Generation of Rainfall Intensity-Duration-Frequency Relationship for North-Western Region in Bangladesh

Size: px
Start display at page:

Download "Generation of Rainfall Intensity-Duration-Frequency Relationship for North-Western Region in Bangladesh"

Transcription

1 IOSR Joural of Evirometal Sciece, Toxicology ad Food Techology (IOSR-JESTFT) e-issn: ,p- ISSN: Volume 9, Issue 9 Ver. I (Sep. 2015), PP Geeratio of Raifall Itesity-Duratio-Frequecy Relatioship for North-Wester Regio i Bagladesh Mushi Md. Rasel 1, Md. Mazharul Islam 2 1 (Lecturer, Departmet of Civil Egieerig, Ahsaullah Uiversity of Sciece & Techology (AUST), Bagladesh) 2 (GRA, Departmet of Civil Egieerig, Ahsaullah Uiversity of Sciece & Techology (AUST), Bagladesh) Abstract: The objective of this research is to geerate Raifall IDF relatioship for North-Wester regio of Bagladesh. Two commo frequecy aalysis techiques Gumbel ad Log Pearso Type III (LPTIII) distributio were used to develop IDF relatioship from raifall data of this regio. Yearly maximum raifall data for last 41 years ( ) from Bagladesh Meteorological Departmet (BMD) was used i this study. Idia Meteorological Departmet (IMD) empirical reductio formula was used to estimate short duratio raifall itesity from yearly maximum raifall data. Results obtaied usig Gumbel method are slightly higher tha LPT III distributio method. Chi-square goodess of fit test was used to determie the best fit probability distributio. The parameters of the IDF equatios ad coefficiet of correlatio for differet retur periods (2, 5, 10, 25, 50 ad 100 years) were calculated by usig oliear multiple regressio method. The results obtaied preseted that i all the cases the correlatio coefficiet is very high represetig goodess of fit of the formulae to estimate IDF curves i the regio of iterest. It was foud that itesity of raifall decreases with icrease i raifall duratio. Further, a raifall of ay give duratio will have a larger itesity if its retur period is large. Keyword:Raifall Itesity, Raifall Duratio, Raifall Frequecy, Gumbel s Extreme Value Distributio Method, Log Pearso Type III I. Itroductio Raifall itesity-duratio-frequecy (IDF) curves are graphical exemplificatios of the amout of water that falls withi a give period of time i catchmet areas [1]. The raifall Itesity- Duratio-Frequecy (IDF) relatioship i oe of the most importat tools i water resource egieerig to assess the risk ad vulerability of water resource structure as well as for plaig, desig ad operatio.the establishmet of such relatioship was doe as early as 1932[1, 2]. Sice the, may sets of relatioships have bee costructed for several parts of the globe. However, such relatioships have ot bee accurately costructed i may developig coutries [4]. IDF relatioship is a mathematical relatioship betwee the raifall itesity I, the duratio d, ad the retur period T [4, 3]. Ideed IDF curves allow the estimatio of the retur period of a observed raifall evet or coversely of the raifall amout correspodig to a give retur period for differet aggregatio times. Further studies were performed o raifall aalysis ad regioalizatio of IDF curves for differet regios [5]. I Bagladesh water loggig ad flood is a commo problem durig Mosoo period because of iadequate draiage system. I order to solve this problem ew draiage desig is eeded where raifall data of differet duratio is eeded. But due to istrumetal limitatio these data were ot available. I the preset study, aual maximum raifall series is cosidered for Raifall Frequecy Aalysis (RFA). Raifall i a regio ca be characterized if the itesity, duratio ad frequecy of the diverse storms occurrig at that place are kow [6, 7, ad 8]. The frequecy-data for raifalls of various duratios, so obtaied, ca be represeted by IDF curves, which give a plot of raifall itesity versus raifall duratio ad recurrece iterval. I recet studies, various authors attempted to relate IDF relatioship to the syoptic meteorological coditios i the area of hydrometric statios [1, 9].The IDF curve for North-East regio of Bagladesh was developed ad observed that the raifall data i this regio follow Gumbel s Extreme Value Distributio [10]. The short duratio raifall IDF curve was developed for Sylhet with retur period of 2, 5,10,20,50, ad 100 years [11].The accuracy of IDF was improved curves by usig log ad short duratio separatio techique [12]. L-momets ad geeralized least squares regressio method was applied for estimatio of desig raifall depths ad developmet of IDF relatioships [13]. Pearso Type-III distributio was applied for modellig of short duratio raifall ad developmet of IDF relatioships for sylhet city i Bagladesh [14]. DOI: / Page

2 Geeratio of Raifall Itesity-Duratio-Frequecy Relatioship for North-Wester Regio Atomic Eergy Regulatory Board (AERB) guidelies described that the Order Statistics Approach (OSA) is the most appropriate method for determiatio of parameters of Gumbel ad Frechetdistributios [15]. I probability theory, extreme value distributios amely Gumbel, Frechet ad Weibull are geerally cosidered for frequecy aalysis of meteorological variables. I this preset study Gumbel s Extreme Value Distributio method is used to develop IDF curves ad equatios. I this cotext, a attempt has bee made to estimate the raifall for differet retur periods for differet duratios of such as 10-mi, 20-mi, 30-mi, 60-mi, 120-mi, 180-mi, 360-mi, 720-mi, 1440-mi adoptig Gumbel ad LPTIII distributios for developmet of IDF relatioships for North-Wester regio of Bagladesh. Model performace idicators (MPIs) such as correlatio coefficiet (R) is used to aalyze the performace of the developed IDF relatioships by Gumbelad LPTIII distributios for estimatio of raifall itesity for the statios uder study. II. Data Collectio Ad Methodology For this study 24 hr daily raifall data from year 1974 to 2014 was collected from Bagladesh Meteorological Departmet (BMD) for North-Wester regio. From the daily data maximum yearly raifall data was used i the aalysis. I North-Wester regio there are seve BMD statios (Bogra, Ishurdi, Diajpur, Ragpur, Rajshahi, Sydpur, Chuadaga) which were take ito cosideratio to develop IDF curve for this regio of Bagladesh. 2.1Estimatio of Short Duratio Raifall Idia Meteorological Departmet (IMD) use a empirical reductio formula equatio (1) for estimatio of various duratio like 1-hr, 2-hr, 3-hr, 5-hr, 8-hr raifall values from aual maximum values. This formula was used to estimate the short duratio raifall from daily raifall data i Sylhet city ad foud that this formula give the best estimatio of short duratio raifall [11]. I this study this empirical formula equatio (1) was used to estimate short duratio raifall of sevestatios of North-Wester regio of Bagladesh. P t = P 24 3 t (1) Where, P t is the required raifall depth i mm at t-hr duratio, P 24 is the daily raifall i mm ad t is the duratio of raifall for which the raifall depth is required i hr. 2.2Gumbel Theory of Distributio The Gumbel method calculates the 2, 5, 10, 25, 50 ad 100 year retur itervals for each duratio period ad requires several calculatios. Frequecy precipitatio P T (i mm) for each duratio with a specified retur period T (i year) is give by the followig equatio: P T = P ave + KS.. (2) Where K is Gumbel frequecy factor give by: K=- 6 T [ l[l[ ]]]..... (3) π T 1 Where P ave is the average of the maximum precipitatio correspodig to a specific duratio. I utilizig Gumbel s distributio, the arithmetic average i equatio (2) is used: P ave = 1 Pi i=1.. (4) Where Pi is the idividual extreme value of raifall ad is the umber of evets or years of record. The stadard deviatio is calculated by equatio (5) computed usig the followig relatio: (Pi Pave) 2 ] 1/2... (5) S=[ 1 1 i=1 Where S is the stadard deviatio of P data. The frequecy factor (K), which is a fuctio of the retur period ad sample size, whe multiplied by the stadard deviatio gives the departure of a desired retur period raifall from the average. The the raifall itesity, I T (i mm/hr) for retur period T is obtaied from: I T = Pt..... (6) Td Where T d is duratio i hours. The frequecy of the raifall is usually defied by referece to the aual maximum series, which cosists of the largest values observed i each year. A alterative data format for raifall frequecy studies is that based DOI: / Page

3 Geeratio of Raifall Itesity-Duratio-Frequecy Relatioship for North-Wester Regio o the peak-over threshold cocept, which cosists of all precipitatio amouts above certai thresholds selected for differet duratios. Due to its simpler structure, the aual-maximum-series method is more popular i practice [16]. 2.3 Log Pearso type III The LPT III probability model is used to calculate the raifall itesity at differet raifall duratios ad retur periods to form the historical IDF curves for each statio. LPT III distributio ivolves logarithms of the measured values. The mea ad the stadard deviatio are determied usig the logarithmically trasformed data. I the same maer as with Gumbel method, the frequecy precipitatio is obtaied usig LPT III method. The simplified expressio for this distributio is give as follows: P* log(pi).. (7) PT* Pave * KTS * (8) 1 Pave* P*.... (9) i 1 1/ (10) i1 1 S* ( P* Pave*) 1 Where P T *, Pave*, S* are desired raifall peak value for a specific frequecy, average of maximum precipitatio correspodig to a specific duratio, stadard deviatio of P* value respectively. K T is the Pearso frequecy factor which depeds o retur period (T) ad skewess coefficiet (Cs). The skewess coefficiet, Cs, is required to compute the frequecy factor for this distributio. The skewess coefficiet is computed by equatio (11) [2&17] C S ( Pi * Pave*) i1 3 ( 1)( 2)(S*) 3...(11) K T values ca be obtaied from tables i may hydrology refereces; for example referece [2]. By kowig the skewess coefficiet ad the recurrece iterval, the frequecy factor, K T for the LPT III distributio ca be extracted. The atilog of the solutio i equatio (7) will provide the estimated extreme value for the give retur period. 2.4 Derivatio of IDF equatio To derive a equatio for calculatig the raifall itesity (I) for the regios of iterest, there are some required steps for establishig a equatio to suit the calculatio of raifall itesity for a certai recurrece iterval ad specific raifall period which depeds maily o the results obtaied from the IDF curves. Two approaches were tried to estimate the equatio parameters. By applyig the logarithmic coversio, where it is possible to covert the equatio ito a liear equatio, thus to calculate all the parameters related to the equatio. The followig steps are followed: 1. Covert the origial equatio i the form of power-law relatio as follows: I CT T m r e d (12) By applyig the logarithmic fuctio to get logi = log K e log T d..... (13) Where K=CTr m.... (14) Ad e represets the slope of the straight lie. DOI: / Page

4 Geeratio of Raifall Itesity-Duratio-Frequecy Relatioship for North-Wester Regio 2. Calculate the atural logarithm for (K) value foud from Gumbel method or from LPTIII method as well as the atural logarithmic for raifall period T d. 3. Plot the values of (log I) o the y-axis ad the value of (log T d ) o the x axis for all the recurrece itervals for the two methods. 4. From the graphs (or mathematically) fid the value of (e) for all recurrece itervals. The it was foud out the average value of e value, e ave, by usig the followig equatio: e eave (15) Where represets recurrece itervals (years) value oted as Tr. 5. From the graph, it was foud logk values for each recurrece iterval where logk represets the Y-itercept values as per Gumbel method or LPTIII method. The covert equatio (14) ito a liear equatio by applyig the atural logarithm to become: logk= logc + m logtr.... (16) 6. Plot the values of logk o the y-axis ad the values of logtr o the x-axis to fid out the values of parameters c ad m as per Gumberl method or LPTIII where m represets the slope of the straight lie ad c represets the (ati log) for the y itercept. I aother approach Estimatio of the equatio parameters by usig oliear regressio aalysis: Usig the Solver fuctio of the ubiquitous spreadsheet program Microsoft Excel, which employs a iterative least squares fittig routie to produce the optimal goodess of fit betwee data ad fuctio. The R 2 value calculated is desiged to give the user a estimate of goodess of fit of the fuctio to the data. 2.5 Goodess of fit test The purpose of this test is to decide how good is a fit betwee the observed frequecy of occurrece i a sample ad the expected frequecies obtaied from the hypothesized distributios. A goodess of fit test betwee observed ad expected frequecies is based o the chi-square quatity, which is expressed as, k 2 2 (Oi E i) / Ei i1... (17) 2 where is a radom variable whose samplig distributio is approximated very closely by the chi-square distributio. The symbols O i ad E i represet the observed ad expected frequecies, respectively, for the i-th class iterval i the histogram. The symbol k represets the umber of class itervals. If the observed 2 frequecies are close to the correspodig expected frequecies, the value will be small, idicatig a good fit; otherwise, it is a poor fit. A good fit leads to the acceptace of ull hypothesis, whereas a poor fit leads to its rejectio. The critical regio will, therefore, fall i the right tail of the chi-square distributio. For a level of 2 sigificace equal to a, the critical value is foud from readily available chi-square tables ad > costitutes the critical regio [19]. III. ResultAd Discussio Fig 1-2 show results of the IDF curves obtaied by Gumbel ad LPT III methods for North- Wester regio. It was show that there were small differeces betwee the results obtaied from the two methods, where Gumbel method gives slightly higher results tha the results obtaied by Log Pearso Type III. This is show also from parameters of the derived equatio for calculatig the raifall itesity usig the two methods. DOI: / Page

5 Itesity (mm/hr) Geeratio of Raifall Itesity-Duratio-Frequecy Relatioship for North-Wester Regio years 5 years 10 years Duratio 25 (mi) years 50 years 100 years Figure 1: IDF curve by Gumbel method at North-Wester regio Parameters of the selected IDF formula were adjusted by the method of miimum squares, where the goodess of fit is judged by the correlatio coefficiet. The results obtaied showed that i all the cases the correlatio coefficiet is very high, ad rages betwee ad 0.987, except few cases where itrages betwee ad whe usig LPT III at 50 ad 100 years. This idicates the goodess of fit of the formulae to estimate IDF curves i the regio of iterest. For each regio the results are give as the mea value of the poits results. Table 1 shows the parameters values obtaied by aalyzig the IDF data usig the two methods ad those are used i derivig formulae for the two regios. Figure 2: IDF curve by LPTIII method at North-Wester Regio DOI: / Page

6 Geeratio of Raifall Itesity-Duratio-Frequecy Relatioship for North-Wester Regio Figure 3: IDF curve by average at North-Wester Regio Table 1: IDF parameters values used i derivig formula Regio Parameter Gumbel Method Log Pearso Type III c North- Wester m e Also, goodess-of-fit tests were used to choose the best statistical distributio amog those techiques. Results of the chi-square goodess of fit test o aual series of raifall are show i Table 2. As it is see most of the data fit the distributios at the level of sigificace of =0.05, which yields <3.84. Oly the data for 10, 20, 30 mi do ot give good fit usig Gumbel method distributio. Also the data for 10, 20 mi usig LPTIII method do ot give good fit. cal Regio North- Wester Table 2: Results of chi-square goodess of fit test o aual maximum raifall Duratio (mi) Distributio Gumbel LPTIII IV. Coclusios Sice Bagladesh has differet climatic coditios from regio to regio, a relatioshipof R-IDF for each regio has to be obtaied to estimate raifall itesities for differet duratios ad retur periods ragig betwee 2 ad 100 years. It was foud that Gumbel method gave some larger raifall itesity estimates compared to LPT III distributio. I geeral, the results obtaied usig the two approaches are very close at most of the retur periods ad have the sametred.the results obtaied from that work are cosistet with the results from previous studies doe i some parts of the study area. The parameters of the desig raifall itesity for a give period of recurrece iterval were estimated for this regio. The results showed that i all the cases data fitted the formula with a correlatio coefficiet greater tha This idicates the goodess of fit of the formulae to estimate IDF curves i the regio of iterest for duratios varyig from 10 to 1440 mi ad retur periods from 2 to 100 years. The Chi-square test was used o oe had to examie the combiatios or cotigecy of the observed ad theoretical frequecies, ad o the other had, to decide about the type of distributio which the available data set follows. The results of the chi-square test of goodess of fit showed that i all the duratios the ull hypothesis that the extreme raifall series have the Gumbel distributio is acceptable at the 5% level of sigificace. Oly few cases i which the fittig was ot good obtaied by usig the LPT III distributio. Although the Chi-square values are appreciably below the critical regio usig Gumbel distributio ad few values are higher tha the critical regio usig LPT III distributio, it is difficult to say that oe distributio is superior to the other. Further studies are recommeded wheever there will be more data to verify the results obtaied or update the IDF curves. DOI: / Page

7 Geeratio of Raifall Itesity-Duratio-Frequecy Relatioship for North-Wester Regio Ackowledgemet Authors are grateful to the Bagladesh Meteorological Departmet (BMD) for providig the ecessary meteorological data. Refereces [1]. Dupot, B.S., Alle, D.L.,Establishmet of Itesity Duratio Frequecy Curves for Precipitatio i the Mosoo Area of Vietam. Ketucky Trasportatio Ceter, College of Egieer, Uiversity of Ketucky i corporatio with US Departmet of Trasportatio, 2006 [2]. Chow, V.T., Hadbook of Applied Hydrology. McGraw-Hill Book, [3]. Koutsoyiais, D., O the appropriateess of the Gumbel distributio for modellig extreme raifall, i: Proceedigs of the ESF LESC Exploratory Workshop, Hydrological Risk: recet advaces i peak river flow modellig, predictio ad real-time forecastig, Assessmet of the impacts of lad-use ad climate chages, Europea Sciece Foudatio, Natioal Research Coucil of Italy, Uiversity of Bologa, Bologa, October 2003.Koutsoyiais, D., Kozois, D., Maetas, A., A mathematical framework for studyig raifall itesity-duratio- frequecy relatioships. J. Hydrol. 206, [4]. Iloa, V., Fraces, F Raifall aalysis ad regioalizatio computig itesity-duratio-frequecy curves, UiversidadPolitecica de Valecia - Departameto de IgeieriaHidraulicaMedioAmbiete - APDO Valecia - Spai. [5]. Burlado P ad Rosso R. Scalig ad multi-scalig models of depth-duratio-frequecy curves for storm precipitatio. Joural of Hydrology. 1996; 187(1&2): [6]. Koutsoyiais D, Kozois D ad Maetas A. A mathematical framework for studyig raifall itesity- duratio-frequecy relatioships. Joural of Hydrology. 1998; 206(1&2): [7]. Bougadis J ad Adamowski K. Scalig model of a raifall itesity-duratio-frequecy relatioship. Hydrological Process. 2006; 20(17): [8]. Mohymot1,B.,Demaree1,G.R.,Faka2,D.N.2004.Establishmet of IDF-curves for precipitatio i the tropical area of Cetral Africa-compariso of techiques ad results, Departmet of Meteorological Research ad Developmet, Royal Meteorological Istitute of Belgium, Riglaa 3, B-1180 Brussels, Belgium. [9]. Mati M. A. ad Ahmed S. M. U Raifall Itesity Duratio Frequecy Relatioship for the N-E Regio of Bagladesh. Joural of Water Resource Research. 5(1). [10]. Chowdhury R., Alam J. B., Das P. ad Alam M. A Short Duratio Raifall Estimatio of Sylhet: IMD ad USWB Method. Joural of Idia Water Works Associatio. pp [11]. Kim T, Shi J, Kim K ad Heo J. Improvig accuracy of IDF curves usig log- ad short duratio separatio ad multi-objective geetic algorithm. World Evirometal ad Water Resources Cogress. 2008, [12]. Khaled H, Ataur R, Jaice G ad George K. Desig raifall estimatio for short storm duratios usig L-Momets ad geeralized least squares regressio-applicatio to Australia Data. Iteratioal Joural of Water Resources ad Arid Eviromets. 2011; 1(3): [13]. Rashid MM, Faruque SB ad Alam JB. Modellig of short duratio raifall itesity duratio Frequecy (SDRIDF) equatio for Sylhet City i Bagladesh. ARPN Joural of Sciece ad Techology. 2012; 2(2): [14]. Atomic Eergy Regulatory Board (AERB), Extreme values of meteorological parameters (Guide No. NF/SG/S-3), [15]. Borga, M., Vezzai, C., Fotaa, G.D A Regioal Raifall Depth Duratio Frequecy Equatios for a Alpie Regio, Departmet of Lad ad AgroForest Eviromets, Uiversity of Padova, Legaro 35020, Italy, Natural Hazards, vol. 36, pp [16]. Burke, C.B., Burke, T.T., Storm Draiage Maual. Idiaa LTAP. [17]. M. M. Rashid, S.B. Faruque J. B. Alam, Modelig of Short Duratio Raifall Itesity Duratio Frequecy (SDRIDF) Equatio for Sylhet City i Bagladesh, ARPN Joural of Sciece ad Techology, Vol 2, No. 2 [18]. Al-Shaikh, A.A. Raifall frequecy studies for Saudi Arabia, M.S.Thesis, Civil Egieerig Departmet, Kig Saud Uiversity, Riaydh (K.S.A), DOI: / Page

GENERATION OF RAINFALL INTENSITY-DURATION-FREQUENCY RELATIONSHIP FOR CENTRAL REGION IN BANGLADESH

GENERATION OF RAINFALL INTENSITY-DURATION-FREQUENCY RELATIONSHIP FOR CENTRAL REGION IN BANGLADESH Proceedigs of the 3 rd Iteratioal Coferece o Civil Egieerig for Sustaiable Developmet (ICCESD 206), 2~4 February 206, KUET, Khula, Bagladesh (ISBN: 978-984-34-0265-3) GENERATION OF RAINFALL INTENSITY-DURATION-FREQUENCY

More information

Modeling Rainfall Intensity Duration Frequency (R-IDF) Relationship for Seven Divisions of Bangladesh

Modeling Rainfall Intensity Duration Frequency (R-IDF) Relationship for Seven Divisions of Bangladesh EUROPEAN ACADEMIC RESEARCH Vol. III, Issue 5/ August 2015 ISSN 2286-4822 www.euacademic.org Impact Factor: 3.4546 (UIF) DRJI Value: 5.9 (B+) Modeling Rainfall Intensity Duration Frequency (R-IDF) Relationship

More information

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio

More information

1 Inferential Methods for Correlation and Regression Analysis

1 Inferential Methods for Correlation and Regression Analysis 1 Iferetial Methods for Correlatio ad Regressio Aalysis I the chapter o Correlatio ad Regressio Aalysis tools for describig bivariate cotiuous data were itroduced. The sample Pearso Correlatio Coefficiet

More information

A statistical method to determine sample size to estimate characteristic value of soil parameters

A statistical method to determine sample size to estimate characteristic value of soil parameters A statistical method to determie sample size to estimate characteristic value of soil parameters Y. Hojo, B. Setiawa 2 ad M. Suzuki 3 Abstract Sample size is a importat factor to be cosidered i determiig

More information

Final Examination Solutions 17/6/2010

Final Examination Solutions 17/6/2010 The Islamic Uiversity of Gaza Faculty of Commerce epartmet of Ecoomics ad Political Scieces A Itroductio to Statistics Course (ECOE 30) Sprig Semester 009-00 Fial Eamiatio Solutios 7/6/00 Name: I: Istructor:

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information

Rainy and Dry Days as a Stochastic Process (Albaha City)

Rainy and Dry Days as a Stochastic Process (Albaha City) IOSR Joural of Mathematics (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X. Volume, Issue Ver. III (Ja - Feb. 25), PP 86-9 www.iosrjourals.org Raiy ad Dry Days as a Stochastic Process (Albaha City) Dr. Najeeb

More information

Confidence interval for the two-parameter exponentiated Gumbel distribution based on record values

Confidence interval for the two-parameter exponentiated Gumbel distribution based on record values Iteratioal Joural of Applied Operatioal Research Vol. 4 No. 1 pp. 61-68 Witer 2014 Joural homepage: www.ijorlu.ir Cofidece iterval for the two-parameter expoetiated Gumbel distributio based o record values

More information

WHAT IS THE PROBABILITY FUNCTION FOR LARGE TSUNAMI WAVES? ABSTRACT

WHAT IS THE PROBABILITY FUNCTION FOR LARGE TSUNAMI WAVES? ABSTRACT WHAT IS THE PROBABILITY FUNCTION FOR LARGE TSUNAMI WAVES? Harold G. Loomis Hoolulu, HI ABSTRACT Most coastal locatios have few if ay records of tsuami wave heights obtaied over various time periods. Still

More information

Comparison of Minimum Initial Capital with Investment and Non-investment Discrete Time Surplus Processes

Comparison of Minimum Initial Capital with Investment and Non-investment Discrete Time Surplus Processes The 22 d Aual Meetig i Mathematics (AMM 207) Departmet of Mathematics, Faculty of Sciece Chiag Mai Uiversity, Chiag Mai, Thailad Compariso of Miimum Iitial Capital with Ivestmet ad -ivestmet Discrete Time

More information

Chapter 12 Correlation

Chapter 12 Correlation Chapter Correlatio Correlatio is very similar to regressio with oe very importat differece. Regressio is used to explore the relatioship betwee a idepedet variable ad a depedet variable, whereas correlatio

More information

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND.

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. XI-1 (1074) MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. R. E. D. WOOLSEY AND H. S. SWANSON XI-2 (1075) STATISTICAL DECISION MAKING Advaced

More information

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm Joural of ad Eergy Egieerig, 05, 3, 43-437 Published Olie April 05 i SciRes. http://www.scirp.org/joural/jpee http://dx.doi.org/0.436/jpee.05.34058 Study o Coal Cosumptio Curve Fittig of the Thermal Based

More information

11 Correlation and Regression

11 Correlation and Regression 11 Correlatio Regressio 11.1 Multivariate Data Ofte we look at data where several variables are recorded for the same idividuals or samplig uits. For example, at a coastal weather statio, we might record

More information

GUIDE FOR THE USE OF THE DECISION SUPPORT SYSTEM (DSS)*

GUIDE FOR THE USE OF THE DECISION SUPPORT SYSTEM (DSS)* GUIDE FOR THE USE OF THE DECISION SUPPORT SYSTEM (DSS)* *Note: I Frech SAD (Système d Aide à la Décisio) 1. Itroductio to the DSS Eightee statistical distributios are available i HYFRAN-PLUS software to

More information

Response Variable denoted by y it is the variable that is to be predicted measure of the outcome of an experiment also called the dependent variable

Response Variable denoted by y it is the variable that is to be predicted measure of the outcome of an experiment also called the dependent variable Statistics Chapter 4 Correlatio ad Regressio If we have two (or more) variables we are usually iterested i the relatioship betwee the variables. Associatio betwee Variables Two variables are associated

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 9 Multicolliearity Dr Shalabh Departmet of Mathematics ad Statistics Idia Istitute of Techology Kapur Multicolliearity diagostics A importat questio that

More information

A proposed discrete distribution for the statistical modeling of

A proposed discrete distribution for the statistical modeling of It. Statistical Ist.: Proc. 58th World Statistical Cogress, 0, Dubli (Sessio CPS047) p.5059 A proposed discrete distributio for the statistical modelig of Likert data Kidd, Marti Cetre for Statistical

More information

Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA

Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA Evirometal Egieerig 0th Iteratioal Coferece eissn 2029-7092 / eisbn 978-609-476-044-0 Vilius Gedimias Techical Uiversity Lithuaia, 27 28 April 207 Article ID: eviro.207.094 http://eviro.vgtu.lt DOI: https://doi.org/0.3846/eviro.207.094

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

The Sampling Distribution of the Maximum. Likelihood Estimators for the Parameters of. Beta-Binomial Distribution

The Sampling Distribution of the Maximum. Likelihood Estimators for the Parameters of. Beta-Binomial Distribution Iteratioal Mathematical Forum, Vol. 8, 2013, o. 26, 1263-1277 HIKARI Ltd, www.m-hikari.com http://d.doi.org/10.12988/imf.2013.3475 The Samplig Distributio of the Maimum Likelihood Estimators for the Parameters

More information

Maximum likelihood estimation from record-breaking data for the generalized Pareto distribution

Maximum likelihood estimation from record-breaking data for the generalized Pareto distribution METRON - Iteratioal Joural of Statistics 004, vol. LXII,. 3, pp. 377-389 NAGI S. ABD-EL-HAKIM KHALAF S. SULTAN Maximum likelihood estimatio from record-breakig data for the geeralized Pareto distributio

More information

Goodness-Of-Fit For The Generalized Exponential Distribution. Abstract

Goodness-Of-Fit For The Generalized Exponential Distribution. Abstract Goodess-Of-Fit For The Geeralized Expoetial Distributio By Amal S. Hassa stitute of Statistical Studies & Research Cairo Uiversity Abstract Recetly a ew distributio called geeralized expoetial or expoetiated

More information

REGRESSION (Physics 1210 Notes, Partial Modified Appendix A)

REGRESSION (Physics 1210 Notes, Partial Modified Appendix A) REGRESSION (Physics 0 Notes, Partial Modified Appedix A) HOW TO PERFORM A LINEAR REGRESSION Cosider the followig data poits ad their graph (Table I ad Figure ): X Y 0 3 5 3 7 4 9 5 Table : Example Data

More information

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals

More information

Worksheet 23 ( ) Introduction to Simple Linear Regression (continued)

Worksheet 23 ( ) Introduction to Simple Linear Regression (continued) Worksheet 3 ( 11.5-11.8) Itroductio to Simple Liear Regressio (cotiued) This worksheet is a cotiuatio of Discussio Sheet 3; please complete that discussio sheet first if you have ot already doe so. This

More information

577. Estimation of surface roughness using high frequency vibrations

577. Estimation of surface roughness using high frequency vibrations 577. Estimatio of surface roughess usig high frequecy vibratios V. Augutis, M. Sauoris, Kauas Uiversity of Techology Electroics ad Measuremets Systems Departmet Studetu str. 5-443, LT-5368 Kauas, Lithuaia

More information

Nonlinear regression

Nonlinear regression oliear regressio How to aalyse data? How to aalyse data? Plot! How to aalyse data? Plot! Huma brai is oe the most powerfull computatioall tools Works differetly tha a computer What if data have o liear

More information

Investigating the Significance of a Correlation Coefficient using Jackknife Estimates

Investigating the Significance of a Correlation Coefficient using Jackknife Estimates Iteratioal Joural of Scieces: Basic ad Applied Research (IJSBAR) ISSN 2307-4531 (Prit & Olie) http://gssrr.org/idex.php?joural=jouralofbasicadapplied ---------------------------------------------------------------------------------------------------------------------------

More information

multiplies all measures of center and the standard deviation and range by k, while the variance is multiplied by k 2.

multiplies all measures of center and the standard deviation and range by k, while the variance is multiplied by k 2. Lesso 3- Lesso 3- Scale Chages of Data Vocabulary scale chage of a data set scale factor scale image BIG IDEA Multiplyig every umber i a data set by k multiplies all measures of ceter ad the stadard deviatio

More information

Chapter 6 Sampling Distributions

Chapter 6 Sampling Distributions Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to

More information

Approximate Confidence Interval for the Reciprocal of a Normal Mean with a Known Coefficient of Variation

Approximate Confidence Interval for the Reciprocal of a Normal Mean with a Known Coefficient of Variation Metodološki zvezki, Vol. 13, No., 016, 117-130 Approximate Cofidece Iterval for the Reciprocal of a Normal Mea with a Kow Coefficiet of Variatio Wararit Paichkitkosolkul 1 Abstract A approximate cofidece

More information

Estimation of Gumbel Parameters under Ranked Set Sampling

Estimation of Gumbel Parameters under Ranked Set Sampling Joural of Moder Applied Statistical Methods Volume 13 Issue 2 Article 11-2014 Estimatio of Gumbel Parameters uder Raked Set Samplig Omar M. Yousef Al Balqa' Applied Uiversity, Zarqa, Jorda, abuyaza_o@yahoo.com

More information

ECON 3150/4150, Spring term Lecture 3

ECON 3150/4150, Spring term Lecture 3 Itroductio Fidig the best fit by regressio Residuals ad R-sq Regressio ad causality Summary ad ext step ECON 3150/4150, Sprig term 2014. Lecture 3 Ragar Nymoe Uiversity of Oslo 21 Jauary 2014 1 / 30 Itroductio

More information

t distribution [34] : used to test a mean against an hypothesized value (H 0 : µ = µ 0 ) or the difference

t distribution [34] : used to test a mean against an hypothesized value (H 0 : µ = µ 0 ) or the difference EXST30 Backgroud material Page From the textbook The Statistical Sleuth Mea [0]: I your text the word mea deotes a populatio mea (µ) while the work average deotes a sample average ( ). Variace [0]: The

More information

Math 152. Rumbos Fall Solutions to Review Problems for Exam #2. Number of Heads Frequency

Math 152. Rumbos Fall Solutions to Review Problems for Exam #2. Number of Heads Frequency Math 152. Rumbos Fall 2009 1 Solutios to Review Problems for Exam #2 1. I the book Experimetatio ad Measuremet, by W. J. Youde ad published by the by the Natioal Sciece Teachers Associatio i 1962, the

More information

Chapter 2 Descriptive Statistics

Chapter 2 Descriptive Statistics Chapter 2 Descriptive Statistics Statistics Most commoly, statistics refers to umerical data. Statistics may also refer to the process of collectig, orgaizig, presetig, aalyzig ad iterpretig umerical data

More information

S Y Y = ΣY 2 n. Using the above expressions, the correlation coefficient is. r = SXX S Y Y

S Y Y = ΣY 2 n. Using the above expressions, the correlation coefficient is. r = SXX S Y Y 1 Sociology 405/805 Revised February 4, 004 Summary of Formulae for Bivariate Regressio ad Correlatio Let X be a idepedet variable ad Y a depedet variable, with observatios for each of the values of these

More information

CHAPTER 4 BIVARIATE DISTRIBUTION EXTENSION

CHAPTER 4 BIVARIATE DISTRIBUTION EXTENSION CHAPTER 4 BIVARIATE DISTRIBUTION EXTENSION 4. Itroductio Numerous bivariate discrete distributios have bee defied ad studied (see Mardia, 97 ad Kocherlakota ad Kocherlakota, 99) based o various methods

More information

Modeling the Distribution of Rainfall Intensity using Quarterly Data

Modeling the Distribution of Rainfall Intensity using Quarterly Data IOSR Joural of Mathematics (IOSR-JM) e-issn: 78-578, p-issn:319-765x. Volume 9, Issue 1 (Nov. Dec. 013), PP 11-16 Modelig the Distributio of Raifall Itesity usig Quarterly Data H. G. Dikko 1, I. J. David

More information

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics Explorig Data: Distributios Look for overall patter (shape, ceter, spread) ad deviatios (outliers). Mea (use a calculator): x = x 1 + x 2 + +

More information

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n.

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n. Jauary 1, 2019 Resamplig Methods Motivatio We have so may estimators with the property θ θ d N 0, σ 2 We ca also write θ a N θ, σ 2 /, where a meas approximately distributed as Oce we have a cosistet estimator

More information

New Correlation for Calculating Critical Pressure of Petroleum Fractions

New Correlation for Calculating Critical Pressure of Petroleum Fractions IARJSET ISSN (Olie) 2393-8021 ISSN (Prit) 2394-1588 Iteratioal Advaced Research Joural i Sciece, Egieerig ad Techology New Correlatio for Calculatig Critical Pressure of Petroleum Fractios Sayed Gomaa,

More information

Estimation for Complete Data

Estimation for Complete Data Estimatio for Complete Data complete data: there is o loss of iformatio durig study. complete idividual complete data= grouped data A complete idividual data is the oe i which the complete iformatio of

More information

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series Applied Mathematical Scieces, Vol. 7, 03, o. 6, 3-337 HIKARI Ltd, www.m-hikari.com http://d.doi.org/0.988/ams.03.3430 Compariso Study of Series Approimatio ad Covergece betwee Chebyshev ad Legedre Series

More information

Mathematical Notation Math Introduction to Applied Statistics

Mathematical Notation Math Introduction to Applied Statistics Mathematical Notatio Math 113 - Itroductio to Applied Statistics Name : Use Word or WordPerfect to recreate the followig documets. Each article is worth 10 poits ad ca be prited ad give to the istructor

More information

Linear Regression Models

Linear Regression Models Liear Regressio Models Dr. Joh Mellor-Crummey Departmet of Computer Sciece Rice Uiversity johmc@cs.rice.edu COMP 528 Lecture 9 15 February 2005 Goals for Today Uderstad how to Use scatter diagrams to ispect

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Sample Size Determination (Two or More Samples)

Sample Size Determination (Two or More Samples) Sample Sie Determiatio (Two or More Samples) STATGRAPHICS Rev. 963 Summary... Data Iput... Aalysis Summary... 5 Power Curve... 5 Calculatios... 6 Summary This procedure determies a suitable sample sie

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

The target reliability and design working life

The target reliability and design working life Safety ad Security Egieerig IV 161 The target reliability ad desig workig life M. Holický Kloker Istitute, CTU i Prague, Czech Republic Abstract Desig workig life ad target reliability levels recommeded

More information

Example 3.3: Rainfall reported at a group of five stations (see Fig. 3.7) is as follows. Kundla. Sabli

Example 3.3: Rainfall reported at a group of five stations (see Fig. 3.7) is as follows. Kundla. Sabli 3.4.4 Spatial Cosistecy Check Raifall data exhibit some spatial cosistecy ad this forms the basis of ivestigatig the observed raifall values. A estimate of the iterpolated raifall value at a statio is

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Orthogonal Gaussian Filters for Signal Processing

Orthogonal Gaussian Filters for Signal Processing Orthogoal Gaussia Filters for Sigal Processig Mark Mackezie ad Kiet Tieu Mechaical Egieerig Uiversity of Wollogog.S.W. Australia Abstract A Gaussia filter usig the Hermite orthoormal series of fuctios

More information

RAINFALL PREDICTION BY WAVELET DECOMPOSITION

RAINFALL PREDICTION BY WAVELET DECOMPOSITION RAIFALL PREDICTIO BY WAVELET DECOMPOSITIO A. W. JAYAWARDEA Departmet of Civil Egieerig, The Uiversit of Hog Kog, Hog Kog, Chia P. C. XU Academ of Mathematics ad Sstem Scieces, Chiese Academ of Scieces,

More information

Modified Ratio Estimators Using Known Median and Co-Efficent of Kurtosis

Modified Ratio Estimators Using Known Median and Co-Efficent of Kurtosis America Joural of Mathematics ad Statistics 01, (4): 95-100 DOI: 10.593/j.ajms.01004.05 Modified Ratio s Usig Kow Media ad Co-Efficet of Kurtosis J.Subramai *, G.Kumarapadiya Departmet of Statistics, Podicherry

More information

Available online Journal of Scientific and Engineering Research, 2017, 4(10): Research Article

Available online  Journal of Scientific and Engineering Research, 2017, 4(10): Research Article Available olie www.jsaer.co, 2017, 4(10):207-212 Research Article ISSN: 2394-2630 CODEN(USA): JSERBR Predictive Raifall Models for Ooku i Rivers State, Nigeria J. C. Ozota 1, C. Ukpaka 2 1 Leadway Ifrastructure

More information

Regression, Inference, and Model Building

Regression, Inference, and Model Building Regressio, Iferece, ad Model Buildig Scatter Plots ad Correlatio Correlatio coefficiet, r -1 r 1 If r is positive, the the scatter plot has a positive slope ad variables are said to have a positive relatioship

More information

Data Analysis and Statistical Methods Statistics 651

Data Analysis and Statistical Methods Statistics 651 Data Aalysis ad Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasii/teachig.html Suhasii Subba Rao Review of testig: Example The admistrator of a ursig home wats to do a time ad motio

More information

Algebra of Least Squares

Algebra of Least Squares October 19, 2018 Algebra of Least Squares Geometry of Least Squares Recall that out data is like a table [Y X] where Y collects observatios o the depedet variable Y ad X collects observatios o the k-dimesioal

More information

TMA4245 Statistics. Corrected 30 May and 4 June Norwegian University of Science and Technology Department of Mathematical Sciences.

TMA4245 Statistics. Corrected 30 May and 4 June Norwegian University of Science and Technology Department of Mathematical Sciences. Norwegia Uiversity of Sciece ad Techology Departmet of Mathematical Scieces Corrected 3 May ad 4 Jue Solutios TMA445 Statistics Saturday 6 May 9: 3: Problem Sow desity a The probability is.9.5 6x x dx

More information

Lecture 2: Monte Carlo Simulation

Lecture 2: Monte Carlo Simulation STAT/Q SCI 43: Itroductio to Resamplig ethods Sprig 27 Istructor: Ye-Chi Che Lecture 2: ote Carlo Simulatio 2 ote Carlo Itegratio Assume we wat to evaluate the followig itegratio: e x3 dx What ca we do?

More information

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ STATISTICAL INFERENCE INTRODUCTION Statistical iferece is that brach of Statistics i which oe typically makes a statemet about a populatio based upo the results of a sample. I oesample testig, we essetially

More information

Measurement uncertainty of the sound absorption

Measurement uncertainty of the sound absorption Measuremet ucertaity of the soud absorptio coefficiet Aa Izewska Buildig Research Istitute, Filtrowa Str., 00-6 Warsaw, Polad a.izewska@itb.pl 6887 The stadard ISO/IEC 705:005 o the competece of testig

More information

Three State Markov Chain Approach On the Behavior Of Rainfall

Three State Markov Chain Approach On the Behavior Of Rainfall New York Sciece Joural ;3() Three State Markov Chai Approach O the Behavior Of Raifall Vivek Kumar Garg ad Jai Bhagwa Sigh School of Basic ad Applied Scieces, Shobhit Uiversity, Meerut, Uttar Pradesh,

More information

On an Application of Bayesian Estimation

On an Application of Bayesian Estimation O a Applicatio of ayesia Estimatio KIYOHARU TANAKA School of Sciece ad Egieerig, Kiki Uiversity, Kowakae, Higashi-Osaka, JAPAN Email: ktaaka@ifokidaiacjp EVGENIY GRECHNIKOV Departmet of Mathematics, auma

More information

Tests of Hypotheses Based on a Single Sample (Devore Chapter Eight)

Tests of Hypotheses Based on a Single Sample (Devore Chapter Eight) Tests of Hypotheses Based o a Sigle Sample Devore Chapter Eight MATH-252-01: Probability ad Statistics II Sprig 2018 Cotets 1 Hypothesis Tests illustrated with z-tests 1 1.1 Overview of Hypothesis Testig..........

More information

6 Sample Size Calculations

6 Sample Size Calculations 6 Sample Size Calculatios Oe of the major resposibilities of a cliical trial statisticia is to aid the ivestigators i determiig the sample size required to coduct a study The most commo procedure for determiig

More information

Frequentist Inference

Frequentist Inference Frequetist Iferece The topics of the ext three sectios are useful applicatios of the Cetral Limit Theorem. Without kowig aythig about the uderlyig distributio of a sequece of radom variables {X i }, for

More information

Number of fatalities X Sunday 4 Monday 6 Tuesday 2 Wednesday 0 Thursday 3 Friday 5 Saturday 8 Total 28. Day

Number of fatalities X Sunday 4 Monday 6 Tuesday 2 Wednesday 0 Thursday 3 Friday 5 Saturday 8 Total 28. Day LECTURE # 8 Mea Deviatio, Stadard Deviatio ad Variace & Coefficiet of variatio Mea Deviatio Stadard Deviatio ad Variace Coefficiet of variatio First, we will discuss it for the case of raw data, ad the

More information

Continuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised

Continuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised Questio 1. (Topics 1-3) A populatio cosists of all the members of a group about which you wat to draw a coclusio (Greek letters (μ, σ, Ν) are used) A sample is the portio of the populatio selected for

More information

Bootstrap Intervals of the Parameters of Lognormal Distribution Using Power Rule Model and Accelerated Life Tests

Bootstrap Intervals of the Parameters of Lognormal Distribution Using Power Rule Model and Accelerated Life Tests Joural of Moder Applied Statistical Methods Volume 5 Issue Article --5 Bootstrap Itervals of the Parameters of Logormal Distributio Usig Power Rule Model ad Accelerated Life Tests Mohammed Al-Ha Ebrahem

More information

G. R. Pasha Department of Statistics Bahauddin Zakariya University Multan, Pakistan

G. R. Pasha Department of Statistics Bahauddin Zakariya University Multan, Pakistan Deviatio of the Variaces of Classical Estimators ad Negative Iteger Momet Estimator from Miimum Variace Boud with Referece to Maxwell Distributio G. R. Pasha Departmet of Statistics Bahauddi Zakariya Uiversity

More information

Teaching Mathematics Concepts via Computer Algebra Systems

Teaching Mathematics Concepts via Computer Algebra Systems Iteratioal Joural of Mathematics ad Statistics Ivetio (IJMSI) E-ISSN: 4767 P-ISSN: - 4759 Volume 4 Issue 7 September. 6 PP-- Teachig Mathematics Cocepts via Computer Algebra Systems Osama Ajami Rashaw,

More information

Assessment of extreme discharges of the Vltava River in Prague

Assessment of extreme discharges of the Vltava River in Prague Flood Recovery, Iovatio ad Respose I 05 Assessmet of extreme discharges of the Vltava River i Prague M. Holický, K. Jug & M. Sýkora Kloker Istitute, Czech Techical Uiversity i Prague, Czech Republic Abstract

More information

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Solutions Descriptive Statistics. None at all!

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Solutions Descriptive Statistics. None at all! ENGI 44 Probability ad Statistics Faculty of Egieerig ad Applied Sciece Problem Set Solutios Descriptive Statistics. If, i the set of values {,, 3, 4, 5, 6, 7 } a error causes the value 5 to be replaced

More information

1 of 7 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 6. Order Statistics Defiitios Suppose agai that we have a basic radom experimet, ad that X is a real-valued radom variable

More information

Confidence Intervals รศ.ดร. อน นต ผลเพ ม Assoc.Prof. Anan Phonphoem, Ph.D. Intelligent Wireless Network Group (IWING Lab)

Confidence Intervals รศ.ดร. อน นต ผลเพ ม Assoc.Prof. Anan Phonphoem, Ph.D. Intelligent Wireless Network Group (IWING Lab) Cofidece Itervals รศ.ดร. อน นต ผลเพ ม Assoc.Prof. Aa Phophoem, Ph.D. aa.p@ku.ac.th Itelliget Wireless Network Group (IWING Lab) http://iwig.cpe.ku.ac.th Computer Egieerig Departmet Kasetsart Uiversity,

More information

Recurrence Relations

Recurrence Relations Recurrece Relatios Aalysis of recursive algorithms, such as: it factorial (it ) { if (==0) retur ; else retur ( * factorial(-)); } Let t be the umber of multiplicatios eeded to calculate factorial(). The

More information

2 1. The r.s., of size n2, from population 2 will be. 2 and 2. 2) The two populations are independent. This implies that all of the n1 n2

2 1. The r.s., of size n2, from population 2 will be. 2 and 2. 2) The two populations are independent. This implies that all of the n1 n2 Chapter 8 Comparig Two Treatmets Iferece about Two Populatio Meas We wat to compare the meas of two populatios to see whether they differ. There are two situatios to cosider, as show i the followig examples:

More information

CONTENTS. Course Goals. Course Materials Lecture Notes:

CONTENTS. Course Goals. Course Materials Lecture Notes: INTRODUCTION Ho Chi Mih City OF Uiversity ENVIRONMENTAL of Techology DESIGN Faculty Chapter of Civil 1: Orietatio. Egieerig Evaluatio Departmet of mathematical of Water Resources skill Egieerig & Maagemet

More information

ESTIMATION AND PREDICTION BASED ON K-RECORD VALUES FROM NORMAL DISTRIBUTION

ESTIMATION AND PREDICTION BASED ON K-RECORD VALUES FROM NORMAL DISTRIBUTION STATISTICA, ao LXXIII,. 4, 013 ESTIMATION AND PREDICTION BASED ON K-RECORD VALUES FROM NORMAL DISTRIBUTION Maoj Chacko Departmet of Statistics, Uiversity of Kerala, Trivadrum- 695581, Kerala, Idia M. Shy

More information

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample. Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized

More information

Some Properties of the Exact and Score Methods for Binomial Proportion and Sample Size Calculation

Some Properties of the Exact and Score Methods for Binomial Proportion and Sample Size Calculation Some Properties of the Exact ad Score Methods for Biomial Proportio ad Sample Size Calculatio K. KRISHNAMOORTHY AND JIE PENG Departmet of Mathematics, Uiversity of Louisiaa at Lafayette Lafayette, LA 70504-1010,

More information

ST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n.

ST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n. ST 305: Exam 3 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad the basic

More information

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc. Chapter 22 Comparig Two Proportios Copyright 2010 Pearso Educatio, Ic. Comparig Two Proportios Comparisos betwee two percetages are much more commo tha questios about isolated percetages. Ad they are more

More information

Class 23. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

Class 23. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700 Class 23 Daiel B. Rowe, Ph.D. Departmet of Mathematics, Statistics, ad Computer Sciece Copyright 2017 by D.B. Rowe 1 Ageda: Recap Chapter 9.1 Lecture Chapter 9.2 Review Exam 6 Problem Solvig Sessio. 2

More information

Correlation Regression

Correlation Regression Correlatio Regressio While correlatio methods measure the stregth of a liear relatioship betwee two variables, we might wish to go a little further: How much does oe variable chage for a give chage i aother

More information

Extreme Value Charts and Analysis of Means (ANOM) Based on the Log Logistic Distribution

Extreme Value Charts and Analysis of Means (ANOM) Based on the Log Logistic Distribution Joural of Moder Applied Statistical Methods Volume 11 Issue Article 0 11-1-01 Extreme Value Charts ad Aalysis of Meas (ANOM) Based o the Log Logistic istributio B. Sriivasa Rao R.V.R & J.C. College of

More information

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals 7-1 Chapter 4 Part I. Samplig Distributios ad Cofidece Itervals 1 7- Sectio 1. Samplig Distributio 7-3 Usig Statistics Statistical Iferece: Predict ad forecast values of populatio parameters... Test hypotheses

More information

A goodness-of-fit test based on the empirical characteristic function and a comparison of tests for normality

A goodness-of-fit test based on the empirical characteristic function and a comparison of tests for normality A goodess-of-fit test based o the empirical characteristic fuctio ad a compariso of tests for ormality J. Marti va Zyl Departmet of Mathematical Statistics ad Actuarial Sciece, Uiversity of the Free State,

More information

Rainfall-Runoff Modelling using Modified NRCS-CN,RS and GIS -A Case Study

Rainfall-Runoff Modelling using Modified NRCS-CN,RS and GIS -A Case Study P.Sudara Kumar It. Joural of Egieerig Research ad Applicatios RESEARCH ARTICLE OPEN ACCESS Raifall-Ruoff Modellig usig Modified NRCS-CN,RS ad GIS -A Case Study P.Sudara Kumar*, T.V.Pravee**, M.A.Prasad***

More information

Discrete Orthogonal Moment Features Using Chebyshev Polynomials

Discrete Orthogonal Moment Features Using Chebyshev Polynomials Discrete Orthogoal Momet Features Usig Chebyshev Polyomials R. Mukuda, 1 S.H.Og ad P.A. Lee 3 1 Faculty of Iformatio Sciece ad Techology, Multimedia Uiversity 75450 Malacca, Malaysia. Istitute of Mathematical

More information

Stat 319 Theory of Statistics (2) Exercises

Stat 319 Theory of Statistics (2) Exercises Kig Saud Uiversity College of Sciece Statistics ad Operatios Research Departmet Stat 39 Theory of Statistics () Exercises Refereces:. Itroductio to Mathematical Statistics, Sixth Editio, by R. Hogg, J.

More information

Estimation of Population Mean Using Co-Efficient of Variation and Median of an Auxiliary Variable

Estimation of Population Mean Using Co-Efficient of Variation and Median of an Auxiliary Variable Iteratioal Joural of Probability ad Statistics 01, 1(4: 111-118 DOI: 10.593/j.ijps.010104.04 Estimatio of Populatio Mea Usig Co-Efficiet of Variatio ad Media of a Auxiliary Variable J. Subramai *, G. Kumarapadiya

More information

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc. Chapter 22 Comparig Two Proportios Copyright 2010, 2007, 2004 Pearso Educatio, Ic. Comparig Two Proportios Read the first two paragraphs of pg 504. Comparisos betwee two percetages are much more commo

More information

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter Time Respose & Frequecy Respose d -Order Dyamic System -Pole, Low-Pass, Active Filter R 4 R 7 C 5 e i R 1 C R 3 - + R 6 - + e out Assigmet: Perform a Complete Dyamic System Ivestigatio of the Two-Pole,

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 9

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 9 Hypothesis testig PSYCHOLOGICAL RESEARCH (PYC 34-C Lecture 9 Statistical iferece is that brach of Statistics i which oe typically makes a statemet about a populatio based upo the results of a sample. I

More information