The New Mathematical Models for Inventory Management under Uncertain Market
|
|
- Robert Sparks
- 6 years ago
- Views:
Transcription
1 Research Joural of Appled Sceces, Egeerg ad Techology 4(3): , 0 ISSN: Maxwell Scetfc Orgazato, 0 Submtted: March 03, 0 Accepted: March 4, 0 Publshed: December 0, 0 The New Mathematcal Models for Ivetory Maagemet uder Ucerta Market A. Mrzazadeh Departmet of Idustral Egeerg, Islamc Azad Uversty, Karaj Brach, Karaj, Ira Abstract: Ths paper presets the ew mathematcal model for determg the optmal orderg polcy for dustral ad commercal compaes. I the prevous research, the umerous vetory models uder flatoary codtos have bee developed. I these models, the demad rate, usually, has bee cosdered costat ad well kow, tme-varyg, stock depedet or prce-depedet. But, the demad rate, usually, s ucerta the real world. Therefore, ths study, the ew flatoary vetory models uder stochastc demad codtos have bee developed. The vetory system s the state of mult-tems wth budget costrat. The umercal examples have also bee gve to llustrate ad valdate the theoretcal results. Keywords: Budget costrat, flato, vetory systems, stochastc market demad INTRODUTION Oe of the most mportat parts of Supply ha Maagemet (SM) s vetory system maagemet whch s heretly o-determstc stuato. The may departmets of orgazato such as warehouse, marketg, sale, purchasg, facal, plag, producto, mateace ad etc. are relevace to the vetory problem. The problem of vetory systems uder flatoary codtos has receved atteto recet years. Sce 975 a seres of related papers appeared that cosdered the effects of flato o the vetory system. Before the 990s, the earler efforts have bee cosdered smple stuatos. Buzacott (975) made a Ecoomc Order Quatty (EOQ) model wth flato subject to dfferet types of prcg polces. Msra (979) developed a dscouted-cost model ad cluded teral (compay) ad exteral (geeral ecoomy) flato rates for varous costs assocated wth a vetory system. Ivetored goods ca be broadly classfed to four meta-categores. Frst, obsolescece whch refers to tems those lose ther value through tme due to rapd chages of techology or the troducto of a ew product by a compettor. Secod, deterorato tems refer to the damage, spolage, dryess, vaporzato, etc. of the products. Products such as vegetables, fsh, medce, blood, gasole ad radoactve chemcals have a fte shelf lfe, ad start to deterorate oce they are produced. Thrd, amelorato refers to tems whose value or utlty or quatty crease wth tme. Fourth, tems wth o obsolescece, o deterorato ad o amelorato. If the rate of obsolescece, deterorato or amelorato s ot suffcetly low, ts mpact o modelg of such a vetory system caot be gored. There are a few papers for obsolescg ad ameloratg tems. Moo et al. (005) cosdered the ameloratg/deteroratg tems o a vetory model wth a tme-varyg demad patter. Aother research for ameloratg tems has bee doe by Saa (00). The o obsolescg, deteroratg ad ameloratg tems have bee cosdered some researches o the flatoary vetory system. Sarker ad Pa (994) surveyed the effects of flato ad the tme value of moey o order quatty wth fte repleshmet rate. Some efforts were exteded the prevous works to cosder more complex ad realstc assumpto, such as Uthayakumar ad Geetha (009), Maty (00), Vrat ad Padmaabha (990), Datta ad Pal (99), Harga (995), Harga ad Be-Daya (996) ad hug (003). The deteroratg vetory systems have bee studed cosderably the recet years. For example, hug ad Tsa (00) preseted a vetory model for deteroratg tems wth the demad of lear tred cosderg the tme-value of moey. Wee ad Law (00) derved a deteroratg vetory model uder flatoary codtos whe the demad rate s a lear decreasg fucto of the sellg prce. he ad L (00) dscussed a vetory model for deteroratg tems wth a ormally dstrbuted shelf lfe, cotuous tme-varyg demad, ad shortages uder a flatoary ad tme dscoutg evromet. Yag (004, 006) dscussed the two-warehouse vetory problem for deteroratg tems wth a costat demad rate ad shortages. hag (004) establshed a deteroratg EOQ model whe the suppler offers a permssble delay to the purchaser f the order quatty s greater tha or equal to a predetermed quatty. Mat et al. (006) proposed a vetory model wth stock-depedet demad rate ad two storage facltes uder flato ad tme value of moey. Lo et al. (007) developed a tegrated producto-vetory model wth assumptos of varyg rate of deterorato, partal backorderg, flato, mperfect producto processes 5034
2 Res. J. Appl. Sc. Eg. Techol., 4(3): , 0 ad multple delveres. A Two storage vetory problem wth dyamc demad ad terval valued lead-tme over a fte tme horzo uder flato ad tme-value of moey cosdered by Dey et al. (008). Other efforts o flatoary vetory systems for deteroratg tems have bee made by Hseh ad Dye (00), Su et al. (996), he (998), Wee ad Law (999), Sarker et al. (000), Yag et al. (00, 00), Lao ad che (003), Balkh (004a, 004b), Hou ad L (004), Hou (006), Jagg et al. (006), her et al. (008), Sarkar ad Moo (0) ad Khara et al. (0a, b). The metoed papers have cosdered a costat ad well-kow flato rate over the tme horzo. Yet, flato eters the vetory pcture oly because t may have a mpact o the future vetory costs, ad the future rate of flato s heretly ucerta ad ustable. But, there are a few works the flatoary vetory researches uder stochastc codtos, especally wth multple stochastc parameters. Mrzazadeh ad Sarfaraz (997) preseted multple-tems vetory system wth a budget costrat ad the uform dstrbuto fucto for the exteral flato rate ad Horowtz (000) dscussed a smple EOQ model wth a Normal dstrbuto for the flato rate ad the frm s cost of captal. He showed the mportace of takg to accout the flato rate ad tme dscoutg, especally whe the former s relatvely hgh or whe there s cosderable ucertaty as to ether the flato rate or the margal cost of captal. Mrzazadeh (007) compared the average aual cost ad the dscouted cost methods the vetory system's modelg wth cosderg stochastc flato. The results show that there s a eglgble dfferece betwee two procedures for wde rage values of the parameters. Furthermore, Mrzazadeh (008) aother work, proposed a vetory model uder tme-varyg flatoary codtos for deteroratg tems. Mrzazadeh (009) developed A Partal Backloggg Mathematcal Model uder Varable Iflato ad Demad. I aother study, Mrzazadeh (0) prepared a optmal producto model for a vetory cotrol system where the tme horzo.e., perod of busess, s radom ature. I the lterature, the demad rate usually has bee cosdered costat ad well kow, tme-varyg, stock depedet or prce-depedet Furthermore, some practcal stuatos, the demad rate may be ucerta. Therefore, ths paper the o-determstc vetory model wth flato uder stochastc demad has bee developed. Also, the umercal example has bee provded to llustrate ad valdate the theoretcal results. METHODOLOGY The basc assumptos ad otatos: The followg assumptos have bee cosdered the developed model: The vetory system costs are kow at the begg of the tme horzo ad wll be creasg through the flato rates. The demad rates are stochastc. A multple tems vetory system has bee cosdered. The avalable budget s costraed ad wll be creased through the flato rate. Repleshmet s stataeous,.e., the repleshmet rate s fte. The followg otatos are used the model: : The umber of tems Q : The order quatty for -th tem D : The aual demad rate for -th tem S : The orderg cost for -th tem at the begg of the tme horzo : The purchasg cost per ut for -th tem at tme zero h k : The teral (for k = ) ad the exteral (for k = ) holdg cost per ut per ut tme for -th tem at tme zero f k : The Iteral Iflato Rate (IIR) for k = ad the Exteral Iflato Rate (EIR) for k = R k : The dscout rate et of flato: R k = r- f k where r s the dscout rate B : The maxmum avalable budget at the begg of the tme horzo E [EUA ] : The total expected aual costs for -th tem, where: =,,..., The models developmet: The may studes of the vetory maagemet systems have bee reported. Aalyss of the vetory systems the lterature s carred out usg two procedures. Frst method, determe the optmal values of the cotrol varables by mmzg the average aual cost ad the alteratve (ad theory more correct) procedure determe the optmal orderg polcy by mmzg the dscouted value of all future costs. As stated prevously, Mrzazadeh (007) showed by detaled computatos that there s a eglgble dfferece betwee two procedures for wde rage values of the parameters. The average aual cost method wll be used ths paper. The total aual vetory systems costs clude purchasg, orderg ad carryg costs. The aual purchasg cost for the -th tem s equal to: [+ (! Q /D )f /] D () also, the aual orderg cost for the -th tem s as follows: 5035
3 Res. J. Appl. Sc. Eg. Techol., 4(3): , 0 [+ (! Q /D )f /](S D / Q ) () Fally, the aual carryg cost for the -th tem after calculatg wll be: k Q / Dfk / hkrq / (3) Therefore, the total aual vetory system costs for the -th tem are: / / / Q / DfkhkrQ / k Q / D f / D EUA Q D f S D Q (4) Budget costrat: The avalable budget at tme zero s B, whch wll be creasg by EIR. Also, the ut prce,, creases by EIR. Therefore, the budget costrat at the tme t s: Qe Be ft ft By smplfyg Eq. (5) we have: (5), f D d d Id d (8) Objectve s to mmzato of the total expected aual costs: where: MZ E EUA EEUA EUA( D) dd d d f (9) (0) By substtutg Eq. (4) (0) ad the substtutg Eq. (9) ad smplfyg the terms we have: rq f h fhl( d / d ) 4( d d) Qh r f/ hr f / f MZ f / d d f S f/ d d Q () Therefore, the costraed multple tem vetory model ca be stated as follow: Q B (6) M Z Q B () The demad rates Eq. ()-(4) are stochastc. The expected value method ca be used ths drecto. Therefore, the optmal orderg quattes may be calculated usg ths model: MZ E EUA ` st..: (7) Q B The expermetal results reveal that three probablty desty fuctos (pdf) are sutable for the demad rate the vetory systems: Uform Normal Expoetal These cases wll be explaed as follows. ase (I): Stochastc demad rate wth uform dstrbuto fucto: Let demad rate has a Uform pdf as follows: The problem has bee solved wth usg the Lagraga method: LQ (, ) Z Q B (3) By takg the frst devato of the above fucto respect to Q I ad l, set them equal to zero ad smplfcato we have: S f/ d d / / Q rh h f Q B For =,..., (4) The Q, for =,,, wll be obtaed by calculatg the above equatos. ase (II): Stochastc demad rate wth ormal dstrbuto fucto: Now assume the demad rate of - th tem has a Normal dstrbuto wth the mea of m ad 5036
4 Res. J. Appl. Sc. Eg. Techol., 4(3): , 0 the stadard devato of F. The Eq. (4), may be rewrtg as follows wth cosderg rf. rf.0 : S f / EUA ( f / ) Q fs rq( h h) Qf D Therefore, the total expected aual costs are: MZ E EUA (5) (6) So, the mathematcal model ca be explaed as follows: S ( f / ) MZ ( f / ) Q fs rq( h h) Qf st..: Q B (7) Smlarly case (), the Lagrage-multpler ca be used. Therefore, the optmal orderg quattes wll be obtaed wth usg these equatos: S( f / ) Q rh h f for,..., ( / ( / ) Q B (8) ase (III): Stochastc demad rate wth expoetal dstrbuto fucto: The demad rate of -th tem has the expoetal pdf wth the mea of /q. Smlarly, the prevous cases, the optmal orderg quattes ca be calculated wth usg: S( f / ) Q rh h f for,..., ( / ( / ) Q B (9) The umercal example: The followg umercal example s provded to llustrate the theoretcal results. Let = S = $00 per order S = $00 per order = $30 per ut = $0 per ut h = $0.5 per ut per year h = $0.5 per ut per year h = $.5 per ut per year h = $0.75 per ut per year Table : The umercal example ase Parameters Q * Q * 8* I d = d = 000 d = 4000 d = 8000 II µ = F = 00 µ = 5000 F = 50 III = / = /9000 B = $0000 r = 0% per ut cost per year f = 8% f = %. The models have bee solved wth cosderg the above metoed values ad the optmal values of Q, Q ad l have bee obtaed (Table ). Summary: Sce 975 a seres of related papers appeared that cosdered the effects of flato o the vetory system. I the prevous research, the demad rate has bee cosdered ostat ad well kow, Tme-varyg, Stock depedet, Prce-depedet. Furthermore, the demad rate has a o-determstc stuato the real world. The sgfcat ad uque fdgs of ths research comparso wth the prevous work, s developg the ew flatoary vetory models wth assumg stochastc demad rate. The multple tems have bee cosdered the system ad the repleshmet s stataeous,.e., the repleshmet rate s fte. The avalable budget s costraed ad wll be creased through the flato rate. The umercal example has bee provded to llustrate the theoretcal results. REFERENES Balkh, Z.T., 004a. O the optmalty of vetory models wth deteroratg tems for demad ad ohad vetory depedet producto rate. IMA J. Maage. Math., 5: Balkh, Z.T., 004b. A optmal soluto of a geeral lot sze vetory model wth deterorated ad mperfect products, takg to accout flato ad tme value of moey. It. J. Syst. Sc., 35: Buzacott, J.A., 975. Ecoomc order quattes wth flato. Oper. Res. Quart., 6: hag,.t., 004. A EOQ model wth deteroratg tems uder flato whe suppler credts lked to order quatty. It. J. Prod. Eco., 88: he, J.M., 998. A vetory model for deteroratg tems wth tme-proportoal demad ad shortages uder flato ad tme dscoutg. It. J. Prod. Eco., 55:
5 Res. J. Appl. Sc. Eg. Techol., 4(3): , 0 he, J.M. ad.s. L, 00. A optmal repleshmet model for vetory tems wth ormally dstrbuted deterorato. Prod. Pla. otr., 3: her, M.S., H.L. Yag ad J.T. Teg, 008. Partal backloggg vetory lot-sze models for deteroratg tems wth fluctuatg demad uder flato. Eur. J. Oper. Res., 9: 7-4. hug, K.J., 003. A algorthm for a vetory model wth vetory-level-depedet demad rate. omput. Oper. Res., 30: hug, K.J. ad S.F. Tsa, 00. Ivetory systems for deteroratg tems wth shortage ad a lear tred demad-takg accout of tme value. omput. Oper. Res., 8: Datta, T.K. ad A.K. Pal, 99. Effects of flato ad tme-value of moey o a vetory model wth lear tme-depedet demad rate ad shortages. Eur. J. Oper. Res., 5: Dey, J.K., S.K. Modal ad M. Mat, 008. Two storage vetory problem wth dyamc demad ad terval valued lead-tme over fte tme horzo uder flato ad tme-value of moey. Eur. J. Oper. Res., 85: Harga, M.A., 995. Effects of flato ad tme-value of moey o a vetory model wth tme-depedet demad rate ad shortages. Eur. J. Oper. Res., 8: Harga, M.A. ad M. Be-Daya, 996. Optmal tme varyg lot-szg models uder flatoary codtos. Eur. J. Oper. Res., 89: Hseh, T.P. ad.y. Dye, 00. Prcg ad lot-szg polces for deteroratg tems wth partal backloggg uder flato. Expert Syst. Appl., I Press. Horowtz, I., 000. EOQ ad flato ucertaty. It. J. Prod. Eco., 65: 7-4. Hou, K.L., 006. A vetory model for deteroratg tems wth stock-depedet cosumpto rate ad shortages uder flato ad tme dscoutg. Eur. J. Oper. Res., 68: Hou, K.L. ad L.. L, 004. Optmal vetory model wth stock-depedet sellg rate uder maxmal total preset value of profts. Proceedg of 4th IASTED Iteratoal oferece o Modelg, Smulato ad Optmzato, pp: 7-. Jagg,.K., K.K. Aggarwal ad S.K. Goel, 006. Optmal order polcy for deteroratg tems wth flato duced demad. It. J. Prod. Eco., 03: Khara S., S.K. Ghosh ad K.S. haudhur, 0a. A EOQ model for a deteroratg tem wth tme depedet quadratc demad uder permssble delay paymet. Appl. Math. omput., 8: -9. Khara, S., S.K. Ghosh ad K.S. haudhur, 0b. Optmal prce ad lot sze determato for a pershable product uder codtos of fte producto, partal backorderg ad lost sale. Appl. Math. omput., 7: Lao, H.. ad Y.K. he, 003. Optmal paymet tme for retaler's vetory system. It. J. Syst. Sc., 34: Lo, S.T., H.M. Wee ad W.. Huag, 007. A tegrated producto-vetory model wth mperfect producto processes ad Webull dstrbuto deterorato uder flato. It. J. Prod. Eco., 06: Maty, A.K., 00. Oe mache multple-product problem wth producto-vetory system uder fuzzy equalty costrat. Appl. Soft omput., : Mat, A.K., M.K. Mat ad M. Mat, 006. Two storage vetory model wth radom plag horzo. Appl. Math. omput., 83: Mrzazadeh, A. ad A.R. Sarfaraz, 997. ostraed multple tems optmal order polcy uder stochastc flatoary codtos. Proceedg of d Aual Iteratoal oferece o Idustral Egeerg Applcato ad Practce, Sa Dego, USA, pp: Mrzazadeh, A., 008. Ecoomc order terval uder varable flatoary codtos. Hamburg Iteratoal oferece of Logstcs (HIL008), Hamburg, Germay. Mrzazadeh, A., 007. Effects of ucerta flatoary codtos o vetory models usg the average aual cost ad the dscouted cost. Eghth Iteratoal oferece o Operatos & Quattatve Maagemet (IOQM-8), Bagkok, pp: 7-0. Mrzazadeh, A., 009. A partal backloggg mathematcal model uder varable flato ad demad wth cosderg deterorato ost. World Appl. Sc. J., 7 (Specal Issue for Appled Math): Mrzazadeh, A., 0. Optmal vetory cotrol problem wth flato-depedet demad rate uder stochastc codtos. Res. J. Appl. Sc. Eg. Techol., 4(4): Msra, R.B., 979. A ote o optmal vetory maagemet uder flato. Nav. Res. Logst. Q., 6: Moo, I, B.. Gr ad B. Ko, 005. Ecoomc order quatty models for ameloratg/deteroratg tems uder flato ad tme dscoutg. Eur. J. Oper. Res., 6: Saa, S.S., 00. ERLINK" com/scece?_ob=artcleurl&_ud=b6v0v- 4YM7FGM-&_user=88467&_coverDate =07%F3%F00&_ald= &_rdoc= 3&_fmt=hgh&_org=search&_cd=5656&_sort=r &_st=4&_docachor=&_ct=5&_acct= &_verso=&_urlverso=0&_userd=88467& md5=aabc86ff37e6dd7cdb7caa4afeec"demad flueced by eterprses tatves-a mult-tem EOQ model of deteroratg ad ameloratg tems. Math. omput. Model., 5:
6 Res. J. Appl. Sc. Eg. Techol., 4(3): , 0 Sarkar, B. ad I. Moo, 0. A EPQ model wth flato a mperfect producto system. Appl. Math. omput., 7: Sarker, B.R., A.M.M. Jamal ad S. Wag, 000. Supply cha models for pershable products uder flato ad permssble delay paymets. omput. Oper. Res., 7: Sarker, B.R. ad H. Pa, 994. Effects of flato ad the tme value of moey o order quatty ad allowable shortage. It. J. Prod. Eco., 34: Su,.T., L.I. Tog ad H.. Lao, 996. A vetory model uder flato for stock depedet demad rate ad expoetal decay. Oper. Res., 33: 7-8. Uthayakumar, R. ad K.V. Geetha, 009. Repleshmet polcy for sgle tem vetory model wth moey flato. Opsearch, 46: Vrat, P. ad G. Padmaabha, 990. A vetory model uder flato for stock depedet cosumpto rate tems. Eg. ost. Prod. Eco., 9: Wee, H.M. ad S.T. Law, 999. Ecoomc producto lot sze for deteroratg tems takg accout of the tme value of moey. omput. Oper. Res., 6: Wee, H.M. ad S.T. Law, 00. Repleshmet ad prcg polcy for deteroratg tems takg to accout the tme-value of moey. It. J. Prod. Eco., 7: 3-0. Yag, H.L., 004. Two-warehouse vetory models for deteroratg tems wth shortages uder flato. Eur. J. Oper. Res., 57: Yag, H.L., 006. Two-warehouse partal backloggg vetory models for deteroratg tems uder flato. It. J. Prod. Eco., 03: Yag, H.L., J.T. Teg ad M.S. her, 00. Determstc vetory lot-sze models uder flato wth shortages ad deterorato for fluctuatg demad. Nav. Res. Log., 48: Yag, H.L., J.T. Teg ad M.S. her, 00. A vetory model uder flato for deteroratg tems wth stock-depedet cosumpto rate ad partal backloggg shortages. It. J. Prod. Eco., 3:
Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.
It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A
More informationInternational Journal of
Iter. J. Fuzzy Mathematcal Archve Vol. 3, 203, 36-4 ISSN: 2320 3242 (P), 2320 3250 (ole) Publshed o 7 December 203 www.researchmathsc.org Iteratoal Joural of Mult Objectve Fuzzy Ivetory Model Wth Demad
More informationA Multi Item Integrated Inventory Model with Reparability and Manufacturing of Fresh Products
Moder Appled Scece; Vol. 1, No. 7; 216 ISSN 1913-1844 E-ISSN 1913-1852 Publshed by Caada Ceter of Scece ad Educato A Mult Item Itegrated Ivetory Model wth Reparablty ad Maufacturg of Fresh Products Pky
More informationEstimation 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 informationFunctions 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 informationMasoud Rabbani 1*, Leila Aliabadi 1
Joural of Idustral ad Systems Egeerg Vol. 11, No.2, pp. 207-227 Sprg (Aprl) 2018 Mult-tem vetory model wth probablstc demad fucto uder permssble delay paymet ad fuzzy-stochastc budget costrat: A sgomal
More informationUncertain Supply Chain Management
Ucerta Supply Cha Maagemet 3 (5) 47 58 Cotets lsts avalable at GrowgScece Ucerta Supply Cha Maagemet homepage: www.growgscece.com/uscm Modelg of a vetory system wth mult varate demad uder volume flexblty
More informationMulti-Objective Inventory Model of Deteriorating Items with Shortages in Fuzzy Environment Omprakash Jadhav 1, V.H. Bajaj 2
Iteratoal Joural of Statstka ad Mathematka, ISSN: 2277 279 EISSN: 2249865, Volume 6, Issue 1, 21 pp 45 MultObjectve Ivetory Model of Deteroratg Items wth Shortages uzzy Evromet Omprakash Jadhav 1, V.H.
More informationSolving 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 informationBayes 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 informationMulti-Item Multi-Objective Inventory Model with Fuzzy Estimated Price dependent Demand, Fuzzy Deterioration and Possible Constraints
Advaces Fuzzy Mathematcs. ISSN 0973-533XVolume 11, Number (016), pp. 157-170 Research Ida Publcatos http://www.rpublcato.com Mult-Item Mult-Objectve Ivetory Model wth Fuzzy Estmated Prce depedet Demad,
More informationLikewise, properties of the optimal policy for equipment replacement & maintenance problems can be used to reduce the computation.
Whe solvg a vetory repleshmet problem usg a MDP model, kowg that the optmal polcy s of the form (s,s) ca reduce the computatoal burde. That s, f t s optmal to replesh the vetory whe the vetory level s,
More informationA 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 informationChapter 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 informationAnalysis 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 informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationDeterministic Constant Demand Models
Determstc Costat Demad Models George Lberopoulos Ecoomc Order uatty (EO): basc model 3 4 vetory λ λ Parts to customers wth costat rate λ λ λ EO: basc model Assumptos/otato Costat demad rate: λ (parts per
More informationComparison 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 informationComparing 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 informationAnalysis of Lagrange Interpolation Formula
P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal
More informationOrdinary 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 informationChapter 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 information2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.
.5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely
More informationWaiting Time Distribution of Demand Requiring Multiple Items under a Base Stock Policy
Joural of Servce Scece ad Maagemet 23 6 266-272 http://d.do.org/.4236/jssm.23.643 Publshed Ole October 23 (http://www.scrp.org/joural/jssm) Watg Tme Dstrbuto of Demad Requrg Multple Items uder a Base Stoc
More informationVOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved.
VOL., NO., November 0 ISSN 5-77 ARPN Joural of Scece ad Techology 0-0. All rghts reserved. http://www.ejouralofscece.org Usg Square-Root Iverted Gamma Dstrbuto as Pror to Draw Iferece o the Raylegh Dstrbuto
More informationLecture 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 informationBounds 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 informationSummary 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 informationhp 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 informationUnimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods
Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal
More informationSimple 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 informationMEASURES 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 informationMultiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades
STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos
More informationESS Line Fitting
ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here
More informationCHAPTER 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 informationConfidence Intervals for Double Exponential Distribution: A Simulation Approach
World Academy of Scece, Egeerg ad Techology Iteratoal Joural of Physcal ad Mathematcal Sceces Vol:6, No:, 0 Cofdece Itervals for Double Expoetal Dstrbuto: A Smulato Approach M. Alrasheed * Iteratoal Scece
More informationContinuous 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 informationLecture 8: Linear Regression
Lecture 8: Lear egresso May 4, GENOME 56, Sprg Goals Develop basc cocepts of lear regresso from a probablstc framework Estmatg parameters ad hypothess testg wth lear models Lear regresso Su I Lee, CSE
More informationJournal of Chemical and Pharmaceutical Research, 2014, 6(7): Research Article
Avalable ole www.jocpr.com Joural of Chemcal ad Pharmaceutcal Research, 04, 6(7):4-47 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Predcto of CNG automoble owershp by usg the combed model Ku Huag,
More informationPart 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))
art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the
More informationENGI 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 informationLecture 2 - What are component and system reliability and how it can be improved?
Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected
More informationA New Measure of Probabilistic Entropy. and its Properties
Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com
More informationBayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study
IJIEST Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue 5, July 04. Bayes Iterval Estmato for bomal proporto ad dfferece of two bomal proportos wth Smulato Study Masoud Gaj, Solmaz hlmad
More informationDynamic Analysis of Axially Beam on Visco - Elastic Foundation with Elastic Supports under Moving Load
Dyamc Aalyss of Axally Beam o Vsco - Elastc Foudato wth Elastc Supports uder Movg oad Saeed Mohammadzadeh, Seyed Al Mosayeb * Abstract: For dyamc aalyses of ralway track structures, the algorthm of soluto
More informationChapter 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 informationCHAPTER 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 informationSimple Linear Regression
Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uversty Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal
More informationOptimal Strategy Analysis of an N-policy M/E k /1 Queueing System with Server Breakdowns and Multiple Vacations
Iteratoal Joural of Scetfc ad Research ublcatos, Volume 3, Issue, ovember 3 ISS 5-353 Optmal Strategy Aalyss of a -polcy M/E / Queueg System wth Server Breadows ad Multple Vacatos.Jayachtra*, Dr.A.James
More informationA Production Model for Time Dependent Decaying Rate with Probabilistic Demand
www.jem.et ISSN (ONLINE: 5-758, ISSN (PRIN: 394-696 Volume-7, Issue-3, May-Jue 7 Iteatoal Joual of Egeeg ad Maagemet Reseach Page Numbe: 4-47 A Poducto Model fo me Depedet Decayg Rate wth Pobablstc Demad
More informationLecture 1 Review of Fundamental Statistical Concepts
Lecture Revew of Fudametal Statstcal Cocepts Measures of Cetral Tedecy ad Dsperso A word about otato for ths class: Idvduals a populato are desgated, where the dex rages from to N, ad N s the total umber
More informationChapter -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 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 informationBlock-Based Compact Thermal Modeling of Semiconductor Integrated Circuits
Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud
More information12.2 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 informationEvaluation of uncertainty in measurements
Evaluato of ucertaty measuremets Laboratory of Physcs I Faculty of Physcs Warsaw Uversty of Techology Warszawa, 05 Itroducto The am of the measuremet s to determe the measured value. Thus, the measuremet
More informationModule 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 informationChapter 13 Student Lecture Notes 13-1
Chapter 3 Studet Lecture Notes 3- Basc Busess Statstcs (9 th Edto) Chapter 3 Smple Lear Regresso 4 Pretce-Hall, Ic. Chap 3- Chapter Topcs Types of Regresso Models Determg the Smple Lear Regresso Equato
More informationBootstrap Method for Testing of Equality of Several Coefficients of Variation
Cloud Publcatos Iteratoal Joural of Advaced Mathematcs ad Statstcs Volume, pp. -6, Artcle ID Sc- Research Artcle Ope Access Bootstrap Method for Testg of Equalty of Several Coeffcets of Varato Dr. Navee
More informationAnalysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed
Amerca Joural of Mathematcs ad Statstcs. ; (: -8 DOI:.593/j.ajms.. Aalyss of a Reparable (--out-of-: G System wth Falure ad Repar Tmes Arbtrarly Dstrbuted M. Gherda, M. Boushaba, Departmet of Mathematcs,
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More informationChapter -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 informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationObjectives of Multiple Regression
Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of
More informationCorrelation and Regression Analysis
Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
More informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More informationDepartment of Agricultural Economics. PhD Qualifier Examination. August 2011
Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly
More informationECONOMETRIC 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 informationChapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance
Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss
More informationA Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions
Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple
More informationLecture 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 informationStatistics: Unlocking the Power of Data Lock 5
STAT 0 Dr. Kar Lock Morga Exam 2 Grades: I- Class Multple Regresso SECTIONS 9.2, 0., 0.2 Multple explaatory varables (0.) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (0.2) Exam 2 Re- grades Re-
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationMultiple Linear Regression Analysis
LINEA EGESSION ANALYSIS MODULE III Lecture - 4 Multple Lear egresso Aalyss Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Cofdece terval estmato The cofdece tervals multple
More informationChapter 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 informationEconometric 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 informationComparison 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 informationSimulation 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 informationNon-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 informationInvestigation 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 informationLINEAR REGRESSION ANALYSIS
LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for
More informationA Method for Damping Estimation Based On Least Square Fit
Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato
More informationFuzzy Programming Approach for a Multi-objective Single Machine Scheduling Problem with Stochastic Processing Time
Proceedgs of the World Cogress o Egeerg 008 Vol II WCE 008, July - 4, 008, Lodo, U.K. Fuzzy Programmg Approach for a Mult-obectve Sgle Mache Schedulg Problem wth Stochastc Processg Tme Ira Mahdav*, Babak
More informationCubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem
Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs
More informationBAYESIAN 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 informationKeywords Specially structured flow shop scheduling. Rental policy, Processing time, weightage of jobs, Set up, Job block.
Iteratoal Joural of Egeerg Research ad Developmet e-issn: 2278-067X, p-issn: 2278-800X,.jerd.com Volume 3, Issue 5 (August 2012), PP. 72-77 Specally Structured To Stage Flo Shop Schedulg To Mmze the Retal
More informationA NEW LOG-NORMAL DISTRIBUTION
Joural of Statstcs: Advaces Theory ad Applcatos Volume 6, Number, 06, Pages 93-04 Avalable at http://scetfcadvaces.co. DOI: http://dx.do.org/0.864/jsata_700705 A NEW LOG-NORMAL DISTRIBUTION Departmet of
More informationLECTURE - 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 informationTHE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE
THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the
More informationAnalyzing Fuzzy System Reliability Using Vague Set Theory
Iteratoal Joural of Appled Scece ad Egeerg 2003., : 82-88 Aalyzg Fuzzy System Relablty sg Vague Set Theory Shy-Mg Che Departmet of Computer Scece ad Iformato Egeerg, Natoal Tawa versty of Scece ad Techology,
More informationIJOART. Copyright 2014 SciResPub.
Iteratoal Joural of Advacemets Research & Techology, Volume 3, Issue 10, October -014 58 Usg webull dstrbuto the forecastg by applyg o real data of the umber of traffc accdets sulama durg the perod (010-013)
More informationAccelerated Life Test Sampling Plans under Progressive Type II Interval Censoring with Random Removals
Iteratoal Joural of Statstcs ad Probablty; Vol. 7, No. ; Jauary 8 ISSN 97-73 E-ISSN 97-74 Publshed by Caada Ceter of Scece ad Educato Accelerated Lfe Test Samplg Plas uder Progressve Type II Iterval Cesorg
More informationComplete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables
Joural of Sceces, Islamc Republc of Ira 8(4): -6 (007) Uversty of Tehra, ISSN 06-04 http://sceces.ut.ac.r Complete Covergece ad Some Maxmal Iequaltes for Weghted Sums of Radom Varables M. Am,,* H.R. Nl
More informationStudy 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 informationLINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD
Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer
More informationA COMPARATIVE STUDY OF THE METHODS OF SOLVING NON-LINEAR PROGRAMMING PROBLEM
DAODIL INTERNATIONAL UNIVERSITY JOURNAL O SCIENCE AND TECHNOLOGY, VOLUME, ISSUE, JANUARY 9 A COMPARATIVE STUDY O THE METHODS O SOLVING NON-LINEAR PROGRAMMING PROBLEM Bmal Chadra Das Departmet of Tetle
More informationSTRONG 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 informationModule 7: Probability and Statistics
Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to
More informationUNIVERSITY 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