dstrbuto for estmato of rafall for Fatehabad ad Hssar. IJSRST1514 Receved: 5 Jue 015 Accepted: 30 Jue 015 ay-jue 015 [(1): 68-73] 68

2 II. ETHODS AND ATERIALS The Cumulatve Dstrbuto Fuctos (CDFs) of EV1 ad EV dstrbutos are expressed by: R (R) e e F,, >0 (for EV1) (1) F(R) ( F RF ) e F, F, F >0 (for EV) () Here, ad are the locato ad scale parameters of EV1 dstrbuto. The rafall estmates (R ) adoptg EV1 dstrbuto are computed from R α Y β wth l( l(1 (1/T))). T Smlarly, F ad F are the scale ad shape parameters of EV dstrbuto. Based o extreme value theory, EV dstrbuto ca be trasformed to EV1 dstrbuto through logarthmc trasformato. Uder ths trasformato, the rafall estmates (R F ) adoptg EV dstrbuto are computed from R F Exp(R ), βf Exp(α ) ad λf 1/ β [1]. Theoretcal Descrptos of OSA OSA s based o the assumpto that the set of extreme values costtutes a statstcally depedet seres of observatos. The parameters of EV1 dstrbuto are gve by: α α Y T r r α ; β r β r β (3) where r ad r are proportoalty factors, whch ca be obtaed from the selected values of k, ad usg the relatos r k/ N ad r / N. Here, N s the sample sze of the basc data that are dvded to k sub groups of elemets each leavg remaders; ad N ca be wrtte the form of N=k+. I OSA, α ad β are the dstrbuto parameters of the groups ad α ad β are the parameters of the remaders, f ay. These ca be computed from the followg equatos: α β where 1 k α S 1 1 kβ S 1 k S R j, 1 ad α ad β α R (4) 1 1 β R (5) j=1,,3,..,. Here, R s the th observato the remader group havg elemets, R j s the th observato the j th group havg elemets. Table 1 gves the weghts of α ad β used determato of parameters of the dstrbutos [13]. The parameters are further used to estmate the rafall for dfferet retur perods. The Stadard Error (SE) o the estmated rafall s computed by: SE [Var(R )] r 1 k k N T 1/ ad ad r N Var(R ) r R T r R (6) Here, R ad R are defed by the geeral form R A Y B Y C as T T β. Here R T deotes the estmated rafall by ether R or R F. The values of A, B, ad C are gve Table. TABLE 1 WEIHTS OF AND FOR COPUTATION OF DISTRIBUTIONAL PARAETERS TABLE VARIANCE DETERINATORS FOR R N A B C oodess-of-ft Tests The adequacy of fttg of probablty dstrbutos to the seres of recorded AR s evaluated by quattatve assessmet usg of tests statstc. Theoretcal descrpto of A test statstc s as follows: N ( 1) l(z ) A N 1 N (7) 1 N 1 l(1 Z ) Here Z F(R ) for =1,,3,,N wth R 1<R <..<R N, F(R ) s the CDF of th sample ( R ) ad N s the sample sze. The theoretcal value ( A ) of A statstc for dfferet sample sze (N) at 5% percet sgfcace level s computed from 0.757(1 (0./ N). A C C Iteratoal Joural of Scetfc Research Scece ad Techology ( 69

3 The KS statstc s defed by: N KS ax ( F (R ) F (R )) 1 e D (8) F s Here F e X s the emprcal CDF of X ad D X the computed CDF of X (Zhag, 00). The theoretcal value KS statstc for dfferet sample sze (N) at 5% sgfcace level s avalable the techcal ote o oodess-of-ft Tests for Statstcal Dstrbutos book [14]. Test crtera: If the computed values of of tests statstc gve by probablty dstrbuto are less tha that of theoretcal values at the desred sgfcace level the the dstrbuto s cosdered to be acceptable for EVA of rafall at that level. Dagostc Test The selecto of a sutable probablty dstrbuto for EVA of rafall s performed through D-dex test [1], whch s defed as below: 6 D-dex = 1 R R R (9) 1 Here, R s the average value of the recorded data whereas R ad R are the hghest recorded ad correspodg estmated values by EV1 ad EV. The dstrbuto havg the least D-dex s cosdered as better suted dstrbuto for rafall estmato [15]. III. APPLICATION I ths paper, a study was carred out to estmate the rafall for dfferet retur perods for Fatehabad ad Hssar adoptg EV1 ad EV dstrbutos (usg OSA). Daly rafall data recorded at Fatehabad for the perod 1954 to 011 ad Hssar for the perod 1969 to 011 was used. From the scruty of the daly rafall data, t was observed that the data for the termttet perod for Fatehabad ad Has (1966 ad 1967) ad Hssar (00) are mssg. So, the AR for the mssg years were mputed by the seres maxmum value of 140 mm (for Fatehabad) ad 56.5 mm (for Hssar) accordace wth Atomc Eergy Regulatory Board gudeles ad used for EVA. Table 3 gves the descrptve statstcs of AR recorded at Fatehabad ad Hssar. Rego TABLE 3 DESCRIPTIVE STATISTICS OF AR Descrptve statstcs R (mm) SD (mm) Skewess Kurtoss Fatehabad Hssar SD: Stadard Devato IV. RESULTS AND DISCUSSIONS By applyg the procedures as descrbed above, a computer program was developed ad used to ft the AR recorded at Fatehabad ad Hssar. The program computes the rafall estmates for dfferet retur perods adoptg EV1 ad EV dstrbutos (usg OSA), of tests statstc ad D-dex values. Table 4 gves the rafall estmates (ER) together wth Stadard Error (SE) adoptg EV1 ad EV dstrbutos for the statos uder study. From Table 4, t may be oted that the estmated rafall by EV dstrbuto s relatvely hgher tha the correspodg values of EV1 for Fatehabad ad Hssar. Iteratoal Joural of Scetfc Research Scece ad Techology ( 70

4 TABLE 4 ESTIATED RAINFALL WITH STANDARD ERROR ADOPTIN EV1 AND EV DISTRIBUTIONS (USIN OSA) FOR FATEHABAD AND HISSAR Retur Estmated rafall (mm) wth stadard error (mm) for perod Fatehabad Hssar (yr) EV1 EV EV1 EV ER SE ER SE ER SE ER SE Aalyss Based o of Tests For quattatve assessmet o fttg of EV1 ad EV dstrbutos to the recorded AR data, of tests statstc values were computed from Eqs. (7) ad (8), ad gve Table 5. TABLE 5 COPUTED AND THEORETICAL VALUES OF OF TESTS STATISTIC Rego A KS Computed values Theoretcal value at Computed values Theoretcal value at EV1 EV 5% level EV1 EV 5% level Fatehabad Hssar From the of tests results gve Table 5, t may be oted that the KS test cofrmed the use of EV1 ad EV dstrbutos (usg OSA) for EVA of rafall (Fatehabad ad Hssar). Smlarly, A test cofrmed the use of EV1 dstrbuto for EVA of rafall for Fatehabad. As regards EVA of rafall for Hssar, A test suggested the EV1 ad EV dstrbutos were ot acceptable. Aalyss Based o Dagostc Test For the selecto of a sutable probablty dstrbuto, D- dex values of EV1 ad EV dstrbutos are computed from Eq. (9) ad gve Table 6. From the results, t may be oted that the D-dex values of EV1 dstrbuto are mmum whe compared wth the correspodg values of EV for the statos uder study. TABLE 6 D-INDEX VALUES OF EV1 AND EV Rego D-dex EV1 EV Fatehabad Hssar Based o quattatve assessmet usg of ad dagostc tests, the study showed that the EV1 dstrbuto s better suted for estmato of rafall for Fatehabad ad Hssar. Fgures 1 ad gve the plots of recorded ad estmated rafall usg EV1 (OSA) wth cofdece lmts at percetage level for Fatehabad ad Hssar. Iteratoal Joural of Scetfc Research Scece ad Techology ( 71

5 (where ea deotes the estmated rafall ad SE the Stadard Error) values of about 75 mm (for Fatehabad) ad 48 mm (for Hssar) computed from EV1 (OSA) dstrbuto were suggested for desg purposes. ACKNOWLEDEENTS The author s grateful to the Drector, Cetral Water ad Power Research Stato, Pue, for provdg the research facltes to carry out the study. The author s thakful to /s Nuclear Power Corporato of Ida Lmted, umba ad Ida eteorologcal Departmet, Pue, for supply of rafall data. FIURE 1: RECORDED AND ESTIATED 1-DAY AXIU RAINFALL USIN EV1 (OSA) DISTRIBUTION WITH PERCENT LOWER AND UPPER CONFIDENCE LIITS FOR FATEHABAD FIURE : RECORDED AND ESTIATED 1-DAY AXIU RAINFALL USIN EV1 (OSA) DISTRIBUTION WITH PERCENT LOWER AND UPPER CONFIDENCE LIITS FOR HISSAR V. CONCLUSIONS The paper preseted the procedures volved quattatve assessmet o fttg of EV1 ad EV dstrbutos (usg OSA) for EVA of rafall for Fatehabad ad Hssar. The KS test results cofrmed the fttg of EV1 ad EV dstrbutos to the seres of AR recorded at the statos uder study. The A test results suggested the use of EV1 dstrbuto for EVA of rafall for Fatehabad. The dagostc aalyss showed that the EV1 dstrbuto s better suted for estmato of rafall for Fatehabad ad Hssar. By cosderg the desg-lfe of the structure over the etre teded ecoomc lfetme, the yr retur perod ea+se REFERENCES [1] E.J. umbel, Statstc of Extremes, d Edto, Columba Uversty Press, New York, [] J.A. Carta ad P. Ramrez, Aalyss of twocompoet mxture Webull statstcs for estmato of wd speed dstrbutos, Joural of Reewable Eergy, Vol. 3, No.3, pp , 007. [3] N. ujere, Flood frequecy aalyss usg the umbel dstrbuto, Joural of Computer Scece ad Egeerg, Vol. 3, No. 7, pp , 011. [4].C. Casas, R. Rodrguez,. Prohom, A. azquez, ad A. Redao, Estmato of the probable maxmum precptato Barceloa (Spa), Joural of Clmatology, Vol. 31, No. 9, pp , 011. [5] E. Baratt, A. otaar, A. Castellar, J.L. Salas, A. Vgloe, ad A. Bezz, Estmatg the flood frequecy dstrbuto at seasoal ad aual tme scales, Hydrologcal Earth System Scece, Vol. 16, No. 1, pp , 01. [6] A. Peck, P. Prodaovc ad S.P. Smoovc, Rafall testy durato frequecy curves uder clmate chage: cty of Lodo, Otaro, Caada, Caada Water Resources Joural, Vol. 37, No. 3, pp , 01. [7] L.S. Esteves, Cosequeces to flood maagemet of usg dfferet probablty dstrbutos to estmate extreme rafall, Joural of Evrometal aagemet, Vol. 115, No. 1, pp , 013. [8] B.A. Olumde,. Sadu, ad A. Oluwasesa, Evaluato of best ft probablty dstrbuto Iteratoal Joural of Scetfc Research Scece ad Techology ( 7

6 models for the predcto of rafall ad ruoff volume (Case Study Tagwa Dam, a- Ngera), Joural of Egeerg ad Techology, Vo1. 3, No., pp , 013. [9] V. Rahma, S.L. Hutchso, J..S. Hutchso ad A. Aadh, Extreme daly rafall evet dstrbuto patters Kasas, Joural of Hydrologc Egeerg, Vol. 19, No. 4, pp , 014. [10] Zhalg L, Zhaje L, We Zhao, ad Yuehua Wag, Probablty odelg of Precptato Extremes over Two Rver Bass Northwest of Cha, Advaces eteorology, Vol. 13, Artcle ID 37417, pp. 1-13, 015. [11] J. Zhag, Powerful goodess-of-ft tests based o the lkelhood rato, Joural of Royal Statstcal Socety, Vol. 64, No., pp , 00. [1] A.H. Ag, ad W.H. Tag, Probablty cocepts egeerg plag ad desg, Vol., Joh Wley & Sos, [13] Atomc Eergy Regulatory Board (AERB), Extreme values of meteorologcal parameters, AERB Safety ude No. NF/S/ S-3, 008. [14] P.E. Charles As, oodess-of-ft Tests for Statstcal Dstrbutos, [ egeerg.com/goodess.html], 009. [15] Uted States Water Resources Coucl (USWRC), udeles for determg flood flow frequecy, Bullet No. 17B, Iteratoal Joural of Scetfc Research Scece ad Techology ( 73

Rainfall Frequency Analysis using Order Statistics Approach of Extreme Value Distributions

Rainfall Frequency Analysis using Order Statistics Approach of Extreme Value Distributions SSRG Iteratoal Joural of Cvl Egeerg (SSRG-IJCE) volume 1 ssue 4 September 014 Rafall requecy Aalyss usg Order Statstcs Approach of Extreme Value Dstrbutos N Vvekaada Cetral Water ad Power Research Stato,

More information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

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

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

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

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

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

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

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

Songklanakarin Journal of Science and Technology SJST R2 Khamkong

Songklanakarin Journal of Science and Technology SJST R2 Khamkong Sogklaakar Joural of Scece ad Techology SJST-0-0.R Khamkog A Modfed Box ad Cox Power Trasformato to Determe the Stadardzed Precptato Idex For Revew Oly Joural: Sogklaakar Joural of Scece ad Techology Mauscrpt

More information

Confidence Intervals for Double Exponential Distribution: A Simulation Approach

Confidence Intervals for Double Exponential Distribution: A Simulation Approach World Academy of Scece, Egeerg ad Techology Iteratoal Joural of Physcal ad Mathematcal Sceces Vol:6, No:, 0 Cofdece Itervals for Double Expoetal Dstrbuto: A Smulato Approach M. Alrasheed * Iteratoal Scece

More information

The Generalized Inverted Generalized Exponential Distribution with an Application to a Censored Data

The Generalized Inverted Generalized Exponential Distribution with an Application to a Censored Data J. Stat. Appl. Pro. 4, No. 2, 223-230 2015 223 Joural of Statstcs Applcatos & Probablty A Iteratoal Joural http://dx.do.org/10.12785/jsap/040204 The Geeralzed Iverted Geeralzed Expoetal Dstrbuto wth a

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

IJOART. Copyright 2014 SciResPub.

IJOART. Copyright 2014 SciResPub. Iteratoal Joural of Advacemets Research & Techology, Volume 3, Issue 10, October -014 58 Usg webull dstrbuto the forecastg by applyg o real data of the umber of traffc accdets sulama durg the perod (010-013)

More information

Comparison of Parameters of Lognormal Distribution Based On the Classical and Posterior Estimates

Comparison of Parameters of Lognormal Distribution Based On the Classical and Posterior Estimates Joural of Moder Appled Statstcal Methods Volume Issue Artcle 8 --03 Comparso of Parameters of Logormal Dstrbuto Based O the Classcal ad Posteror Estmates Raja Sulta Uversty of Kashmr, Sragar, Ida, hamzasulta8@yahoo.com

More information

Flood Frequency Analysis of Ikpoba River Catchment at Benin City Using Log Pearson Type III Distribution

Flood Frequency Analysis of Ikpoba River Catchment at Benin City Using Log Pearson Type III Distribution Joural of Emergg Treds Egeerg ad Appled Sceces (JETEAS) (): 50-55 Scholarlk Research Isttute Jourals, 0 (ISSN: 4-706) jeteas.scholarlkresearch.org Flood Frequecy Aalyss of Ikpoba Rver Catchmet at Be Cty

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

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

More information

Special Instructions / Useful Data

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

More information

A new Family of Distributions Using the pdf of the. rth Order Statistic from Independent Non- Identically Distributed Random Variables

A new Family of Distributions Using the pdf of the. rth Order Statistic from Independent Non- Identically Distributed Random Variables Iteratoal Joural of Cotemporary Mathematcal Sceces Vol. 07 o. 8 9-05 HIKARI Ltd www.m-hkar.com https://do.org/0.988/jcms.07.799 A ew Famly of Dstrbutos Usg the pdf of the rth Order Statstc from Idepedet

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

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud

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

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

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

Lecture 1 Review of Fundamental Statistical Concepts

Lecture 1 Review of Fundamental Statistical Concepts Lecture Revew of Fudametal Statstcal Cocepts Measures of Cetral Tedecy ad Dsperso A word about otato for ths class: Idvduals a populato are desgated, where the dex rages from to N, ad N s the total umber

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

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

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

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

More information

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

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions. It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A

More information

Estimation of the Loss and Risk Functions of Parameter of Maxwell Distribution

Estimation of the Loss and Risk Functions of Parameter of Maxwell Distribution Scece Joural of Appled Mathematcs ad Statstcs 06; 4(4): 9- http://www.scecepublshggroup.com/j/sjams do: 0.648/j.sjams.060404. ISSN: 76-949 (Prt); ISSN: 76-95 (Ole) Estmato of the Loss ad Rsk Fuctos of

More information

Class 13,14 June 17, 19, 2015

Class 13,14 June 17, 19, 2015 Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral

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

LECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR

LECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR amplg Theory MODULE II LECTURE - 4 IMPLE RADOM AMPLIG DR. HALABH DEPARTMET OF MATHEMATIC AD TATITIC IDIA ITITUTE OF TECHOLOGY KAPUR Estmato of populato mea ad populato varace Oe of the ma objectves after

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

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

Estimation and Testing in Type-II Generalized Half Logistic Distribution

Estimation and Testing in Type-II Generalized Half Logistic Distribution Joural of Moder Appled Statstcal Methods Volume 13 Issue 1 Artcle 17 5-1-014 Estmato ad Testg Type-II Geeralzed Half Logstc Dstrbuto R R. L. Katam Acharya Nagarjua Uversty, Ida, katam.rrl@gmal.com V Ramakrsha

More information

Some Notes on the Probability Space of Statistical Surveys

Some Notes on the Probability Space of Statistical Surveys Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty

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

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

Some Statistical Inferences on the Records Weibull Distribution Using Shannon Entropy and Renyi Entropy

Some Statistical Inferences on the Records Weibull Distribution Using Shannon Entropy and Renyi Entropy OPEN ACCESS Coferece Proceedgs Paper Etropy www.scforum.et/coferece/ecea- Some Statstcal Ifereces o the Records Webull Dstrbuto Usg Shao Etropy ad Rey Etropy Gholamhosse Yar, Rezva Rezae * School of Mathematcs,

More information

A Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies

A Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies ISSN 1684-8403 Joural of Statstcs Volume 15, 008, pp. 44-53 Abstract A Combato of Adaptve ad Le Itercept Samplg Applcable Agrcultural ad Evrometal Studes Azmer Kha 1 A adaptve procedure s descrbed for

More information

Journal of Water and Soil Vol. 26, No. 1, Mar-Apr 2012, p Kriging. (

Journal of Water and Soil Vol. 26, No. 1, Mar-Apr 2012, p Kriging. ( Joural of Water ad Sol Vol. 26, No. 1, Mar-Apr 212, p. 53-64 ( ) 53-64. 1391 1 26 *2 1-89/7/26: 9/8/15:.. (1386-87 1361-62) 26 32.. (GPI) (IDW). (Co-Krgg) (Krgg) (RBF) (LPI) 64/46 6/49 77/2 66/86 IDW RBF.

More information

A NEW GENERALIZATION OF ERLANG DISTRIBUTION WITH BAYES ESTIMATION

A NEW GENERALIZATION OF ERLANG DISTRIBUTION WITH BAYES ESTIMATION Iteratoal Joural of Iovatve Research ad Revew ISSN: 347 444 Ole Ole Iteratoal Joural valable at http://www.cbtech.org/jrr.htm 06 Vol. 4 prl-jue pp.4-9/bhat et al. Research rtcle NEW GENERLIZTION OF ERLNG

More information

Goodness of Fit Test for The Skew-T Distribution

Goodness of Fit Test for The Skew-T Distribution Joural of mathematcs ad computer scece 4 (5) 74-83 Artcle hstory: Receved ecember 4 Accepted 6 Jauary 5 Avalable ole 7 Jauary 5 Goodess of Ft Test for The Skew-T strbuto M. Magham * M. Bahram + epartmet

More information

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study IJIEST Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue 5, July 04. Bayes Iterval Estmato for bomal proporto ad dfferece of two bomal proportos wth Smulato Study Masoud Gaj, Solmaz hlmad

More information

4. Standard Regression Model and Spatial Dependence Tests

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

More information

= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality

= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality UCLA STAT Itroducto to Statstcal Methods for the Lfe ad Health Sceces Istructor: Ivo Dov, Asst. Prof. of Statstcs ad Neurology Teachg Assstats: Fred Phoa, Krste Johso, Mg Zheg & Matlda Hseh Uversty of

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

Chapter 8. Inferences about More Than Two Population Central Values

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

More information

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

Study of Correlation using Bayes Approach under bivariate Distributions

Study of Correlation using Bayes Approach under bivariate Distributions Iteratoal Joural of Scece Egeerg ad Techolog Research IJSETR Volume Issue Februar 4 Stud of Correlato usg Baes Approach uder bvarate Dstrbutos N.S.Padharkar* ad. M.N.Deshpade** *Govt.Vdarbha Isttute of

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

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

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

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

Module 7. Lecture 7: Statistical parameter estimation

Module 7. Lecture 7: Statistical parameter estimation Lecture 7: Statstcal parameter estmato Parameter Estmato Methods of Parameter Estmato 1) Method of Matchg Pots ) Method of Momets 3) Mamum Lkelhood method Populato Parameter Sample Parameter Ubased estmato

More information

ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN EXTENDED LOG-LOGISTIC DISTRIBUTION FROM PROGRESSIVELY CENSORED SAMPLES

ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN EXTENDED LOG-LOGISTIC DISTRIBUTION FROM PROGRESSIVELY CENSORED SAMPLES ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN EXTENDED LOG-LOGISTIC DISTRIBUTION FROM PROGRESSIVELY CENSORED SAMPLES Mahmoud Rad Mahmoud Isttute of Statstcs, Caro Uversty Suza Mahmoud Mohammed Faculty

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

Predicting CO Concentrations Levels Using Probability Distributions

Predicting CO Concentrations Levels Using Probability Distributions Iteratoal Joural of Egeerg ad Techology Volume 3 o. 9, September, 03 Predctg CO Cocetratos Levels Usg Probablty Dstrbutos. A. S. Yahaya,.A. Raml, A.Z Ul-Saufe, H. A. Hamd, H. Ahmat, Z.A Mohtar School of

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

To use adaptive cluster sampling we must first make some definitions of the sampling universe:

To use adaptive cluster sampling we must first make some definitions of the sampling universe: 8.3 ADAPTIVE SAMPLING Most of the methods dscussed samplg theory are lmted to samplg desgs hch the selecto of the samples ca be doe before the survey, so that oe of the decsos about samplg deped ay ay

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

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

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 Statistics Background of Regression Analysis

Chapter Statistics Background of Regression Analysis Chapter 06.0 Statstcs Backgroud of Regresso Aalyss After readg ths chapter, you should be able to:. revew the statstcs backgroud eeded for learg regresso, ad. kow a bref hstory of regresso. Revew of Statstcal

More information

C. Statistics. X = n geometric the n th root of the product of numerical data ln X GM = or ln GM = X 2. X n X 1

C. Statistics. X = n geometric the n th root of the product of numerical data ln X GM = or ln GM = X 2. X n X 1 C. Statstcs a. Descrbe the stages the desg of a clcal tral, takg to accout the: research questos ad hypothess, lterature revew, statstcal advce, choce of study protocol, ethcal ssues, data collecto ad

More information

Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq

Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq Iteratoal Joural of Physcal Sceces ol. 8(5), pp. 86-9, 9 February, 3 Avalable ole at http://www.academcjourals.org/ijps DOI:.5897/IJPS.697 ISSN 99-95 3 Academc Jourals Full Legth Research Paper Comparatve

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

Chapter -2 Simple Random Sampling

Chapter -2 Simple Random Sampling Chapter - Smple Radom Samplg Smple radom samplg (SRS) s a method of selecto of a sample comprsg of umber of samplg uts out of the populato havg umber of samplg uts such that every samplg ut has a equal

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

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

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

Regresso What s a Model? 1. Ofte Descrbe Relatoshp betwee Varables 2. Types - Determstc Models (o radomess) - Probablstc Models (wth radomess) EPI 809/Sprg 2008 9 Determstc Models 1. Hypothesze

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

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

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

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Chapter -2 Simple Random Sampling

Chapter -2 Simple Random Sampling Chapter - Smple Radom Samplg Smple radom samplg (SRS) s a method of selecto of a sample comprsg of umber of samplg uts out of the populato havg umber of samplg uts such that every samplg ut has a equal

More information

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

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

More information

Consistency test of martial arts competition evaluation criteria based on mathematical ahp model

Consistency test of martial arts competition evaluation criteria based on mathematical ahp model ISSN : 0974-7435 Volume 8 Issue 2 BoTechology BoTechology A Ida Joural Cosstecy test of martal arts competto evaluato crtera based o mathematcal ahp model Hu Wag Isttute of Physcal Educato, JagSu Normal

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

Parameter, Statistic and Random Samples

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

More information

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

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

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

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

More information

Analyzing Fuzzy System Reliability Using Vague Set Theory

Analyzing Fuzzy System Reliability Using Vague Set Theory Iteratoal Joural of Appled Scece ad Egeerg 2003., : 82-88 Aalyzg Fuzzy System Relablty sg Vague Set Theory Shy-Mg Che Departmet of Computer Scece ad Iformato Egeerg, Natoal Tawa versty of Scece ad Techology,

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

SPATIAL RAINFALL FIELD SIMULATION WITH RANDOM CASCADE INTRODUCING OROGRAPHIC EFFECTS ON RAINFAL

SPATIAL RAINFALL FIELD SIMULATION WITH RANDOM CASCADE INTRODUCING OROGRAPHIC EFFECTS ON RAINFAL Proc. of the d Asa Pacfc Assocato of Hydrology ad Water Resources (APHW) Coferece, July 5-8, 4, Sutec Sgapore Iteratoal Coveto Exhbto Cetre, Sgapore, vol., pp. 67-64, 4 SPATIAL RAINFALL FIELD SIMULATION

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

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

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

A NEW LOG-NORMAL DISTRIBUTION

A NEW LOG-NORMAL DISTRIBUTION Joural of Statstcs: Advaces Theory ad Applcatos Volume 6, Number, 06, Pages 93-04 Avalable at http://scetfcadvaces.co. DOI: http://dx.do.org/0.864/jsata_700705 A NEW LOG-NORMAL DISTRIBUTION Departmet of

More information

Arithmetic Mean Suppose there is only a finite number N of items in the system of interest. Then the population arithmetic mean is

Arithmetic Mean Suppose there is only a finite number N of items in the system of interest. Then the population arithmetic mean is Topc : Probablty Theory Module : Descrptve Statstcs Measures of Locato Descrptve statstcs are measures of locato ad shape that perta to probablty dstrbutos The prmary measures of locato are the arthmetc

More information

On the Link Between the Concepts of Kurtosis and Bipolarization. Abstract

On the Link Between the Concepts of Kurtosis and Bipolarization. Abstract O the Lk etwee the Cocepts of Kurtoss ad polarzato Jacques SILE ar-ila Uversty Joseph Deutsch ar-ila Uversty Metal Haoka ar-ila Uversty h.d. studet) Abstract I a paper o the measuremet of the flatess of

More information

Modified Moment Estimation for a Two Parameter Gamma Distribution

Modified Moment Estimation for a Two Parameter Gamma Distribution IOSR Joural of athematcs (IOSR-J) e-issn: 78-578, p-issn: 39-765X. Volume 0, Issue 6 Ver. V (Nov - Dec. 04), PP 4-50 www.osrjourals.org odfed omet Estmato for a Two Parameter Gamma Dstrbuto Emly rm, Abel

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

CHARACTERIZATION AND FREQUENCY ANALYSIS OF ONE DAY ANNUAL MAXIMUM AND TWO TO FIVE CONSECUTIVE DAYS MAXIMUM RAINFALL OF ACCRA, GHANA

CHARACTERIZATION AND FREQUENCY ANALYSIS OF ONE DAY ANNUAL MAXIMUM AND TWO TO FIVE CONSECUTIVE DAYS MAXIMUM RAINFALL OF ACCRA, GHANA VOL., NO. 5, OCOBER 007 ISSN 1819-6608 ARPN Joural o Egeerg ad Appled Sceces 006-007 Asa Research Publshg Network (ARPN). All rghts reserved. CHARACERIZAION AND FREQUENCY ANALYSIS OF ONE DAY ANNUAL MAXIMUM

More information

Lecture 2 - What are component and system reliability and how it can be improved?

Lecture 2 - What are component and system reliability and how it can be improved? Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected

More information

9.1 Introduction to the probit and logit models

9.1 Introduction to the probit and logit models EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos

More information