A Generalized Class of Dual to Product-Cum-Dual to Ratio Type Estimators of Finite Population Mean In Sample Surveys

Size: px
Start display at page:

Download "A Generalized Class of Dual to Product-Cum-Dual to Ratio Type Estimators of Finite Population Mean In Sample Surveys"

Transcription

1 Appl Math If Sc Lett 4 o 5-33 (6) 5 Appled Mathematcs & Ifmato Sceces Letters A Iteratoal Joural A Geeralzed lass of Dual to Product-um-Dual to Rato Tpe stmats of Fte Populato Mea I Sample Surves Housla P Sgh Sura K Pal ad Vshal Mehta School of Studed Statstcs Vkram Uverst Ujja M P Ida Receved: 3 Jul 5 Revsed: 7 Aug 5 Accepted: 8 Aug 5 Publshed ole: Ja 6 Abstract: I ths paper we have suggested a geeral class of dual to product-cum-dual to rato tpe estmats of fte populato mea usg a aular varable that s crelated wth the varable of terest The proposed class of estmats cludes several kow estmats based o trasfmato aular varable The bas ad mea squared err () epressos of the proposed class of estmats have bee obtaed to the frst degree of appromato We have compared the proposed class of dual to product-cum- dual to rato tpe estmats of fte populato mea to varous estg rato product ad rato-cum-product tpe estmats ad show that the suggested class of estmats s better tha other estg estmats uder some realstc codtos Kewds: Aular varable Dual to product-cum-dual to rato tpe estmat Fte populato mea Smple radom samplg as Mea squared err Itroducto It s well establshed fact that sample surves aular fmato s ofte used to mprove the precso of estmats of populato parameters The use of aular fmato at the estmato stage appears to have started wth the wk of ochra (94) He evsaged the rato estmat to estmate the populato mea total of the stud varate b usg supplemetar fmato o a aular varate postvel crelated wth The rato estmat s most effectve whe the relatoshp betwee stud varate ad aular varate s lear passg through the g ad the mea square err of s proptoal to Whe the aular varable s egatvel crelated wth the stud varate Robso (957) ad Murth (964) proposed the product estmat of the populato mea total I fact f the better utlzato of fmato o a aular varate Murth (964) has suggested the use of Rato estmat R f / / Product estmat P f / / Ubased estmat f / / / Where ) are coeffcets of varato of ( ( ) respectvel ad s the crelato coeffcet betwee ad respectvel osder a fte populato U U U U uts A sample of sze of s draw usg smple radom samplg wthout replacemet (SRSWOR) method to estmate the populato mea of the stud varate Let the sample meas be the ubased estmats of the populato meas respectvel based o observatos The classcal rato ad product estmats of populato mea are respectvel gve b R () P () The bases ad mea squared errs (s) of R ad P to the frst degree of appromato are respectvel gve b respodg auth e-mal: surakatpal6676@gmalcom 6 SP atural Sceces Publshg

2 6 R P f K (3) f K (4) where R f K f K (5) f K (6) P S S S S ( ) S ad osder a trasfmato S S S ( )( ) ( ) ( ) g g where g /( ) The ( g) g ubased estmat f betwee ad s a ad the crelato s egatve Usg the trasfmato Srvekataramaa (98) ad adopadhaa (98) obtaed dual to rato ad dual to product estmats respectvel as (7) R P (8) The bases ad mea squared errs (s) of R ad P to the frst degree of appromato are respectvel gve as f R gk (9) f P f g g K () R g g K () f P g g K () Sgh ad Aghotr (8) defed a faml of productcum-rato estmats of populato mea smple radom samplg (SRS) as a b a b (3) a b a b H P Sgh et al: A geeralzed class of dual to where a ad b are kow characterzg postve scalars ad s a real costat to be determed such that the of s mmum The bas ad of to the frst degree of appromato are respectvel gve as f K K (4) where f a a b K (5) The am of ths paper s to suggest a geeralsed class of dual to product-cum-dual to rato estmats f populato mea SRSWOR ad ther propertes are studed uder large sample appromato It s show that the proposed dual to product-cum dual to rato estmat cludes several kow estmats based o trasfmato aular varable A emprcal stud s carred out to dscuss the supert of the proposed class of estmats A Geeralzed lass of Dual to Product- um- Dual to Rato Tpe stmats of Fte Populato Mea We suggest a class of dual to product-cum-dual to rato tpe estmats SRSWOR f populato mea as a b a b a b a b () where ( a b) are same as defed earler beg a sutabl chose scalar ad g g wth g To obta the bas ad of appromato we wrte ad e e such that e e ad f e f e e e to the frst degree of () f K Table shows the members of the proposed class of estmats f dfferet choces of ( a b ) I Table ad respectvel are kow coeffcet of varato ad coeffcet of kurtoss respectvel of a aular varable 6 SP atural Sceces Publshg

3 Appl Math If Sc Lett 4 o 5-33 (6) / 7 Table : Members of the estmat S o stmats R Dual to rato estmat P Dual to product estmat 3 SD Sgh ad Upadhaa (986) estmat 4 SK Dual to Sgh et al (4) estmat UP 5 Dual to Upadhaa ad Sgh (999) rato-tpe estmat UP Dual to Upadhaa ad Sgh (999) product-tpe estmat UP 3 Dual to Upadhaa ad Sgh (999) rato-tpe estmat UP 4 Upadhaa ad Sgh (999) product-tpe estmat S haudhar ad Sgh () estmat a b SA a b Dual to Shah ad Patel (984) ad Sgh ad Aghotr (8) rato-tpe estmat a b 4 SA a b Dual to Sgh ad Aghotr (8) product-tpe estmat 5 TS pressg () terms of e' s we have f dfferet choces of Dual to Tal ad Sharma (9) estmat a ( a b ) Values of costats b ( a b ) a b a b a a b a a b ( e )[ ( ge ) ( )( ge )] (3) We assume that ge so that ge s epadable From (3) we have 6 SP atural Sceces Publshg

4 8 e [ ( ge g e ) ge e ge g e e ge ge e g e eglectg terms of e s havg power greater tha two we have [ e ge ge e g e ] [ e ge ge e g e ] (4) Takg epectato of both the sdes of (4) we obta the bas of to the frst degree of appromato as f f The bas of gk g f g K g s almost ubased whe g K g ] (5) e K K g Squarg both sdes of (4) ad eglectg terms of e s havg power greater tha two we have e g e g e e (6) Takg epectato of both sdes of (6) we get the of to the frst degree of appromato as f g g K (7) whch s mmzed whe K opt sa g (8) Substtutg the value of () elds the opt asmptotcall optmum estmat (AO) as a b a b opt opt opt (9) a b a b Thus the resultg bas ad of respectvel opt are respectvel gve b f g K m opt opt g () opt f () opt 3 ffcec omparso () Uder SRSWOR the varace of sample mea s H P Sgh et al: A geeralzed class of dual to f Var (3) From (7) ad (3) t s foud that the proposed dual to product-cum-dual to rato tpe estmat effcet tha f Var f f g f Ths codto holds f ether ad ad s me g K g g K K g K g (3) Therefe the rage of uder whch the proposed dual to product-cum-dual to rato tpe estmat effcet tha s s me K K m ma (33) g g () The s of dual to Shah ad Patel (984) ad Sgh ad Aghotr (8) rato(product) tpe estmat as f SA SA s obtaed b puttg g g K (34) SA f SA g g K (35) From (7) ad (34) t s foud that the proposed dual to product-cum-dual to rato tpe estmat s me effcet tha dual to Shah ad Patel (984) ad Sgh ad Aghotr (8) class of rato estmats f f SA Ths codto holds f g SA f g g K 4g g K g K ether K g K (36) g Therefe the rage of uder whch the proposed dual to product-cum-dual to rato tpe estmat s me effcet tha dual to Shah ad Patel (984) ad Sgh ad Aghotr (8) class of rato estmats f SA K K m ma (37) g g () From (7) ad (35) t s foud that the proposed dual to product-cum-dual to rato tpe estmat s 6 SP atural Sceces Publshg

5 Appl Math If Sc Lett 4 o 5-33 (6) / 9 me effcet tha dual to Sgh ad Aghotr (8) class of product estmats SA f SA Ths codto holds f f g f g g 4g g K g K K ther K K g (38) g Therefe the rage of uder whch the proposed dual to product-cum-dual to rato tpe estmat s me effcet tha dual to Sgh ad Aghotr (8) class of product estmats f SA K K m ma (39) g g Remark 3 The codtos whch suggested dual to product-cum-dual to rato tpe estmat s better tha other estg estmats (dscussed Table ) ca be easl obtaed b puttg dfferet values of a b 4 Illustrato () To llustrate the geeral results we cosder a b a b () we get a class F of estmats f populato mea (4) where ad as are same as defed earler The bas ad of to the large sample appromato are respectvel gve as ( f ) g K g (4) f g g K F we get a estmat f as (43) (44) whch s due to Shah ad Patel (984) ad Sgh ad Upadhaa (986) We ote that the estmat s dual to Ssoda ad Dwved s (98) rato-tpe estmat F reduces to the estmat f as whch s dual to product tpe estmat (45) The bases ad s of ad to the frst degree of appromato are respectvel gve as f g (46) K f g g K (47) f g g K (48) g g K (49) f It s observed from (43) that ( ) Var( ) f ( ) K ether g (4) ( ) K g From () ad (43) we see that the proposed class of estmat ) wll domate over ( Srvekataramaa (98) estmat R K ether g (4) K g From (43) ad (48) we ote that ( ) ( ) f K( ) ether g (4) K( ) g Further from (43) ad (49) we have that ( ) ( ) f K( ) ether g (43) K( ) g () To llustrate the geeral results we cosder a b S a b () we get a F S class of estmats f populato mea as f 6 SP atural Sceces Publshg

6 3 S S S S (44) where ad The bas ad of are same as defed earler to the frst degree of appromato are respectvel gve as f g K g (45) g f g F (46) K we get a estmat f S S as (47) whch s due to Shah ad Gupta (986) We ote that the estmat s dual to Shah ad Patel (984) rato tpe estmat F reduces to the estmat f as S (48) S whch s dual to product-tpe estmat The bases ad s of ad to the frst degree of appromato are respectvel gve as f g K (49) f g g K (4) f g g K (4) f g g K (4) We ote from (46) that the proposed class of estmats s me effcet tha the usual ubased estmat f ( ) K ether g (43) ( ) K g It follows from () ad (46) that the suggested class of estmats s better tha Srvekataramaa (98) estmat R f H P Sgh et al: A geeralzed class of dual to K ether g K g From (46) ad (4) that ( ) ( ) f K( ) ether g K( ) g (44) (45) Further t observed from (46) ad (4) that ( ) ( f ) K( ) ether g K( ) g 5 mprcal Stud (46) To llustrate the perfmace of the proposed class of estmats ad over usual ubased estmat rato estmat R product estmat P ad we cosder two atural populato data sets Populato I: (Itala bureau f the evrometal protecto-apat Waste 4) =Total amout (tos) of recclable-waste collecto Ital 3 ad = amout (tos) of recclable-waste collecto Ital Populato II: (Itala bureau f the evrometal protecto- APAT Waste 4) =Total amout (tos) of recclable-waste collecto Ital 3 ad = umber of habtats We have computed the percet relatve effceces (s) of the estmats ad wth respect to R ad b usg the followg fmulae: 6 SP atural Sceces Publshg

7 Appl Math If Sc Lett 4 o 5-33 (6) / 3 ( ) g( ) ( ) g K (5) ( [ ( )] g g K R ) g( ) ( ) g K g ) g K g( ) ( ) g K 3 ( ( ) ( ) g ( ) g K (5) (53) (54) [ ( )] g g K (55) ( R ) ( ) g ( ) g K (56) g 3 ( ) g K ( ) g ( ) g K Table 5 shows the percet relatve effceces (s) of ad wth respect to the usual ubased estmat f dfferet value of Table 5: Rage of estmats stmat s better tha whch the proposed class of R ad Rage of Populato I Populato II (-77 5) (-689 5) R (-6 -) (-88 -) (-7 ) (-89 ) We ote that the crelato betwee stud varable ad s postve f both the populato data sets I ad II Owg to ths the proposed classes of estmats are comparable wth usual ubased estmat dual to rato estmat R ad Upadhaa (986) estmat Gupta (986) estmat Table 5: Rage of estmats stmat R ad Shah ad Patel (984) ad Sgh s better tha ad Shah ad whch the proposed class of R ad Rage of Populato I Populato II (-498 5) (-48 5) ( ) ( ) (-448 ) (-358 ) Tables 5 ad 5 show that there s eough scope of selectg the value of scalar obtag estmats R better tha ad That s eve f the devates from ts true optmum value scalar opt cosderable ga effcec b usg proposed classes of estmats ad over other estg estmats ca be obtaed Largest ga effcec s observed at the optmum value Tables 53 ad 54 ehbt opt of that there s apprecable ga effcec b usg the proposed class of estmats = ; over usual ubased estmat Srvekataramaa s (98) dual to rato estmat Upadhaa (986) estmat R Shah ad Patel (984) ad Sgh ad ad Shah ad Gupta (986) estmat Fdgs closed Tables 5 to 54 are the favour of proposed classes of estmats ad Thus we recommed the proposed classes of estmats ad f ther use practce 6 SP atural Sceces Publshg

8 3 H P Sgh et al: A geeralzed class of dual to Table 53: s of the proposed class of estmats Populato I 3 wth respect to R ad f dfferet values of Populato II Stads f s less tha % Table 54: s of the proposed class of estmats Populato I 3 wth respect to R ad Populato II f dfferet values of Stads f s less tha % 6 ocluso Refereces 3 Ths paper proposed a geeral class of dual to product-cumdual to rato tpe estmat estmat based o the trasfmato a aular varable smple radom samplg The evsaged class of estmats cludes several kow estmats based o trasfmato aular varable The bas ad epressos of the proposed class of estmats have bee obtaed uder large sample appromato It s terestg to ote that ths stud ufes several estmats wth ther propertes at oe place mprcal stud also shows the supert of the proposed class of estmats over other estg estmats Ackowledgemets The auths are thakful to the dt--hef ad to the aomous leared referees f hs valuable suggestos regardg mprovemet of the paper [] adopadhaa S (98): Improved rato ad product estmats Sakha 4() [] haudhar S ad Sgh K (): A effcet class of dual to product-cum-dual to rato estmat of fte populato mea sample surves Global Joural of Scece Froter Research (3) 5-33 [3] ochra W G (94): Some propertes of estmats based o samplg scheme wth varg probabltes Australa Joural of Statstcs 7-8 [4] Murth M (964): Product method of estmato Sakha [5] Robso D S (957): Applcatos of multvarate polkas to the the of ubased rato-tpe estmato Joural of the Amerca Statstcal Assocato SP atural Sceces Publshg

9 Appl Math If Sc Lett 4 o 5-33 (6) / 33 [6] Shah D ad Gupta M R (986): A geeral modfed dual rato estmat Gujarat Statstcal Revew 3 () 4-5 [7] Shah S M ad Patel H R (984): ew modfed rato estmat usg coeffcet of varato of aular varable Gujarat Statstcal Revew [8] Sgh H P ad Aghotr (8): A geeral procedure of estmatg populato mea usg aular fmato sample surves Statstcs Trasto- ew seres [9] Sgh H P ad Upadhaa L (986): A dual to modfed rato estmat usg coeffcet of varato of aular varable Proceedgs of the atoal Academ of Sceces Ida 56 (A) [] Sgh H P Tal R Tal R ad Kakra M S (4): A mproved estmat of populato mea usg power trasfmato Joural of Ida Socet Agrculture Statstcs 58() 3-3 [] Ssoda VS ad Dwved VK (98): A modfed rato estmat usg coeffcet of varato of aular varable Joural of Ida Socet Agrculture Statstcs [] Srvekataramaa T (98): A dual to rato estmat sample surves ometrka [3] Tal R ad Sharma (9): A modfed rato-cumproduct estmat of fte populato mea usg kow coeffcet of varato ad coeffcet of kurtoss Statstcs Trasto- ew seres 5-4 [4] Upadhaa L ad Sgh H P (999): Use of trasfmed aular varable estmatg the fte populato mea ometrcal Joural Housla P Sgh (Dea Facult of Scece) Profess School of Studed Statstcs Vkram Uverst Ujja MP Ida Hs research terests are the areas of Statstcs Samplg The ad Statstcal Iferece Sura K Pal Research Scholar Pursug PhD(Statstcs) School of Studed Statstcs Vkram Uverst Ujja M P Ida Vshal Mehta Research Scholar Pursug PhD(Statstcs) School of Studed Statstcs Vkram Uverst Ujja M P Ida 6 SP atural Sceces Publshg

A Generalized Class of Ratio-Cum-Dual to Ratio Estimators of Finite Population Mean Using Auxiliary Information in Sample Surveys

A Generalized Class of Ratio-Cum-Dual to Ratio Estimators of Finite Population Mean Using Auxiliary Information in Sample Surveys Math Sc Lett 5 o 3- (6) 3 Mathematcal Sceces Letters A Iteratoal Joural http://ddog/8576/msl/55 A Geeralzed lass of ato-um-dual to ato Estmats of Fte Populato Mea Usg Aular Ifmato Sample Surves Housla

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

Sampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION

Sampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION Samplg Theor MODULE V LECTUE - 4 ATIO AND PODUCT METHODS OF ESTIMATION D. SHALABH DEPATMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPU A mportat objectve a statstcal estmato procedure

More information

ADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE

ADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE Joural o Mathematcs ad tatstcs 9 (3): 49-55, 03 I: 549-3644 03 cece Publcatos do:0.3844/jmssp.03.49.55 Publshed Ole 9 (3) 03 (http://www.thescpub.com/jmss.toc) ADAPTIVE CLUTER AMPLIG UIG AUXILIARY VARIABLE

More information

Department of Statistics, Banaras Hindu University Varanasi , India 2 Chair of Department of Mathematics, University of New Mexico, Gallup, USA

Department of Statistics, Banaras Hindu University Varanasi , India 2 Chair of Department of Mathematics, University of New Mexico, Gallup, USA A Famly of eda Based Estmators Smple Radom Samplg Hemat K.Verma, Rajesh Sgh ad Floret Smaradache Departmet of Statstcs, Baaras Hdu Uversty Varaas-5, Ida har of Departmet of athematcs, Uversty of e exco,

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

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

Applied Mathematics and Computation

Applied Mathematics and Computation Appled Mathematcs ad Computato 215 (2010) 4198 4202 Cotets lsts avalable at SceceDrect Appled Mathematcs ad Computato joural homepage: www.elsever.com/locate/amc Improvemet estmatg the populato mea smple

More information

Chapter -2 Simple Random Sampling

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

More information

Chapter -2 Simple Random Sampling

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

More information

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

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

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

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

More information

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch

More information

Chapter 11 Systematic Sampling

Chapter 11 Systematic Sampling Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of

More information

ON ESTIMATION OF POPULATION MEAN IN THE PRESENCE OF MEASUREMENT ERROR AND NON-RESPONSE

ON ESTIMATION OF POPULATION MEAN IN THE PRESENCE OF MEASUREMENT ERROR AND NON-RESPONSE Pak. J. Statst. 015 ol. 31(5), 657-670 ON ESTIMATION OF POPLATION MEAN IN THE PRESENCE OF MEASREMENT ERROR AND NON-RESPONSE Muhammad Azeem 1 ad Muhammad Haf Natoal College of Busess Admstrato & Ecoomcs,

More information

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

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

More information

Lecture Notes Forecasting the process of estimating or predicting unknown situations

Lecture Notes Forecasting the process of estimating or predicting unknown situations Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg

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

Sampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING)

Sampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING) Samplg Theory ODULE X LECTURE - 35 TWO STAGE SAPLIG (SUB SAPLIG) DR SHALABH DEPARTET OF ATHEATICS AD STATISTICS IDIA ISTITUTE OF TECHOLOG KAPUR Two stage samplg wth uequal frst stage uts: Cosder two stage

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

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

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

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

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

More information

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

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

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

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

General Families of Estimators for Estimating Population Mean in Stratified Random Sampling under Non-Response

General Families of Estimators for Estimating Population Mean in Stratified Random Sampling under Non-Response J. tat. Appl. Pro. Lett. 3, No., 7-7 06 7 Joural of tatstcs Applcatos & Probablty Letters A Iteratoal Joural http://dx.do.org/0.8576/jsapl/0300 Geeral Famles of Estmators for Estmatg Populato Mea tratfed

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

Lecture 2: The Simple Regression Model

Lecture 2: The Simple Regression Model Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

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

More information

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

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

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

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

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

STRATIFIED SAMPLING IN AGRICULTURAL SURVEYS

STRATIFIED SAMPLING IN AGRICULTURAL SURVEYS 3 STRATIFIED SAMPLIG I AGRICULTURAL SURVEYS austav Adtya Ida Agrcultural Statstcs Research Isttute, ew Delh-00 3. ITRODUCTIO The prme objectve of a sample survey s to obta fereces about the characterstc

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

MEASURES OF DISPERSION

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

More information

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

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

9.1 Introduction to the probit and logit models

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

More information

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

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

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

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

Further Results on Pair Sum Labeling of Trees

Further Results on Pair Sum Labeling of Trees Appled Mathematcs 0 70-7 do:046/am0077 Publshed Ole October 0 (http://wwwscrporg/joural/am) Further Results o Par Sum Labelg of Trees Abstract Raja Poraj Jeyaraj Vjaya Xaver Parthpa Departmet of Mathematcs

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

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

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a

More information

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

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

More information

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

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

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

More information

Chapter Two. An Introduction to Regression ( )

Chapter Two. An Introduction to Regression ( ) ubject: A Itroducto to Regresso Frst tage Chapter Two A Itroducto to Regresso (018-019) 1 pg. ubject: A Itroducto to Regresso Frst tage A Itroducto to Regresso Regresso aalss s a statstcal tool 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

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

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

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

Midterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uverst Mchael Bar Sprg 5 Mdterm xam, secto Soluto Thursda, Februar 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes exam.. No calculators of a kd are allowed..

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

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

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

More information

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

22 Nonparametric Methods.

22 Nonparametric Methods. 22 oparametrc Methods. I parametrc models oe assumes apror that the dstrbutos have a specfc form wth oe or more ukow parameters ad oe tres to fd the best or atleast reasoably effcet procedures that aswer

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

Median as a Weighted Arithmetic Mean of All Sample Observations

Median as a Weighted Arithmetic Mean of All Sample Observations Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of

More information

Chapter 5 Properties of a Random Sample

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

More information

Class 13,14 June 17, 19, 2015

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

More information

Chapter 10 Two Stage Sampling (Subsampling)

Chapter 10 Two Stage Sampling (Subsampling) Chapter 0 To tage amplg (usamplg) I cluster samplg, all the elemets the selected clusters are surveyed oreover, the effcecy cluster samplg depeds o sze of the cluster As the sze creases, the effcecy decreases

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

UNIT 4 SOME OTHER SAMPLING SCHEMES

UNIT 4 SOME OTHER SAMPLING SCHEMES UIT 4 SOE OTHER SAPLIG SCHEES Some Other Samplg Schemes Structure 4. Itroducto Objectves 4. Itroducto to Systematc Samplg 4.3 ethods of Systematc Samplg Lear Systematc Samplg Crcular Systematc Samplg Advatages

More information

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING Joural of tatstcs: Advaces Theory ad Alcatos Volume 5, Number, 6, Pages 3- Avalable at htt://scetfcadvaces.co. DOI: htt://d.do.org/.864/jsata_7678 TRONG CONITENCY FOR IMPLE LINEAR EV MODEL WITH v/ -MIXING

More information

4. Standard Regression Model and Spatial Dependence Tests

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

More information

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

MAX-MIN AND MIN-MAX VALUES OF VARIOUS MEASURES OF FUZZY DIVERGENCE

MAX-MIN AND MIN-MAX VALUES OF VARIOUS MEASURES OF FUZZY DIVERGENCE merca Jr of Mathematcs ad Sceces Vol, No,(Jauary 0) Copyrght Md Reader Publcatos wwwjouralshubcom MX-MIN ND MIN-MX VLUES OF VRIOUS MESURES OF FUZZY DIVERGENCE RKTul Departmet of Mathematcs SSM College

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

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

Chapter 4 Multiple Random Variables

Chapter 4 Multiple Random Variables Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:

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

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01 ECO 745, Homework 6 Le Cabrera. Assume that the followg data come from the lear model: ε ε ~ N, σ,,..., -6. -.5 7. 6.9 -. -. -.9. -..6.4.. -.6 -.7.7 Fd the mamum lkelhood estmates of,, ad σ ε s.6. 4. ε

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

Generalized Minimum Perpendicular Distance Square Method of Estimation

Generalized Minimum Perpendicular Distance Square Method of Estimation Appled Mathematcs,, 3, 945-949 http://dx.do.org/.436/am..366 Publshed Ole December (http://.scrp.org/joural/am) Geeralzed Mmum Perpedcular Dstace Square Method of Estmato Rezaul Karm, Morshed Alam, M.

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

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

STATISTICAL INFERENCE

STATISTICAL INFERENCE (STATISTICS) STATISTICAL INFERENCE COMPLEMENTARY COURSE B.Sc. MATHEMATICS III SEMESTER ( Admsso) UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION CALICUT UNIVERSITY P.O., MALAPPURAM, KERALA, INDIA -

More information

EFFICIENT ESTIMATOR IN SUCCESSIVE SAMPLING USING POST-STRATIFICATION

EFFICIENT ESTIMATOR IN SUCCESSIVE SAMPLING USING POST-STRATIFICATION EFFICIET ETIMATOR I UCCEIVE AMPLIG UIG POT-TRATIFICATIO M. Trved* ad D. hula ** ABTRACT It s ofte see that a populato havg large umber of elemets remas uchaged several occasos but the value of uts chages.

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

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

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

More information

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

Bivariate Vieta-Fibonacci and Bivariate Vieta-Lucas Polynomials

Bivariate Vieta-Fibonacci and Bivariate Vieta-Lucas Polynomials IOSR Joural of Mathematcs (IOSR-JM) e-issn: 78-78, p-issn: 19-76X. Volume 1, Issue Ver. II (Jul. - Aug.016), PP -0 www.osrjourals.org Bvarate Veta-Fboacc ad Bvarate Veta-Lucas Polomals E. Gokce KOCER 1

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

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

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

More information

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

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

More information

SMALL AREA ESTIMATION USING NATURAL EXPONENTIAL FAMILIES WITH QUADRATIC VARIANCE FUNCTION (NEF-QVF) FOR BINARY DATA 1

SMALL AREA ESTIMATION USING NATURAL EXPONENTIAL FAMILIES WITH QUADRATIC VARIANCE FUNCTION (NEF-QVF) FOR BINARY DATA 1 SMALL AREA ESIMAION USING NAURAL EXPONENIAL FAMILIES WIH QUADRAIC VARIANCE FUNCION NEF-QVF FOR BINARY DAA Ksmat Departmet of Mathematcs Educato,,Yogakarta State Uverst Karagmalag, Yogakarta 558, Idoesa

More information

UNIT 7 RANK CORRELATION

UNIT 7 RANK CORRELATION UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7

More information

02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES

02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES 0/5/04 ITERESTIG FIITE AD IFIITE PRODUCTS FROM SIMPLE ALGEBRAIC IDETITIES Thomas J Osler Mathematcs Departmet Rowa Uversty Glassboro J 0808 Osler@rowaedu Itroducto The dfferece of two squares, y = + y

More information

ENGI 3423 Simple Linear Regression Page 12-01

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

More information

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

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

More information

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model ECON 48 / WH Hog The Smple Regresso Model. Defto of the Smple Regresso Model Smple Regresso Model Expla varable y terms of varable x y = β + β x+ u y : depedet varable, explaed varable, respose varable,

More information

GOALS The Samples Why Sample the Population? What is a Probability Sample? Four Most Commonly Used Probability Sampling Methods

GOALS The Samples Why Sample the Population? What is a Probability Sample? Four Most Commonly Used Probability Sampling Methods GOLS. Epla why a sample s the oly feasble way to lear about a populato.. Descrbe methods to select a sample. 3. Defe ad costruct a samplg dstrbuto of the sample mea. 4. Epla the cetral lmt theorem. 5.

More information