Distribution of wind direction recorded at maximum wind speed: A case study of Malaysian wind data for 2005

Size: px
Start display at page:

Download "Distribution of wind direction recorded at maximum wind speed: A case study of Malaysian wind data for 2005"

Transcription

1 Iteratoal Joural of the Physcal Sceces Vol. 6(7), pp , 4 Aprl, Avalable ole at ISSN Academc Jourals Full Legth Research Paper Dstrbuto of wd drecto recorded at maxmum wd speed: A case study of Malaysa wd data for 5 N. A. B. Kamsa, A. G. Huss*, Y. Z. Zubar ad S. F. Hassa Cetre for Foudato Studes Scece, Uversty of Malaya, 563 Kuala Lumpur, Malaysa. Accepted 5 February, I ths study, four types of crcular probablty dstrbuto, amely crcular uform dstrbuto, vo Mses dstrbuto, wrapped-ormal dstrbuto ad wrapped-cauchy dstrbuto are cosdered search of a crcular probablty dstrbuto provdes the best ft for Malaysa wd drecto data recorded at maxmum speed. The data collected were classfed as aual, ortheast mosoo ad southwest mosoo. Seve stes were detfed for ths study based o ther geographc locato, that s, whether they are stuated o the east coast or west coast of Malaysa. The obectve of the study was to see whether there are ay dffereces the crcular probablty dstrbuto of the wd drecto based o these two coasts. Graphcal measures were used to gauge whch dstrbuto gves the best fttg. To further support the fdgs, two performace dcators or goodess of ft tests are used the aalyss, amely the mea crcular dstace ad the. Geerally, t was foud that the mosoo-based data fts well wth the vo Mses dstrbuto as compared to the aual dataset. Key words: Wd data, crcular dstace,, crcular probablty dstrbuto, performace dcator. INTRODUCTION Malaysa has bee fortuate that extreme atural catastrophes rarely occur the coutry, wth the excepto of the typhoo Utor that caused severe floodg Johor 6 ad recetly, recurret evet of heavy ra dowpour caused massve floodg the orther, souther ad easter part of the pesular. Such cdeces have made us realze that somethg should be doe to uderstad meteorologcal dsasters lke typhoos, for example. To uderstad what costtutes typhoos, we eed to start wth wd. Wd s whe ar flows, bee caused by ueve heatg o a surface. Dfferet types of lads ad water absorb heat at dfferet rates. Thus wd vares stregth accordg to the dfferet parts of the earth ad dfferet surfaces. A typhoo ca be classfed as a example of a extremely strog wd that ca be dsastrous. The obectve of ths paper s to model parametrcally wd drectos *Correspodg author. E-mal: ghapor@um.edu.my. Malaysa recorded at maxmum speed usg crcular probablty dstrbutos. Some of the beefts of ths research are terms of statstcal cotrbutos predctg ad modellg wd patters whch ca be used for may purposes ad for further research edeavour. Wd drecto s oe of the features that should be cosdered buldg wd turbes as well as structural ad evrometal desg aalyss (Wzelus, 7). I may coutres, proect maagers ad archtects have started to ackowledge the mportace of esurg a safe ad comfortable wd evromet the vcty of ew buldgs. The developmet of tall buldgs wdy evromets may lead to problems lke fuellg ad dowwash. Other tha that, wd drecto s mportat predctg the weather. Ths s possble because by predctg wd drecto, oe ca have a dea of how the weather wll chage ad forecast o the weather. Predctos of wd drecto are also mportat for sports lke golf ad skydvg. Data o wd drecto are comprsed of agles measured degrees or rada.

2 Kamsa et al. 84 Fgure. Locatos of the maxmum wd data that has bee recorded daly 5. Source: These types of data are kow as crcular data ad thus wd drecto ca be modelled by usg crcular dstrbutos; for example, Razal et al. (8) ther study o surface wd drecto for Bag, a locato Malaysa, foud that the vo Mses seems to be the best dstrbuto to descrbe the patter of surface wd drecto. I ths study, the wd drectos at maxmum wd speed have bee recorded daly for seve locatos ad ftted wth four crcular dstrbutos. The seve locatos selected are, Lagkaw, Melaka, Sea, Kota Bharu, Kuala Tereggau ad Kuata as show Fgure. These places are selected based o ther locatos, that s, whether they are stuated o the east coast or west coast of Malaysa. Ths s because the two coasts are affected dfferetly by the two mosoos that are prevalet Malaysa, the ortheast mosoo ad the southwest mosoo. I the frst stage of the study, the aual data o the wd drectos were aalysed as a whole. I the secod stage, data were aalysed based o the mosoos whch relate to the locatos. Oly four crcular dstrbutos were selected as the software Axs, 3 could oly support these four dstrbutos. CIRCULAR PROBABILITY DISTRIBUTION A crcular probablty dstrbuto s a probablty dstrbuto whose total probablty s cocetrated o the crcumferece of a ut crcle (Jamalamadaka ad

3 84 It. J. Phys. Sc. SeGupta, ). The rage of crcular radom varables ca be measured rada whch ca be defed as [,π ) or [ π, π ). I ths artcle, four crcular probablty dstrbutos are dscussed, whch are the crcular uform dstrbuto, vo Mses dstrbuto, wrapped-ormal dstrbuto ad wrapped-cauchy dstrbuto. Crcular uform dstrbuto Crcular uform dstrbuto s the basc dstrbuto o the crcle whch s varat uder rotato ad reflecto (Bogda et al., ). If the total probablty s spread out uformly o the crcumferece, the we have crcular uformty (CU) dstrbuto wth the costat desty fucto of g ( θ ), θ π π The vo Mses dstrbuto The most useful dstrbuto o the crcle ad was troduced 98 by vo Mses order to study the devatos of measured atomc weghts from tegral values (Marda, 97). Ths dstrbuto s also kow as crcular ormal dstrbuto. A crcular radom varable θ s sad to have a vo Mses dstrbuto f t has desty fucto of κ cos( θ µ ) g ( θ; µ, κ ) e, θ < π πi ( κ ) where µ < π ad κ are mea drecto ad cocetrato parameters, respectvely. The fucto I deotes the modfed Bessel fucto of the frst kd ad order zero ad s defed as: I r κ. r r! ρ g ( θ; µ, ρ), θ < π π + ρ ρ cos( θ µ ) ρ σ e. The equalty of the two aforemetoed where expressos s verfed by equatg the real parts of the k a geometrc seres detty a wth a k ( θ µ ) a ρe. The dstrbuto s umodal ad symmetrc. Wrapped-ormal dstrbuto A wrapped-ormal dstrbuto s obtaed by wrappg a N ( µ, σ ) dstrbuto aroud the crcle where σ log ρ,.e fucto s gve by g( θ; µ, ρ) σ π ρ / σ e. Its probablty desty m ( θ µ πm) exp σ I partcular, the mea drecto s µ (mod π ) ad the mea resultat s ρ. From the theory of theta fucto, a alterate ad more useful represetato of ths desty ca be show to be + p g ( ; µ, ρ) ρ cos p( θ µ ) π p ρ. θ, It s clear that the desty ca be adequately approxmated by ust the frst few terms of the fte σ seres, depedg o the value of. It s umodal ad symmetrc about the value of θ µ. The wrappedormal dstrbuto possesses the addtve property whch s the covoluto of two wrapped-ormal varables s also wrapped-ormal ulke the vo Mses dstrbuto.. Wrapped-Cauchy dstrbuto The wrapped-cauchy dstrbuto s obtaed by wrappg the Cauchy dstrbuto o the real le wth desty of σ f ( x), < x < π σ + ( x µ ) aroud the crcle. It has the probablty desty fucto PERFORMANCE INDICATOR I measurg the sutablty of a model, oe could use performace dcators to check whether the data follows the specfed model. Performace dcator s a basc measuremet of how close a set of data follows a dstrbuto. However, the case of drectoal or crcular data, oe must ot treat the data lke lear data sce drectoal data has dstctve features (Jammalamadaka ad SeGupta, ). Two performace dcators are

4 Kamsa et al. 843 where θ s the observed, A [,]. θˆ s the predcted value ad If the shows a small value, t meas that the data follows the specfed dstrbuto. It ca be see as the weghted average of the of the. crcular dstace Fgure. Chord. proposed for ths study, amely the mea ad mea crcular dstace. Chord s the of the curve segmet betwee two adacet data pots. As ca be see from Fgure, a (broke le) s the. Jammalamadaka ad SeGupta () proposed θ as the smallest agle betwee θ ad θ whch s gve by θ d ( θ, θ ) π π θ θ where θ [, π ]. Oe ca use ths cocept θ to obta the betwee θ ad θ. The of a betwee two pots θ ad θ s calculated accordg to the formula θ crd( θ ) s r where crd( θ ) [,] ad r s the radus. I ths study. The mea s proposed ad gve by π π θ ˆ s θ A Crcular dstace calculates the betwee ay two pots alog the crcumferece ad t takes the smaller of the two arc s betwee the pots. crcular dstace calculates the mea for all of the crcular dstaces for each pot the data. Let us assume that there are pots θ,..., located o the crcumferece of a ut crcle. Let, θ θ, d be the crcular dstace betwee θ ad θ for,,,, where d s gve by d cos( θ θ ) ad where d [,]. I ths study, a ew measure of mea crcular dstace s proposed ad gve by ( cos( θ ˆ θ ) D ) where θ s the observed data ad data ad D [,]. θˆ s the predcted Values that are approxmately close to zero would dcate that the data follows the specfed dstrbuto. Watso-Wllams test The Watso-Wllams test s a crcular aalogue of two sample t-test. It assesses whether the mea drectos of two or more groups are detcal or ot (Beres, 9). The hypotheses are gve below: H All of s groups share a commo mea drecto, that s, α... α s H Not all s groups have a commo mea drecto A The test statstc (Marda ad Jupp, ) s gve as

5 844 It. J. Phys. Sc. F ( N s)( K ( s )( N s R s R) R ) where R s the mea resultat whe all samples are pooled ad R the mea resultat vector computed o the th group aloe (smlar to total varace ad wth group varace the ANOVA settg). 3 The correcto factor K s computed from K +, 8κ where κ s the maxmum lkelhood estmate of the cocetrato parameter of a vo Mses dstrbuto wth resultat vector r. We compute κ va the approxmato gve by Fsher (993). Here w r w s the mea resultat of the s resultat vector computed for each group dvdually. The obtaed value s the compared to a crtcal value at the δ level obtaed from the F- table ad a small p value meas reecto of the ull hypothess, H. FITTING THE WIND DIRECTION WITH CIRCULAR PROBABILITY DISTRIBUTION Parameter estmates For all crcular dstrbutos, the three mportat parameters are mea agle, cocetrato parameter ad mea resultat respectvely. The mea agle, µ s gve by S ta S >, C > C S µ ta + π C < C S ta + π S <, C > C where C cosθ ad S sθ. The mea resultat of crcular dstrbuto, ρ s gve by ρ ( C + S ), r where C cos θ ad S s θ. The cocetrato parameter of crcular dstrbuto κ (Fsher, 993) s gve by: ρ + ρ + ρ 3.43 κ ρ + ρ 3 ( ρ 4ρ + 3ρ ) ρ < ρ <.85 ρ.85 The parameters estmates for all seve locatos ths study are gve subsequetly. Graphcal ad umercal measures for aual wd drecto Table shows the parameter estmated for each data set. After the parameters have bee obtaed from the observed data, a ew set of data s geerated from these parameters. The the geerated data s compared wth the observed data graphcally by usg cumulatve dstrbuto fucto (cdf) plot ad umercally by usg three goodess-of-ft tests as prevously dscussed. Oe example of a cdf plot s show subsequetly for the locato of whch the observed values ad ftted values for all the dstrbuto cosdered are plotted o the same graph. It ca be see that oe of the cdf plots fts well wth the observed cdf plot. I fact, smlar plots doe for the remag sx locatos also show a smlar result to the oe Fgure 3. Ths dcates that oe of these four crcular dstrbutos s a good ft for all of the specfed locatos for the aual dataset. To verfy ths outcome, three goodess-of-ft tests were used to calculate the ftess. The results are show Tables to 4. As ca be see from the Tables to 4, the smaller the value of the mea ad mea crcular dstace, the better the specfed dstrbuto fts wth the wd drecto. The same apples for the Watso- Wllams test, the smaller the F value compared to the p- value dcates the better the ft wth the wd drecto. But as ca be observed, both cdf plot ad the three goodess-of-ft tests do ot show a good ft. Ths could very well to be due to the fact that the aual wd drecto data comprse the wd drecto for both mosoos, amely the southwest ad ortheast mosoos. As a result, the aual wd drecto data have a mxture of both etrely two dfferet two mosoos, thus hderg the search for the best crcular dstrbuto to descrbe the aual wd drecto. The ext step of the aalyss to exame the data wth respect to the two mosoos.

6 Kamsa et al. 845 Table. Parameter estmates for all seve locatos. Locato Parameter agle ( rada) Cocetrato parameter resultat Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata Wrapped - Cauchy.7.6 Crcular Uform vo Mses Wrapped - ormal Fgure 3. The cdf plot of. Table. The mea for aual wd drecto. Locato Crcular dstrbuto Crcular uform Wrapped Cauchy Wrapped ormal vo Mses Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata

7 846 It. J. Phys. Sc. Table 3. The mea crcular dstace for aual wd drecto. Locato Crcular dstrbuto Crcular uform Wrapped Cauchy Wrapped ormal vo Mses Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata Table 4. The Watso-Wllams test for aual wd drecto. Locato Crcular dstrbuto Crcular uform Wrapped ormal Wrapped Cauchy vo Mses Statstcal test p F p F p F p F Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata Table 5. Parameter estmates for ortheast (N.e) ad southwest (S.w) mosoos. Locato Parameter agle (rada) Cocetrato parameter resultat Mosoo N.e S.w N.e S.w N.e S.w N.e S.w Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata FITTING MONSOON WIND DIRECTION WITH CIRCULAR PROBABILITY DISTRIBUTION Parameter estmates As metoed earler, sce the results obtaed from the cdf plot ad the goodess of ft do ot provde cosstet results, the aual data were the categorsed accordg to the two maor mosoos Malaysa amely, the southwest mosoo ad the ortheast mosoo (the southwest mosoo hts the west coast of Malaysa from November tll March ad the ortheast mosoo the east coast of Malaysa from May tll September every year). The same procedure appled for studyg the aual data was repeated for the mosoo-based data. Table 5 shows the parameters estmate for both mosoos

8 Kamsa et al crcular uform vo Mses.5.4 Wrapped - ormal Wrapped - Cauchy Fgure 4. The cdf plot of ortheast mosoo for. Kota Bharu.9 vo Mses.8 Kota Bharu.7.6 wrapped-ormal.5 wrapped-cauchy.4 crcular uform Fgure 5. The cdf plot of ortheast mosoo for Kota Bharu. accordg to the locatos. As ca be see from Table 5, locatos that faced a partcular mosoo have larger cocetrato parameters compared to the oes that dd ot face the partcular mosoo. For stace, Lagkaw ad Melaka have a larger cocetrato parameter durg the southwest mosoo compared to Kota Bharu, Kuala Tereggau ad Kuata whch have larger cocetrato durg the ortheast mosoo. Larger cocetrato parameter suggests that the data s closer to each other. Graphcal ad umercal measures for mosoo wd drecto After the parameters had bee obtaed, ew data were geerated ad compared wth the prevous data. Followg are the results for both the cdf plot (for two locatos) ad the goodess-of-ft tests (for all the locatos). As a llustrato, the cdf plots for two locatos amely ad Kota Bharu s as gve o Fgures 4 to 7. We oted that a umber of fdgs ca be draw. Ftted wth probablty crcular dstrbutos by

9 848 It. J. Phys. Sc..9.8 wrapped - ormal.7 crcular uform.6 wrapped - Cauchy.5.4 vo Mses Fgure 6. The cdf plot of southwest mosoo for. Kota Bharu.9 Kota Bharu.8 wrapped-ormal.7 vo Mses.6.5 wrapped-cauchy.4 crcular uform Fgure 7. The cdf plot of southwest mosoo for Kota Bharu. mosoos; locatos o the east coast lke Kota Bharu seems to ft wth vo Mses durg the ortheast mosoo but t s ot the case durg the southwest mosoo (Fgures 5 ad 7). A smlar patter ca be detected durg the southwest mosoo where vo Mses seems to ft wth (Fgure 6) but ot for Kota Bharu. To support these fdgs, the three goodess-offt tests results are show Tables 6 to 9.

10 Kamsa et al. 849 Table 6. ad crcular dstace of ortheast (N.e) mosoo. Locato Crcular dstrbuto Crcular uform Wrapped Cauchy Wrapped Normal vo Mses crcular dstace Performace dcator crcular dstace crcular dstace crcular dstace Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata Table 7. Watso-Wllams test for ortheast (N.e) mosoo. Locato Crcular uform Wrappedormal Wrapped- Cauchy Crcular dstrbuto Vo Mses Crcular dstrbuto Statstcal test Crcular uform Wrappedormal Wrapped- Cauchy p F p F p F p F Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata Table 8. ad mea crcular dstace for southwest (S.w) mosoo Locato Crcular dstrbuto Crcular uform Wrapped Cauchy Wrapped Normal vo Mses Performace dcator crcular dstace crcular dstace crcular dstace crcular dstace Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata The goodess-of-ft tests results are cosstet wth the cdf plot. Vo Mses fts well wth the locatos alog the east coast of Malaysa durg the ortheast mosoo ad locatos alog the west coast durg the southwest

11 85 It. J. Phys. Sc. Table 9. Watso-Wllams test for southwest (S.w) mosoo Locato Crcular dstrbuto Crcular uform Wrapped-ormal Wrapped-Cauchy Vo Mses Statstcal test p F p F p F p F Lagkaw Melaka Sea Kota Bharu Kuala Tereggau Kuata mosoo. Although for Kuala Tereggau, the Watso- Wllams test does ot show a acceptable p-value, ths s uderstadable sce the data for Kuala Tereggau cotas a large amout of mssg values durg the ortheast mosoo compared to the other locatos. Where Sea s cocered, whe t was tested usg mea ad mea crcular dstace, for both mosoos, t shows a good ft though the values for both performace dcators are larger tha for other locatos whe ftted wth vo Mses. However, whe the Watso- Wllams test was appled, Sea do ot ft well wth ay of the dstrbuto cosdered for both mosoos. Ths s because of Sea s partcular locato. It s stuated at the souther part of Malaysa where t s exposed to both mosoos. Two approaches of performace dcators, the mea ad the mea crcular dstace are cosstet wth the graphcal ad Watso-Wllams test whch mea that the two performace dcators ca be used to test the goodess-of-ft for a comparso betwee two sets of crcular data. REFERENCES AXIS (3). A program for the statstcal aalyss of crcular data, Verso.. Bogda M, Bogda K, Futschk A (). A data drve smooth test for crcular uformty. A. Ist. Stat. Math., 54(): Beres P (9). CrcStat: A MATLAB toolbox for crcular statstcs. J. Stat. Softw., 3(): -. Fsher NI (993). Statstcal Aalyss of Crcular Data. Cambrdge Uversty Press, Cambrdge, pp Jammalamadaka SR, SeGupta A (). Topcs I Crcular Statstcs. World Scetfc, Sgapore. pp. 33: Marda KV, Jupp PE (). Drectoal Statstcs. Joh Wley, Chchester, pp Marda KV (97). Statstcs of Drectoal Data. Academc Press, New York, pp Razal AM, Ahmad A, Zaharm A, Sopa K (8). Fttg the probablty dstrbuto to wd drecto data Bag. Proc. Sem. Eg. Math., pp Wzelus T (7). Developg wd power proects: Theory ad practce. Earthsca Publcato Ltd., Lodo. Cocluso The aual wd drecto recorded at maxmum wd speed 5 dd ot ft wth ay of the four crcular dstrbutos cosdered for all the seve locatos. Ths s because the data gathered were spread betwee the two maor mosoos Malaysa. Whe the data were dvded accordg to the two mosoos whch are the ortheast ad the southwest mosoos, vo Mses dstrbuto seems to be the best crcular dstrbuto that fts well wth the data ad depedg o the locato ad the mosoo. I cocluso, t ca be sad that vo Mses dstrbuto s the best crcular dstrbuto to descrbe the wd drecto recorded at maxmum wd speed ad t depeds o the locato ad the mosoo.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 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

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

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

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

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

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

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

PROJECTION PROBLEM FOR REGULAR POLYGONS

PROJECTION PROBLEM FOR REGULAR POLYGONS Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c

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

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

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

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

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

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

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

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

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

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

Construction of Control Charts for Some Circular Distributions

Construction of Control Charts for Some Circular Distributions IJIRST Iteratoal Joural for Iovatve Research Scece & Techology Volume 5 Issue 1 Jue 2018 ISSN (ole): 2349-6010 Costructo of Cotrol Charts for Some Crcular Dstrbutos P. Srvasa Subrahmayam A. V. Dattatreya

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

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1 STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal

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

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

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 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

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

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

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

Probability and. Lecture 13: and Correlation

Probability and. Lecture 13: and Correlation 933 Probablty ad Statstcs for Software ad Kowledge Egeers Lecture 3: Smple Lear Regresso ad Correlato Mocha Soptkamo, Ph.D. Outle The Smple Lear Regresso Model (.) Fttg the Regresso Le (.) The Aalyss of

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

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

Bayes (Naïve or not) Classifiers: Generative Approach

Bayes (Naïve or not) Classifiers: Generative Approach Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg

More information

Simple Linear Regression

Simple Linear Regression Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uversty Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal

More information

Likelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests. Soccer Goals in European Premier Leagues

Likelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests. Soccer Goals in European Premier Leagues Lkelhood Rato, Wald, ad Lagrage Multpler (Score) Tests Soccer Goals Europea Premer Leagues - 4 Statstcal Testg Prcples Goal: Test a Hpothess cocerg parameter value(s) a larger populato (or ature), based

More information

Laboratory I.10 It All Adds Up

Laboratory I.10 It All Adds Up Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

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

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

BAYESIAN ESTIMATOR OF A CHANGE POINT IN THE HAZARD FUNCTION

BAYESIAN ESTIMATOR OF A CHANGE POINT IN THE HAZARD FUNCTION Mathematcal ad Computatoal Applcatos, Vol. 7, No., pp. 29-38, 202 BAYESIAN ESTIMATOR OF A CHANGE POINT IN THE HAZARD FUNCTION Durdu Karasoy Departmet of Statstcs, Hacettepe Uversty, 06800 Beytepe, Akara,

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

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

{ }{ ( )} (, ) = ( ) ( ) ( ) 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

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

Chapter 3 Sampling For Proportions and Percentages

Chapter 3 Sampling For Proportions and Percentages Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys

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

Permutation Tests for More Than Two Samples

Permutation Tests for More Than Two Samples Permutato Tests for ore Tha Two Samples Ferry Butar Butar, Ph.D. Abstract A F statstc s a classcal test for the aalyss of varace where the uderlyg dstrbuto s a ormal. For uspecfed dstrbutos, the permutato

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

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

The conformations of linear polymers

The conformations of linear polymers The coformatos of lear polymers Marc R. Roussel Departmet of Chemstry ad Bochemstry Uversty of Lethbrdge February 19, 9 Polymer scece s a rch source of problems appled statstcs ad statstcal mechacs. I

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

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty

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

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

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

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

CS286.2 Lecture 4: Dinur s Proof of the PCP Theorem

CS286.2 Lecture 4: Dinur s Proof of the PCP Theorem CS86. Lecture 4: Dur s Proof of the PCP Theorem Scrbe: Thom Bohdaowcz Prevously, we have prove a weak verso of the PCP theorem: NP PCP 1,1/ (r = poly, q = O(1)). Wth ths result we have the desred costat

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

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

1 Solution to Problem 6.40

1 Solution to Problem 6.40 1 Soluto to Problem 6.40 (a We wll wrte T τ (X 1,...,X where the X s are..d. wth PDF f(x µ, σ 1 ( x µ σ g, σ where the locato parameter µ s ay real umber ad the scale parameter σ s > 0. Lettg Z X µ σ we

More information

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i. CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

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

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

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

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA THE ROYAL STATISTICAL SOCIETY EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER II STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d 9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,

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

CHAPTER 3 POSTERIOR DISTRIBUTIONS

CHAPTER 3 POSTERIOR DISTRIBUTIONS CHAPTER 3 POSTERIOR DISTRIBUTIONS If scece caot measure the degree of probablt volved, so much the worse for scece. The practcal ma wll stck to hs apprecatve methods utl t does, or wll accept the results

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

Statistics: Unlocking the Power of Data Lock 5

Statistics: Unlocking the Power of Data Lock 5 STAT 0 Dr. Kar Lock Morga Exam 2 Grades: I- Class Multple Regresso SECTIONS 9.2, 0., 0.2 Multple explaatory varables (0.) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (0.2) Exam 2 Re- grades Re-

More information

Dimensionality Reduction and Learning

Dimensionality Reduction and Learning CMSC 35900 (Sprg 009) Large Scale Learg Lecture: 3 Dmesoalty Reducto ad Learg Istructors: Sham Kakade ad Greg Shakharovch L Supervsed Methods ad Dmesoalty Reducto The theme of these two lectures s that

More information

Mathematics HL and Further mathematics HL Formula booklet

Mathematics HL and Further mathematics HL Formula booklet Dploma Programme Mathematcs HL ad Further mathematcs HL Formula booklet For use durg the course ad the eamatos Frst eamatos 04 Mathematcal Iteratoal Baccalaureate studes SL: Formula Orgazato booklet 0

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

A Note on Ratio Estimators in two Stage Sampling

A Note on Ratio Estimators in two Stage Sampling Iteratoal Joural of Scetfc ad Research Publcatos, Volume, Issue, December 0 ISS 0- A ote o Rato Estmators two Stage Samplg Stashu Shekhar Mshra Lecturer Statstcs, Trdet Academy of Creatve Techology (TACT),

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