Emergency Food Security Assessments (EFSAs) Technical Guidance Sheet No. 11 1

Size: px
Start display at page:

Download "Emergency Food Security Assessments (EFSAs) Technical Guidance Sheet No. 11 1"

Transcription

1 Emergecy Food Securty Assessmets (EFSAs) Techcal gudace sheet. Usg the T-square samplg method to estmate populato sze, demographcs ad other characterstcs emergecy food securty assessmets (EFSAs) Table of Cotet. Why do populato sze ad demographcs chage a crss?.... What are the key steps the T-square method?.... Whe s t approprate to use the T square method? What are the actvtes for the T-square samplg procedure? What are the lmtatos of the T-square samplg procedure?... 8 Aex : Alteratve T method for use areas wth clusterg of households... 0 Aex : Example of data aalyss from a T sample method... 5 Aex : Table for determg t α... 7 Alde Hederso, cosultat, WFP Food Securty Aalyss Servce, December 008. Techcal Gudace Sheet No. Last updated: February 009

2 Techcal Gudace Sheet No. Last updated: February 009 Usg the T-square samplg method to estmate populato sze, demographcs ad other characterstcs emergecy food securty assessmets (EFSAs) Ths Techcal Gudace Sheet (TGS) descrbes the T-square (T) samplg method, ad provdes gudace ad structos o whe ad how to use the method a EFSA. The T s a samplg desg that ca be used stead of other samplg methods a EFSA survey. The advatage of the T method s that t ca estmate the populato sze ad the umbers of people wth certa characterstcs, such as food securty status, lvelhood or age, wthout costructg a samplg frame. Ths TGS complemets other gudace for EFSAs the EFSA Hadbook., partcular Part IV whch deals wth estmatg umbers of people at lvelhood rsk ad projectg umbers of people eed of assstace.. Why do populato sze ad demographcs chage a crss? A shock such as a tsuam, drought or armed coflct may damage roads, crops ad lvestock, homes, markets ad medcal clcs. Ths destructo creates hardshp for the people who lve or work the affected area. As a result, they may leave for areas that are safer ad that offer better opportutes for food, come, shelter ad securty. These movemets ofte chage the populato sze ad demographcs of both the abadoed ad the resettled areas. Accurate ad tmely formato o the umbers ad locatos of people, ad o ther eeds, s a key elemet of a effectve ad approprate humatara respose.. What are the key steps the T-square method? The T method descrbed ths TGS s a modfed verso of a method used ecology to determe the spatal dstrbuto ad desty of trees ad plats large areas. I ths method, the umber of trees s calculated by measurg the dstace from a radom pot to ts closest tree, or earest eghbour. The dstace to the earest eghbour s related to desty ad, whe multpled by the total area, yelds the total umber of trees a area. Ths TGS modfes the ecology method to estmate huma populato sze ad demographcs by measurg the dstace betwee a radom pot ad a occuped house ad coutg the umber of occupats the house. Secto 4 descrbes how to apply the modfed T method. The key steps are: a. selectg a radom pot; b. measurg the dstace from ths pot to the earest occuped house; c. gog to ths house, fdg the occuped house earest to t, ad measurg the dstace betwee the two houses; d. coutg the umber of people lvg the two houses ad calculatg the average; e. estmatg the total area of the survey; f. calculatg the total umber of houses the survey area by dvdg the total area of the survey by the average space occuped by each house ad ts surroudgs; ad g. calculatg the populato sze by multplyg the umber of houses the survey area by the average umber of people a household. Whe a EFSA survey exames specfc characterstcs of households, such as ther food securty stuato or relace o specfc lvelhood actvtes, these results ca be used to determe the umbers of people wth each characterstc by multplyg the total populato sze by the percetage of people wth the characterstc. See revsed EFSA Hadbook, Part IV Secto, Coductg a stuato aalyss, WFP Food Securty Aalyss Servce, 009.

3 Techcal Gudace Sheet No. Last updated: February 009. Whe s t approprate to use the T square method? The followg questos provde a gudele for whe to use the T. If the aswers to all the followg questos are yes, the T samplg method ca be used. If the aswers to questos a to d are yes, but ay aswer to questos e to h s o, the T should be cosdered wth the other samplg methods suggested, to determe whch s most approprate. a) Is a samplg frame uavalable, urelable or outdated, ad would t be dffcult, expesve or laborous to costruct oe? If YES, the T method may be approprate. If NO, cosder usg a radom, systematc, proportoal or cluster sample. b) Is the samplg frame complex? If YES, the T method ca smplfy the sample desg by reducg the umber of stages. Ths may also reduce the desg effect, thereby decreasg the sample sze ecessary to obta the ecessary precso. If NO, cosder usg a radom, proportoal or cluster sample. c) Is the survey takg place durg the recovery stage of a crss? If YES, the T method may be approprate f there s eough tme. It takes oe or two weeks to detfy ad tra surveyors the method before startg the survey. IF NO, other methods requre less tme ad fewer resources, ad would be more approprate the mmedate post-mpact phase of a acute emergecy. These clude the Delph method, aeral surveys, cluster surveys, quadrat surveys ad usg avalable cesus ad survey data to estmate populato sze ad demographcs. WFP has TGS for cluster ad quadrat surveys, ad the Delph method. d) Is the area where the survey wll be coducted larger tha 0 km? If YES, the T method may be approprate. If NO, cosder a radom, proportoal, cluster or quadrat sample. e) Are most of the houses the survey area sgle-storey resdeces? If YES, the T method may be approprate. If NO, cosder a proportoal or cluster sample. f) Are most of the houses radomly dstrbuted? If YES, the T method may be approprate. If NO, a modfed T method (Aex I) may be approprate, but requres more tme ad resources. g) Are detaled maps avalable? If YES, the T method may be approprate. If NO, use a cluster sample desg to select households radomly. See Techcal Gudace Sheets No. 7 Area Method to Estmate Populato Sze ad Demographcs Emergecy Food Securty Assessmets (EFSAs), A. Hederso, WFP Emergecy Needs Assessmet Servce (ow Food Securty Aalyss Servce), September 007; ad No. 0. Usg the Delph Method to Estmate Populato Sze ad Demographcs Emergecy Food Securty Assessmets (EFSAs), A. Hederso, WFP Food Securty Aalyss Servce, Jauary 008.

4 Techcal Gudace Sheet No. Last updated: February 009 The T method ca be used to fd the radom startg pot for each cluster. h) Are global postog system (GPS) uts avalable to survey teams? If YES, the T method may be approprate. If NO, use a cluster sample desg to select households radomly, ad the T method to fd whch houses to survey. 4. What are the actvtes for the T-square samplg procedure? The T method produces three umbers that are used to calculate the populato sze the target area: ) the total sze of the survey area; ) the average lad area occuped by a household; ad ) the average umber of people a household. The T method ca be dvded to pre-feld, feld ad post-feld actvtes. The ma pre-feld actvtes are detfyg the area to survey ad decdg the level of precso, or marg of error, for the estmate. Feld actvtes clude recrutg ad trag the survey teams, coductg the T samplg procedure ad esurg that resposes are accurately recorded. Post-feld actvtes volve aalyzg ad terpretg the data ad reportg the results Pre-feld actvtes Oce t has bee decded to use the T samplg method the ESFA survey, ad admstratve ad logstcs support are avalable, the ext steps are to defe the area to be surveyed, decde o the marg of error for the survey, calculate the umber of households to sample, ad select ad mark radom pots o the map. a. Defg the area to be surveyed Ths s usually the area where the shock has occurred, people are located ad assstace wll be provded. Geographcal Iformato System (GIS) 4 programs are used to detfy the survey area s borders ad calculate ts surface area. If GIS s ot avalable, Google Earth ( or local maps ca be used to estmate the area to be surveyed. A refemet of detfyg the target area s to clude oly the habted areas the survey area. Ths ca be accomplshed by cludg areas aroud ctes ad vllages ad excludg uhabted areas such as rvers, lakes, deserts, raves, moutas, swamps, parks, forests, etc. Ths wll decrease the amout of area the survey wll cover ad the varablty of the estmate. If the uhabted areas caot be detfed, alterate approaches are to: ) Have GIS programs detfy buffers aroud geographcal features assocated wth habtato, such as roads etc., ad posto radom pots these buffered areas. ) Icrease the umber of radom pots to be surveyed to accout for uhabted areas. Local staff ca defe what costtutes a uhabted area, such as whe there s o occuped house wth km of a radom pot (the horzo s about.7 km away for a perso m tall stadg a flat area), or o house wth a 0-mute walk from the radom pot. The survey team records ths area as uhabted ad moves to the ext radom pot. The umber of pots surveyed should be creased to accout for uhabted areas. b. Decdg the marg of error/level of precso for the survey The marg of error/level of precso for the populato estmate obtaed from the survey ca be: 4 Amog other ageces, the Food Securty Aalyss Servce at WFP Headquarters may be able to provde assstace wth ths. 4

5 Techcal Gudace Sheet No. Last updated: February 009 ± 0 to 0 percet for surveys doe te to 5 days after the shock; or ± 0 percet (or less) for surveys doe 5 days after the shock. The marg of error ca be chaged accordg to how the ESFA results are to be used. For stace, f the EFSA results are to be compared wth prevous or future surveys, a marg of error of less tha 0 percet may be more approprate; the smaller the marg of error, however, the more households have to be surveyed. c. Calculatg the umber of households to be sampled The marg of error s used to calculate the umber of households to be surveyed, whch should be less tha 0 percet of the total households the area. The followg table provdes a gude to the umber of houses to sample accordg to the precso of the survey ad usg a smple radom sample desg. The estmated percetage of the characterstc the surveyed populato refers to the populato characterstc that the survey s measurg secure households, chldre uder 5, farmers, etc. If ths percetage s ot kow, 50 percet should be used as the estmate, because ths produces the largest sample sze. Estmated percetage of the characterstc the surveyed populato Marg of error (%) d. Selectg ad markg radom pots o a map of the area to be surveyed If possble, systematc sample selecto should be used to locate radom pots for the survey. Systematc selecto s preferred because t detfes a pot each secto of the survey area, so the surveyed households are spread across the etre survey area. A radom sample may have clusters of houses oe area, ad/or large areas that are ot sampled Feld actvtes T feld actvtes calculate the average area a household occupes ad the average household sze the target area. These umbers, alog wth the total sze of the target area, are used to estmate the populato sze ad, f addtoal detals are collected, demographcs. The followg steps are carred out the feld: a. Recrutg survey teams: Whe possble, recrut people who have experece of coductg surveys ad kow how to admster a questoare ad record resposes. As a mmum, each team should have oe leader ad oe assstat. The team leader avgates to the radom pot, fds the earest eghbour ad house T (see Fgure ), explas the purpose of the survey to the household occupats ad obtas ther approval for coductg the survey. The assstat measures the dstaces from the radom pot to ts earest eghbour, ad from there to the ext earest eghbour. Both admster the questoare to household T. b. Trag surveyors: Team members eed specal trag o coductg the T method. People take tme to grasp the cocept of fdg the earest eghbour ad cosstetly measurg the dstace betwee the houses. Oe day of trag a morg sesso to expla the cocept ad a afteroo sesso to practse the T sample procedure the feld, followed by a dscusso o ay problems ecoutered helps team members to become comfortable wth ad profcet ths step. c. Obtag maps ad equpmet for the survey: Survey teams eed detaled maps showg the survey area, each radom pot ad earby ladmarks so that they ca fd the boudares of the survey area ad avgate to the radom pots. WFP Headquarters, regoal bureaux or coutry offces may be able to provde these 5

6 Techcal Gudace Sheet No. Last updated: February 009 maps. If ot, they ca be dowloaded from Google Earth ( Fgure ). Methods for measurg the dstaces betwee houses are also eeded. These ca be as smple as trag survey teams to measure ther paces ad cout the umber of paces to reach a house, or to use a tape or rope marked metres. Specalzed measurg tools such as a surveyor s wheel or a laser rage fder speed up the measurg process. d. Navgatg to the radom pot (R): There are two ways of reachg the radom pot, depedg o whether or ot GPS strumets are avalable:. Whe GPS strumets are avalable: GIS software marks the radom pots (R o Fgure ) ad ladmarks the survey area (Fgure ), calculates the dstaces ad drectos from each ladmark to each radom pot, ad provdes the lattude ad logtude of each radom pot ad ladmark. The survey team puts these umbers to a GPS strumet ad uses t to avgate to a radom pot. Ths s best doe by gog to a earby ladmark ad usg GPS to avgate to the radom pot. Oce at the radom pot, the survey team coducts the T samplg as llustrated Fgure.. Whe GPS strumets are ot avalable: Teams mark radom pots o locally avalable maps or those draw by GIS or Google Earth, ad detfy the locatos of earby ladmarks (Fgure ). The survey team uses the map to avgate to the radom pot, where t coducts the T samplg as llustrated Fgure. Fgure. Usg the T method to detfy whch houses to survey Source: adapted from Dggle, 98. Each dot Fgure s a house. The survey team uses GPS strumets or maps to avgate to the radom pot, R. Oce at R, the team vsually locates the earest household (S) ad goes to t. At S, the team looks for the ext earest household (T) wth the hemsphere that s perpedcular to the le draw from R to S. The survey team measures the dstaces from R to S ad from S to T, ad tervews the members of household T. The dstace from R to S s x ad the dstace from S to T s z the formulas used to calculate populato sze ad marg of error. e. Takg measuremets usg the T samplg method: Use Fgure ad the followg structos:. Oce at R, the team marks t wth a coloured flag or stake. It the locates the earest house (S) by vsual observato, ad walks to t. Whe two or more households appear to be at smlar dstaces, the team couts the umber of paces t takes to reach each, to detfy whch s the closest. If two or more 6

7 Techcal Gudace Sheet No. Last updated: February 009 houses are equdstat from R, the oe to be surveyed s selected radomly. The team the measures the dstace from R to S.. At S, the team determes whether the house s occuped or vacat. A vacat house s usually oe where o oe has lved for the past three moths, but survey teams ca use a dfferet referece perod based o local evets or codtos. If S s vacat, the team goes back to R ad selects the ext earest house, repeatg these steps utl the earest occuped house s detfed.. A team member stads frot of the occuped house S, faces R ad exteds her/hs arms outwards. The agle betwee the outstretched arms ad the le from R to S forms the T (Fgure ), but oe of the houses sde ths T ca be selected as house T. The team member turs 80º oe of the houses frot of hm/her ca be selected as house T. The team looks for the earest occuped house house T ad measures the dstace from S to T. The survey s admstered to occupats of house T. 4. The radom pot s completed whe the team has: () measured the dstace from pot R to house S; () measured the dstace from house S to house T; ad () tervewed the occupats of house T. 5. The T sample procedure s desged for areas where each buldg s habted by oe household. Other combatos clude several famles sharg a mult-storey buldg, compoud of several huts, or sgle hut. Typcally, a household s defed as a group of people who eat from the same pot ad sleep uder the same roof. The shock may have chaged lvg codtos so that several famles are lvg oe household, but f they all eat ad sleep uder the same roof, they are cosdered to be oe famly. f. Idetfyg household respodets ad obtag demographc ad other formato: As ay survey, the team leader must expla the purpose of the survey to potetal partcpats, provde ay ecessary formato ad ask permsso to admster the questoare. If a partcpat refuses, surveyors should ask how may people lve the household, or obta the formato from a eghbour. 4. Post-feld actvtes At ths stage, the data are aalyzed ad a report produced o the estmated populato sze ad ts marg of error. As metoed earler, the T samplg procedure produces three umbers for calculatg the populato sze the target area: ) the total sze of the survey area; ) the average lad area occuped by a household; ad ) the average umber of people a household. The total surface area surveyed dvded by the average area occuped by a household dcates how may houses are the survey area. Multplyg ths umber by the average umber of people a household gves the populato sze of the survey area. The followg formula s used: total sze of survey area average area of household F average umber of people household populato survey area The average lad area a house occupes cludes the lad t sts o, the lad assocated wth t garde, amal pe, paths/roads, uhabted structures such as latres, storage, amal housg, etc. ad the lad betwee t ad the ext house. As llustrated the followg example, the report should clude at least the estmated average umber of people the survey area, ad the marg of error ad rage of the estmate. Addtoal elemets ca be the umber of houses sampled, how may were vacat, 7

8 Techcal Gudace Sheet No. Last updated: February 009 ad ay problems ecoutered durg the survey that may have affected the results ad populato estmates. The accompayg Excel spreadsheet cotas formulas that automatcally calculate the populato sze ad cofdece terval whe survey team members put the dstaces betwee houses ad the umbers of occupats. Example The T method determed the followg: a. Average lad area occuped by a household: 50 m. b. Average umber of people a household: 7.6. c. Total sze of target area:.5 mllo m. Total populato:,50,000 m X 7.6 people/house (8,. houses x 7.6 people/house) 6,0 people 50 m Fal results: The total umber of people the survey area s estmated at 6,0. Ths estmate has a 0 percet marg of error, ad the true populato wll be betwee 57,000 ad 69, What are the lmtatos of the T-square samplg procedure? a) The earest eghbour to a radom pot o the edge of the target area may be outsde the survey area, ad teams must select oly houses that are wth the area. I ths stuato, the earest eghbour the survey wll ot be the house closest to the radom pot, so the earest eghbour dstace may be overestmated. The survey team should ote that the true earest eghbour s outsde the survey area, ad that t has used the earest house wth that area. b) Surveyors must uderstad the method ad be able to fd pot R ad houses S ad T, measure dstaces accurately ad use the same pot of referece for each house. c) The referece pot used to measure the dstace from oe house to aother may vary whe the shapes ad szes of houses vary. Survey teams should agree what referece pot the house to use for example, the frot door, a specfc corer of the house, or the mdpot of a specfc wall o the house. At the ed of each day, survey teams should dscuss stuatos where they had to make decsos. The dmesos of houses ca be measured ad reported to show the varato house shape. d) The T method s desged to estmate the desty of households, so t s assumed that the households the sample area are ot clustered. If there s clusterg, the alteratve T method descrbed Aex I should be used. e) As for ay survey method appled the feld, before usg the T method, t s mportat to cosder whether securty, log dstaces ad access to survey areas wll hder survey teams ablty to travel to survey stes. If so, alteratve methods should be cosdered. 8

9 Techcal Gudace Sheet No. Last updated: February 009 Fgure. Sample map for avgatg to a radom pot Detal of area surroudg waypot N, Nearby ladmarks of a market ad a school are detfed wth ther dstaces ad drectos to the waypot. Refereces Byth, K. ad Rpley, B.D O Samplg Spatal Patters by Dstace Methods. Bometrcs, 6: Dggle, P. 98. Some Statstcal Aspects of Spatal Dstrbuto Models for Plats ad Trees. Studa Forestala Sueca, l: 6. Greewood, J.D Basc techques. I W.J. Sutherlad, ed. Ecologcal cesus techques: a hadbook, pp. 0. Cambrdge Uversty Press, Cambrdge, UK. Krebs, C.J Ecologcal Methodology. d edto. Chapter 5. Estmatg Abudace:Le Trasects ad Dstace Methods These Techcal Gudace Sheets, the EFSA Hadbook ad other related resources are avalable at: 9

10 Techcal Gudace Sheet No. Last updated: February 009 Aex I. Alteratve T method for use areas wth clusterg of households Ths procedure s based o the Byth ad Rpley procedure for dstace methods large areas wth clusterg of households; t cludes a step that adjusts for the atural clusterg of households (Byth ad Rpley, 980). The steps the procedure are:. Double the umber of households to survey because two surveys wll be carred out ad two estmates made: oe usg the dstace from a radom pot to a house; the other usg the dstace from a radom house to ts earest eghbour.. Radomly select half of the startg pots. Ths umber s. For example, Fgure 4, the sample s based o three pots; doublg ths meas that sx pots wll be selected. I ths case, radomly select three of the sx pots (marked as uboxed Ps Fgure 4). A survey team goes to each of these three pots ad measures the dstace from t to ts earest occuped household. Ths s the x value. The survey team admsters the questoare to the occupats of the earest household. The followg formula s used to calculate household desty the survey area: ^ N ( x F π Σ ) ˆN estmated populato desty of households the survey area; umber of pots sampled; x dstace from a radom pot to the earest household the survey area.. Aroud each of the remag three pots, mark a small plot cotag about fve households (show as boxed Ps Fgure 4). Number all the households the box ad radomly select households from across all the plots. s the umber of plots ths example, three. Because the three houses are radomly selected from all the houses all the plots, a plot may have o, oe, two or eve three selected households t. I Fgure 4, oe plot cotas o selected households, oe has oe household, ad aother has two. The survey team measures the dstace from the radom pot to the radomly selected house. Ths dstace s z. The team admsters the questoare to the occupats of the household. The followg formula s used to calculate household desty the survey area: ˆ N F π Σ( z ) ˆN estmated populato desty of households the plots; umber of pots/plots sampled; z dstace from the radom household to ts earest household the plot. 4. If N ad N are statstcally smlar (use a T-test), the dstrbuto of houses the survey area s radom. The average of N or N ca be used as the populato estmate the survey area. If N ad N are statstcally dfferet, the dstrbuto of the households the survey area s ot radom, ad formula F4 s used to adjust for the clusterg effect: 0

11 Techcal Gudace Sheet No. Last updated: February 009 N ˆ F4 ˆN estmated populato sze whe calculatg ts geometrc average from ˆN. pot-to-households wth the survey zoe ( ) ˆN ad wth the plots ( ) 5. The marg of error for N or N s calculated by: defg varace as, F5 the varace for each N wll be yˆ F6 ŷ ad the marg of error for each N wll be. s ether N or N ad s the sample sze. F7 6. The marg of error for ˆN s calculated by: defg varace as (/ ) F8 the stadard error s: varace(/ ) F9 ˆ N π Σ( ) F0 x F π Σ( ) z N ˆ F ( ) average area a household occupes; ( ) sample sze; x dstace from the radom pot to ts earest eghbour; z dstace from the radom pot to ts earest eghbour the plot.

12 Techcal Gudace Sheet No. Last updated: February 009 Fgure 4. Sample map for usg the T method a target area wth clusterg of households Source: Krebs, 999. radom pot; house; radomly selected house; survey area; boudary strp aroud survey area; plot. The border of the survey area s marked ad a boudary area about 5 m wde s draw roud ths boudary. Ths survey has a sample sze () of : ( x ) 6, so sx radom pots are selected ad marked systematcally the survey area. Three survey pots are radomly selected for the N method (formula F) ad the three remag pots are used for the N method (formula F). For the N method, a survey team avgates to the radom pot ad fds the dstace to ts earest eghbour. For the N method, the team marks a plot cotag up to fve houses aroud each radom pot, umbers each house the plots cosecutvely, ad radomly chooses houses (three ths case because the sample sze s three) from all the plots. Oce the houses have bee selected, the survey team measures the dstace from the radom pot to radomly selected house(s) the plot. A example of the data ad calculatos volved are descrbed the followg:

13 Techcal Gudace Sheet No. Last updated: February 009 Table. House umber x x z z Number of occupats Sum Average ˆN ˆN ˆN Fgures for x ad z were obtaed from Kerbs, 999: Box 5.. Number of occupats was radomly selected to cetre aroud 6. Usg the umbers Table, calculato of the populato s as follows: ^ N 0 πσ( x ) (.4) 0 πσ( ) (.4)(665.84) ( ) z house/m 40 houses/ha; house/m 96 houses/ha; N ˆ (0.0040)( ) house/m 6 houses/ha. As 40 96, the houses are clustered the survey area, ad N s a less based estmate of the populato. ˆ (/ N) (/ ) (6.5) Varace

14 Techcal Gudace Sheet No. Last updated: February 009 Stadard error varace(/ ) ˆ 0 N Upper 95% CI + tα [ S. E.(/ N)] (.09)(8.075) m /house N house/m 56 houses/ha. Lower 95% CI tα [ S. E.(/ N)] 6.55 (.09)(8.075) m /house N The 95 percet cofdece terval (CI).09 where (see Aex ) house/m 69 houses/ha. t α for - (0-) 9.09 The populato ca be estmated from formula F, usg the survey area ad average umber of people a household (6.9 ths example). The 95 percet CI ca be calculated wth a upper ad a lower 95 percet CI (69 ad 56 ths example). 4

15 Techcal Gudace Sheet No. Last updated: February 009 Example of data aalyss from a T sample method that estmates populato sze ad demographcs Aex. Data from a survey ca be etered the Excel spreadsheet below to obta a populato estmate. Table. Measuremets from a T survey House surveyed Dstace from radom pot R to house S Square of dstace from R to S Dstace from house S to house T Square of dstace from S to T Number of people lvg house T Sum Average x dstace from radom pot R to house S metres (colum ); x square of dstace from R to S (colum ); z dstace from house S to house T metres (colum 4); z square of dstace from S to T (colum 5); N umber of people lvg house T (colum 6); sample sze (0); s x varace of R to S; s covarace of the dstace from R to S ad the dstace from S to T. xz 5

16 Techcal Gudace Sheet No. Last updated: February 009 The desty of houses s estmated as: 0 N T houses/m or.7 Σ [ ] ( 705)[ (797) ] ( x ) Σ( z ) houses/ha. F The stadard error s calculated as: 8( z sx xzxz x sz ) S.E. + + F T 8(797) (40.) + (705)(797)(77.6) + (705) (777.5) Varace of F4 Σx ( x) ( Σx) / 7675 (705) 0 / Σz Varace of ( z) F5 ( Σz) / 465 (797) 0 / ( Σx)( Σz) / 9569 (705)( 797) Σxz / 0 CoVarace of x ad z).9 0 F6 The 95 percet CI for the recprocal of ths desty estmate s: ) F7 ± tα S. E.( ˆ where t α. 7 for 9 (-) degrees of freedom (see Aex T N T 97 ± (.7)(094) or,0 ad 5,845 Takg recprocals gets ad , whch yelds 4.76 ad.7 houses/ha. If the total area s 0,000 ha ad the average umber of people a household s 6., the populato sze s: 0,000 ha x.6 x 6. 05,80 0,000 ha x.7 x 6. 90,8 0,000 ha x 4.76 x 6. 04,6 Thus, the populato sze s 05,00 wth a 95 percet CI of 04,400 ad 0,00. The survey estmated that 6 percet of these people are farmers. To calculate the umber of farmers fd 6 percet of 05,00 4,600. 6

17 Techcal Gudace Sheet No. Last updated: February 009 Table for determg t α Aex. For example, a survey s to have a marg of error of 0 percet. To calculate the cofdece, subtract 0 percet from 00 percet ( percet). The 90 percet ( the cofdece colum o the rght of the table above) s used to fd the t α to be used the Excel spreadsheet to calculate the populato sze. The degree of freedom s calculated by subtractg from the sample sze: where the sample sze. For a sample sze of 0, the degree of freedom s 0 9. The chart does ot have 9, so use the closest degree of freedom, whch s 0. To fd the t α for a sample of 0 at a cofdece of 90 percet, fd the 90% row the frst colum, ad fd 0 o that row. The t α s.7. If the survey has a cofdece of 95 percet ad a sample of 00, the t α s.98. 7

Consult the following resources to familiarize yourself with the issues involved in conducting surveys:

Consult the following resources to familiarize yourself with the issues involved in conducting surveys: Cofdece Itervals Learg Objectves: After completo of ths module, the studet wll be able to costruct ad terpret cofdece tervals crtcally evaluate the outcomes of surveys terpret the marg of error the cotext

More information

TOPIC 7 ANALYSING WEIGHTED DATA

TOPIC 7 ANALYSING WEIGHTED DATA TOPIC 7 ANALYSING WEIGHTED DATA You do t have to eat the whole ox to kow that the meat s tough. Samuel Johso Itroducto dfferet aalyss for sample data Up utl ow, all of the aalyss techques have oly dealt

More information

Gene Expression Data Analysis (II) statistical issues in spotted arrays

Gene Expression Data Analysis (II) statistical issues in spotted arrays STATC4 Sprg 005 Lecture Data ad fgures are from Wg Wog s computatoal bology course at Harvard Gee Expresso Data Aalyss (II) statstcal ssues spotted arrays Below shows part of a result fle from mage aalyss

More information

The Consumer Price Index for All Urban Consumers (Inflation Rate)

The Consumer Price Index for All Urban Consumers (Inflation Rate) The Cosumer Prce Idex for All Urba Cosumers (Iflato Rate) Itroducto: The Cosumer Prce Idex (CPI) s the measure of the average prce chage of goods ad servces cosumed by Iraa households. Ths measure, as

More information

- Inferential: methods using sample results to infer conclusions about a larger pop n.

- Inferential: methods using sample results to infer conclusions about a larger pop n. Chapter 6 Def : Statstcs: are commoly kow as umercal facts. s a feld of dscple or study. I ths class, statstcs s the scece of collectg, aalyzg, ad drawg coclusos from data. The methods help descrbe ad

More information

Sample Survey Design

Sample Survey Design Sample Survey Desg A Hypotetcal Exposure Scearo () Assume we kow te parameters of a worker s exposure dstrbuto of 8-our TWAs to a cemcal. As t appes, te worker as four dfferet types of days wt regard to

More information

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

= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality UCLA STAT Itroducto to Statstcal Methods for the Lfe ad Health Sceces Istructor: Ivo Dov, Asst. Prof. of Statstcs ad Neurolog Teachg Assstats: Brad Shaata & Tffa Head Uverst of Calfora, Los Ageles, Fall

More information

? Economical statistics

? Economical statistics Probablty calculato ad statstcs Probablty calculato Mathematcal statstcs Appled statstcs? Ecoomcal statstcs populato statstcs medcal statstcs etc. Example: blood type Dstrbuto A AB B Elemetary evets: A,

More information

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions Math 3 Probablty ad Statstcal Methods Chapter 8 Fudametal Samplg Dstrbutos Samplg Dstrbutos I the process of makg a ferece from a sample to a populato we usually calculate oe or more statstcs, such as

More information

Random Variables. Discrete Random Variables. Example of a random variable. We will look at: Nitrous Oxide Example. Nitrous Oxide Example

Random Variables. Discrete Random Variables. Example of a random variable. We will look at: Nitrous Oxide Example. Nitrous Oxide Example Radom Varables Dscrete Radom Varables Dr. Tom Ilveto BUAD 8 Radom Varables varables that assume umercal values assocated wth radom outcomes from a expermet Radom varables ca be: Dscrete Cotuous We wll

More information

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions Math 3 Probablty ad Statstcal Methods Chapter 8 Fudametal Samplg Dstrbutos Samplg Dstrbutos I the process of makg a ferece from a sample to a populato we usually calculate oe or more statstcs, such as

More information

IEOR 130 Methods of Manufacturing Improvement Fall, 2017 Prof. Leachman Solutions to First Homework Assignment

IEOR 130 Methods of Manufacturing Improvement Fall, 2017 Prof. Leachman Solutions to First Homework Assignment IEOR 130 Methods of Maufacturg Improvemet Fall, 2017 Prof. Leachma Solutos to Frst Homework Assgmet 1. The scheduled output of a fab a partcular week was as follows: Product 1 1,000 uts Product 2 2,000

More information

Mathematics 1307 Sample Placement Examination

Mathematics 1307 Sample Placement Examination Mathematcs 1307 Sample Placemet Examato 1. The two les descrbed the followg equatos tersect at a pot. What s the value of x+y at ths pot of tersecto? 5x y = 9 x 2y = 4 A) 1/6 B) 1/3 C) 0 D) 1/3 E) 1/6

More information

Inferential: methods using sample results to infer conclusions about a larger population.

Inferential: methods using sample results to infer conclusions about a larger population. Chapter 1 Def : Statstcs: 1) are commoly kow as umercal facts ) s a feld of dscple or study Here, statstcs s about varato. 3 ma aspects of statstcs: 1) Desg ( Thk ): Plag how to obta data to aswer questos.

More information

Lecture 9 February 21

Lecture 9 February 21 Math 239: Dscrete Mathematcs for the Lfe Sceces Sprg 2008 Lecture 9 February 21 Lecturer: Lor Pachter Scrbe/ Edtor: Sudeep Juvekar/ Alle Che 9.1 What s a Algmet? I ths lecture, we wll defe dfferet types

More information

Types of Sampling Plans. Types of Sampling Plans. Sampling Procedures. Probability Samples -Simple Random sample -Stratified sample -Cluster sample

Types of Sampling Plans. Types of Sampling Plans. Sampling Procedures. Probability Samples -Simple Random sample -Stratified sample -Cluster sample Samplg Procedures Defe the Populato Idetfy the Samplg Frame Select a Samplg Procedure Determe the Sample Sze Select the Sample Elemets Collect the Data Types of Samplg Plas o-probablty Samples -Coveece

More information

Valuation of Asian Option

Valuation of Asian Option Mälardales Uversty västerås 202-0-22 Mathematcs ad physcs departmet Project aalytcal face I Valuato of Asa Opto Q A 90402-T077 Jgjg Guo89003-T07 Cotet. Asa opto------------------------------------------------------------------3

More information

FINANCIAL MATHEMATICS : GRADE 12

FINANCIAL MATHEMATICS : GRADE 12 FINANCIAL MATHEMATICS : GRADE 12 Topcs: 1 Smple Iterest/decay 2 Compoud Iterest/decay 3 Covertg betwee omal ad effectve 4 Autes 4.1 Future Value 4.2 Preset Value 5 Skg Fuds 6 Loa Repaymets: 6.1 Repaymets

More information

CHAPTER - IV STANDARDIZED CUSUM MEDIAN CONTROL CHART

CHAPTER - IV STANDARDIZED CUSUM MEDIAN CONTROL CHART A Study o Process Varablty usg CUSUM ad Fuzzy Cotrol Charts Ph.D Thess CHAPTER - IV STANDARDIZED CUSUM MEDIAN CONTROL CHART. Itroducto: I motorg e process mea, e Mea ( X ) cotrol charts, ad cumulatve sum

More information

Chapter 4. More Interest Formulas

Chapter 4. More Interest Formulas Chapter 4 More Iterest ormulas Uform Seres Compoud Iterest ormulas Why? May paymets are based o a uform paymet seres. e.g. automoble loas, house paymets, ad may other loas. 2 The Uform aymet Seres s 0

More information

Chapter 4. More Interest Formulas

Chapter 4. More Interest Formulas Chapter 4 More Iterest ormulas Uform Seres Compoud Iterest ormulas Why? May paymets are based o a uform paymet seres. e.g. automoble loas, house paymets, ad may other loas. 2 The Uform aymet Seres s 0

More information

Overview. Linear Models Connectionist and Statistical Language Processing. Numeric Prediction. Example

Overview. Linear Models Connectionist and Statistical Language Processing. Numeric Prediction. Example Overvew Lear Models Coectost ad Statstcal Laguage Processg Frak Keller keller@col.u-sb.de Computerlgustk Uverstät des Saarlades classfcato vs. umerc predcto lear regresso least square estmato evaluatg

More information

A Test of Normality. Textbook Reference: Chapter 14.2 (eighth edition, pages 591 3; seventh edition, pages 624 6).

A Test of Normality. Textbook Reference: Chapter 14.2 (eighth edition, pages 591 3; seventh edition, pages 624 6). A Test of Normalty Textbook Referece: Chapter 4. (eghth edto, pages 59 ; seveth edto, pages 64 6). The calculato of p-values for hypothess testg typcally s based o the assumpto that the populato dstrbuto

More information

1036: Probability & Statistics

1036: Probability & Statistics 036: Probablty & Statstcs Lecture 9 Oe- ad Two-Sample Estmato Problems Prob. & Stat. Lecture09 - oe-/two-sample estmato cwlu@tws.ee.ctu.edu.tw 9- Statstcal Iferece Estmato to estmate the populato parameters

More information

CHAPTER 8. r E( r ) m e. Reduces the number of inputs for diversification. Easier for security analysts to specialize

CHAPTER 8. r E( r ) m e. Reduces the number of inputs for diversification. Easier for security analysts to specialize CHATE 8 Idex odels cgra-hll/ir Copyrght 0 by The cgra-hll Compaes, Ic. All rghts reserved. 8- Advatages of the Sgle Idex odel educes the umber of puts for dversfcato Easer for securty aalysts to specalze

More information

0.07 (12) i 1 1 (12) 12n. *Note that N is always the number of payments, not necessarily the number of years. Also, for

0.07 (12) i 1 1 (12) 12n. *Note that N is always the number of payments, not necessarily the number of years. Also, for Chapter 3, Secto 2 1. (S13HW) Calculate the preset value for a auty that pays 500 at the ed of each year for 20 years. You are gve that the aual terest rate s 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01 0.07

More information

Deriving & Understanding the Variance Formulas

Deriving & Understanding the Variance Formulas Dervg & Uderstadg the Varace Formulas Ma H. Farrell BUS 400 August 28, 205 The purpose of ths hadout s to derve the varace formulas that we dscussed class ad show why take the form they do. I class we

More information

LECTURE 5: Quadratic classifiers

LECTURE 5: Quadratic classifiers LECURE 5: Quadratc classfers Bayes classfers for Normally dstrbuted classes Case : σ I Case : ( daoal) Case : ( o-daoal) Case : σ I Case 5: j eeral case Numercal example Lear ad quadratc classfers: coclusos

More information

FINANCIAL MATHEMATICS GRADE 11

FINANCIAL MATHEMATICS GRADE 11 FINANCIAL MATHEMATICS GRADE P Prcpal aout. Ths s the orgal aout borrowed or vested. A Accuulated aout. Ths s the total aout of oey pad after a perod of years. It cludes the orgal aout P plus the terest.

More information

CREDIT MANAGEMENT 3 - (SWC) CRM33B3 FINAL ASSESSMENT OPPORTUNITY. Date of examination: 5 NOVEMBER 2015

CREDIT MANAGEMENT 3 - (SWC) CRM33B3 FINAL ASSESSMENT OPPORTUNITY. Date of examination: 5 NOVEMBER 2015 Departmet of Commercal Accoutg CREDIT MANAGEMENT 3 - (SWC) CRM33B3 FINAL ASSESSMENT OPPORTUNITY Date of examato: 5 NOVEMBER 05 Tme: 3 hours Marks: 00 Assessor: Iteral Moderator: Exteral Moderator: Fred

More information

Monetary fee for renting or loaning money.

Monetary fee for renting or loaning money. Ecoomcs Notes The follow otes are used for the ecoomcs porto of Seor Des. The materal ad examples are extracted from Eeer Ecoomc alyss 6 th Edto by Doald. Newa, Eeer ress. Notato Iterest rate per perod.

More information

GAUTENG DEPARTMENT OF EDUCATION SENIOR SECONDARY INTERVENTION PROGRAMME MATHEMATICS GRADE 12 SESSION 3 (LEARNER NOTES)

GAUTENG DEPARTMENT OF EDUCATION SENIOR SECONDARY INTERVENTION PROGRAMME MATHEMATICS GRADE 12 SESSION 3 (LEARNER NOTES) MATHEMATICS GRADE SESSION 3 (LEARNER NOTES) TOPIC 1: FINANCIAL MATHEMATICS (A) Learer Note: Ths sesso o Facal Mathematcs wll deal wth future ad preset value autes. A future value auty s a savgs pla for

More information

Sorting. Data Structures LECTURE 4. Comparison-based sorting. Sorting algorithms. Quick-Sort. Example (1) Pivot

Sorting. Data Structures LECTURE 4. Comparison-based sorting. Sorting algorithms. Quick-Sort. Example (1) Pivot Data Structures, Sprg 004. Joskowcz Data Structures ECUE 4 Comparso-based sortg Why sortg? Formal aalyss of Quck-Sort Comparso sortg: lower boud Summary of comparso-sortg algorthms Sortg Defto Iput: A

More information

APPENDIX M: NOTES ON MOMENTS

APPENDIX M: NOTES ON MOMENTS APPENDIX M: NOTES ON MOMENTS Every stats textbook covers the propertes of the mea ad varace great detal, but the hgher momets are ofte eglected. Ths s ufortuate, because they are ofte of mportat real-world

More information

0.07. i PV Qa Q Q i n. Chapter 3, Section 2

0.07. i PV Qa Q Q i n. Chapter 3, Section 2 Chapter 3, Secto 2 1. (S13HW) Calculate the preset value for a auty that pays 500 at the ed of each year for 20 years. You are gve that the aual terest rate s 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01 0.07

More information

Accounting 303 Exam 2, Chapters 4, 5, 6 Fall 2016

Accounting 303 Exam 2, Chapters 4, 5, 6 Fall 2016 Accoutg 303 Exam 2, Chapters 4, 5, 6 Fall 2016 Name Row I. Multple Choce Questos. (2 pots each, 24 pots total) Read each questo carefully ad dcate your aswer by crclg the letter precedg the oe best aswer.

More information

SCEA CERTIFICATION EXAM: PRACTICE QUESTIONS AND STUDY AID

SCEA CERTIFICATION EXAM: PRACTICE QUESTIONS AND STUDY AID SCEA CERTIFICATION EAM: PRACTICE QUESTIONS AND STUDY AID Lear Regresso Formulas Cheat Sheet You ma use the followg otes o lear regresso to work eam questos. Let be a depedet varable ad be a depedet varable

More information

Optimal Reliability Allocation

Optimal Reliability Allocation Optmal Relablty Allocato Yashwat K. Malaya malaya@cs.colostate.edu Departmet of Computer Scece Colorado State Uversty Relablty Allocato Problem Allocato the relablty values to subsystems to mmze the total

More information

MEASURING THE FOREIGN EXCHANGE RISK LOSS OF THE BANK

MEASURING THE FOREIGN EXCHANGE RISK LOSS OF THE BANK Gabrel Bstrceau, It.J.Eco. es., 04, v53, 7 ISSN: 9658 MEASUING THE FOEIGN EXCHANGE ISK LOSS OF THE BANK Gabrel Bstrceau Ecoomst, Ph.D. Face Natoal Bak of omaa Bucharest, Moetary Polcy Departmet, 5 Lpsca

More information

Actuarial principles of the cotton insurance in Uzbekistan

Actuarial principles of the cotton insurance in Uzbekistan Actuaral prcples of the cotto surace Uzeksta Topc : Rsk evaluato Shamsuddov Bakhodr The Tashket rach of Russa ecoomc academy, the departmet of hgher mathematcs ad formato techology 763, Uzekstasky street

More information

Variance Covariance (Delta Normal) Approach of VaR Models: An Example From Istanbul Stock Exchange

Variance Covariance (Delta Normal) Approach of VaR Models: An Example From Istanbul Stock Exchange ISSN 2222-697 (Paper) ISSN 2222-2847 (Ole) Vol.7, No.3, 206 Varace Covarace (Delta Normal) Approach of VaR Models: A Example From Istabul Stock Exchage Dr. Ihsa Kulal Iformato ad Commucato Techologes Authorty,

More information

Accounting 303 Exam 2, Chapters 4, 6, and 18A Fall 2014

Accounting 303 Exam 2, Chapters 4, 6, and 18A Fall 2014 Accoutg 303 Exam 2, Chapters 4, 6, ad 18A Fall 2014 Name Row I. Multple Choce Questos. (2 pots each, 34 pots total) Read each questo carefully ad dcate your aswer by crclg the letter precedg the oe best

More information

A Hierarchical Multistage Interconnection Network

A Hierarchical Multistage Interconnection Network A Herarchcal Multstage Itercoecto Networ Mohtar Aboelaze Dept. of Computer Scece Yor Uversty Toroto, ON. CANADA M3J P3 aboelaze@cs.yoru.ca Kashf Al Dept. of Computer Scece Yor Uversty Toroto, ON. CANADA

More information

Solutions to Problems

Solutions to Problems Solutos to Problems ( Pt Pt + Ct) P5-. LG : Rate of retur: rt Pt Basc ($,000 $0,000 + $,500) a. Ivestmet X: Retur.50% $0,000 Ivestmet Y: Retur ($55,000 $55,000 + $6,800).36% $55,000 b. Ivestmet X should

More information

May 2005 Exam Solutions

May 2005 Exam Solutions May 005 Exam Soluto 1 E Chapter 6, Level Autes The preset value of a auty-mmedate s: a s (1 ) v s By specto, the expresso above s ot equal to the expresso Choce E. Soluto C Chapter 1, Skg Fud The terest

More information

Poverty indices. P(k;z; α ) = P(k;z; α ) /(z) α. If you wish to compute the FGT index of poverty, follow these steps:

Poverty indices. P(k;z; α ) = P(k;z; α ) /(z) α. If you wish to compute the FGT index of poverty, follow these steps: Poverty dces DAD offers four possbltes for fxg the poverty le: - A determstc poverty le set by the user. 2- A poverty le equal to a proporto l of the mea. 3- A poverty le equal to a proporto m of a quatle

More information

AMS Final Exam Spring 2018

AMS Final Exam Spring 2018 AMS57.1 Fal Exam Sprg 18 Name: ID: Sgature: Istructo: Ths s a close book exam. You are allowed two pages 8x11 formula sheet (-sded. No cellphoe or calculator or computer or smart watch s allowed. Cheatg

More information

Online Encoding Algorithm for Infinite Set

Online Encoding Algorithm for Infinite Set Ole Ecodg Algorthm for Ifte Set Natthapo Puthog, Athast Surarers ELITE (Egeerg Laboratory Theoretcal Eumerable System) Departmet of Computer Egeerg Faculty of Egeerg, Chulalogor Uversty, Pathumwa, Bago,

More information

Forecasting the Movement of Share Market Price using Fuzzy Time Series

Forecasting the Movement of Share Market Price using Fuzzy Time Series Iteratoal Joural of Fuzzy Mathematcs ad Systems. Volume 1, Number 1 (2011), pp. 73-79 Research Ida Publcatos http://www.rpublcato.com Forecastg the Movemet of Share Market Prce usg Fuzzy Tme Seres B.P.

More information

An Efficient Estimator Improving the Searls Normal Mean Estimator for Known Coefficient of Variation

An Efficient Estimator Improving the Searls Normal Mean Estimator for Known Coefficient of Variation ISSN: 2454-2377, A Effcet Estmator Improvg the Searls Normal Mea Estmator for Kow Coeffcet of Varato Ashok Saha Departmet of Mathematcs & Statstcs, Faculty of Scece & Techology, St. Auguste Campus The

More information

The Firm. The Firm. Maximizing Profits. Decisions. ECON 370: Microeconomic Theory Summer 2004 Rice University Stanley Gilbert

The Firm. The Firm. Maximizing Profits. Decisions. ECON 370: Microeconomic Theory Summer 2004 Rice University Stanley Gilbert The Frm The Frm ECON 370: Mcroecoomc Theory Summer 004 Rce Uversty Staley Glbert A Frm s a mechasm for covertg labor, captal ad raw materals to desrable goods A frm s owed by cosumers ad operated for the

More information

Measures of Dispersion

Measures of Dispersion Chapter IV Meaure of Dpero R. 4.. The meaure of locato cate the geeral magtue of the ata a locate oly the cetre of a trbuto. They o ot etablh the egree of varablty or the prea out or catter of the vual

More information

Accounting 303 Exam 2, Chapters 5, 6, 7 Fall 2015

Accounting 303 Exam 2, Chapters 5, 6, 7 Fall 2015 Accoutg 303 Exam 2, Chapters 5, 6, 7 Fall 2015 Name Row I. Multple Choce Questos. (2 pots each, 30 pots total) Read each questo carefully ad dcate your aswer by crclg the letter precedg the oe best aswer.

More information

Application of Portfolio Theory to Support Resource Allocation Decisions for Biosecurity

Application of Portfolio Theory to Support Resource Allocation Decisions for Biosecurity Applcato of Portfolo Theory to Support Resource Allocato Decsos for Bosecurty Paul Mwebaze Ecoomst 11 September 2013 CES/BIOSECURITY FLAGSHIP Presetato outle The resource allocato problem What ca ecoomcs

More information

Statistics for Journalism

Statistics for Journalism Statstcs for Jouralsm Fal Eam Studet: Group: Date: Mark the correct aswer wth a X below for each part of Questo 1. Questo 1 a) 1 b) 1 c) 1 d) 1 e) Correct aswer v 1. a) The followg table shows formato

More information

Measuring the degree to which probability weighting affects risk-taking. Behavior in financial decisions

Measuring the degree to which probability weighting affects risk-taking. Behavior in financial decisions Joural of Face ad Ivestmet Aalyss, vol., o.2, 202, -39 ISSN: 224-0988 (prt verso), 224-0996 (ole) Iteratoal Scetfc Press, 202 Measurg the degree to whch probablty weghtg affects rsk-takg Behavor facal

More information

The Complexity of General Equilibrium

The Complexity of General Equilibrium Prof. Ja Bhattachara Eco --Sprg 200 Welfare Propertes of Market Outcomes Last tme, we covered equlbrum oe market partal equlbrum. We foud that uder perfect competto, the equlbrum prce ad quatt mamzed the

More information

b. (6 pts) State the simple linear regression models for these two regressions: Y regressed on X, and Z regressed on X.

b. (6 pts) State the simple linear regression models for these two regressions: Y regressed on X, and Z regressed on X. Mat 46 Exam Sprg 9 Mara Frazer Name SOLUTIONS Solve all problems, ad be careful ot to sped too muc tme o a partcular problem. All ecessary SAS fles are our usual folder (P:\data\mat\Frazer\Regresso). You

More information

The Prediction Error of Bornhuetter-Ferguson

The Prediction Error of Bornhuetter-Ferguson The Predcto Error of Borhuetter-Ferguso Thomas Mac Abstract: Together wth the Cha Ladder (CL method, the Borhuetter-Ferguso ( method s oe of the most popular clams reservg methods. Whereas a formula for

More information

Allocating Risk Dollars Back to Individual Cost Elements

Allocating Risk Dollars Back to Individual Cost Elements Allocatg Rsk Dollars Back to Idvdual Cost Elemets Stephe A. Book Chef Techcal Offcer MCR, LLC sbook@mcr.com (0) 60-0005 x 0th Aual DoD Cost Aalyss Symposum Wllamsburg VA -6 February 007 007 MCR, LLC Approved

More information

Algorithm Analysis. x is a member of the set P x is not a member of the set P The null or empty set. Cardinality: the number of members

Algorithm Analysis. x is a member of the set P x is not a member of the set P The null or empty set. Cardinality: the number of members Algorthm Aalyss Mathematcal Prelmares: Sets ad Relatos: A set s a collecto of dstgushable members or elemets. The members are usually draw from some larger collecto called the base type. Each member of

More information

Feature Selection and Predicting CardioVascular Risk

Feature Selection and Predicting CardioVascular Risk Feature Selecto ad Predctg CardoVascular Rsk T.T.T.Nguye ad D.N. Davs, Computer Scece, Uversty of Hull. Itroducto No gold stadard ests for assessg the rsk of dvdual patets cardovascular medce. The medcal

More information

A point estimate is the value of a statistic that estimates the value of a parameter.

A point estimate is the value of a statistic that estimates the value of a parameter. Chapter 9 Estimatig the Value of a Parameter Chapter 9.1 Estimatig a Populatio Proportio Objective A : Poit Estimate A poit estimate is the value of a statistic that estimates the value of a parameter.

More information

The Application of Asset Pricing to Portfolio Management

The Application of Asset Pricing to Portfolio Management Clemso Ecoomcs The Applcato of Asset Prcg to Portfolo Maagemet The Nature of the Problem Portfolo maagers have two basc problems. Frst they must determe whch assets to hold a portfolo, ad secod, they must

More information

ON MAXIMAL IDEAL OF SKEW POLYNOMIAL RINGS OVER A DEDEKIND DOMAIN

ON MAXIMAL IDEAL OF SKEW POLYNOMIAL RINGS OVER A DEDEKIND DOMAIN Far East Joural of Mathematcal Sceces (FJMS) Volume, Number, 013, Pages Avalable ole at http://pphmj.com/jourals/fjms.htm Publshed by Pushpa Publshg House, Allahabad, INDIA ON MAXIMAL IDEAL OF SKEW POLYNOMIAL

More information

STATIC GAMES OF INCOMPLETE INFORMATION

STATIC GAMES OF INCOMPLETE INFORMATION ECON 10/410 Decsos, Markets ad Icetves Lecture otes.11.05 Nls-Herk vo der Fehr SAIC GAMES OF INCOMPLEE INFORMAION Itroducto Complete formato: payoff fuctos are commo kowledge Icomplete formato: at least

More information

Prediction Error of the Future Claims Component of Premium Liabilities under the Loss Ratio Approach. International Regulatory Changes

Prediction Error of the Future Claims Component of Premium Liabilities under the Loss Ratio Approach. International Regulatory Changes Predcto rror o the Future lams ompoet o Premum Labltes uder the Loss Rato Approach (accepted to be publshed ace) AS Aual Meetg November 8 00 Jacke L PhD FIAA Nayag Busess School Nayag Techologcal Uversty

More information

Integrating Mean and Median Charts for Monitoring an Outlier-Existing Process

Integrating Mean and Median Charts for Monitoring an Outlier-Existing Process Proceedgs of the Iteratoal MultCoferece of Egeers ad Computer Scetsts 8 Vol II IMECS 8 19-1 March 8 Hog Kog Itegratg Mea ad Meda Charts for Motorg a Outler-Exstg Process Lg Yag Suzae Pa ad Yuh-au Wag Abstract

More information

PORTFOLIO OPTIMIZATION IN THE FRAMEWORK MEAN VARIANCE -VAR

PORTFOLIO OPTIMIZATION IN THE FRAMEWORK MEAN VARIANCE -VAR Lecturer Floret SERBAN, PhD Professor Vorca STEFANESCU, PhD Departmet of Mathematcs The Bucharest Academy of Ecoomc Studes Professor Massmlao FERRARA, PhD Departmet of Mathematcs Uversty of Reggo Calabra,

More information

SEARCH FOR A NEW CONCEPTUAL BOOKKEEPING MODEL: Anne-Marie Vousten-Sweere and Willem van Groenendaal 1. November 1999

SEARCH FOR A NEW CONCEPTUAL BOOKKEEPING MODEL: Anne-Marie Vousten-Sweere and Willem van Groenendaal 1. November 1999 SEARCH FOR A NEW CONCEPTUAL BOOKKEEPING MODEL: DIFFERENT LEVELS OF ABSTRACTION Ae-Mare Vouste-Sweere ad Wllem va Groeedaal November 999 Abstract Nowadays, every bookkeepg system used practce s automated.

More information

Regional Workshop on the Use of Sampling in Agricultural Surveys

Regional Workshop on the Use of Sampling in Agricultural Surveys Regoal Worksop o te Use of Samplg Agrcultural Surveys MOTEVIDEO, URUGUAY, 0 4 Jue 0 REFERECE MATERIAL O SAMPLIG METHODS * FOOD AD AGRICULTURE ORGAIZATIO OF THE UITED ATIOS * Prepared by A.K. Srvastava,

More information

Mathematical Background and Algorithms

Mathematical Background and Algorithms (Scherhet ud Zuverlässgket egebetteter Systeme) Fault Tree Aalyss Mathematcal Backgroud ad Algorthms Prof. Dr. Lggesmeyer, 0 Deftos of Terms Falure s ay behavor of a compoet or system that devates from

More information

Two Approaches for Log-Compression Parameter Estimation: Comparative Study*

Two Approaches for Log-Compression Parameter Estimation: Comparative Study* SERBAN JOURNAL OF ELECTRCAL ENGNEERNG Vol. 6, No. 3, December 009, 419-45 UDK: 61.391:61.386 Two Approaches for Log-Compresso Parameter Estmato: Comparatve Study* Mlorad Paskaš 1 Abstract: Stadard ultrasoud

More information

Basic consumption and income based indicators of economic inequalities in Bosnia and Herzegovina: evidence from household budget surveys

Basic consumption and income based indicators of economic inequalities in Bosnia and Herzegovina: evidence from household budget surveys Workg paper 3 August 207 UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS Expert meetg o measurg poverty ad equalty 26-27 September 207, Budva, Moteegro Sesso D: Methodologcal

More information

Qiang LIU 1, Shu-juan XIA 2. 3) They have a good life and business environment, Always been decorated luxurious

Qiang LIU 1, Shu-juan XIA 2. 3) They have a good life and business environment, Always been decorated luxurious Aalyze ad evaluate o the vestmet rsks of the Servced Apartmets uder the ew Real Estate Regulato Polces take the developg Servced Apartmet Jao Na as a example Qag LIU 1, Shu-jua XIA 2 1 The Ocea Uversty

More information

DEGRESSIVE PROPORTIONALITY IN THE EUROPEAN PARLIAMENT

DEGRESSIVE PROPORTIONALITY IN THE EUROPEAN PARLIAMENT M A T H E M A T I C A L E C O N O M I C S No. 7(4) 20 DEGRESSIVE PROPORTIONALITY IN THE EUROPEAN PARLIAMENT Katarzya Cegełka Abstract. The dvso of madates to the Europea Parlamet has posed dffcultes sce

More information

Management Science Letters

Management Science Letters Maagemet Scece Letters (0) 355 36 Cotets lsts avalable at GrowgScece Maagemet Scece Letters homepage: www.growgscece.com/msl A tellget techcal aalyss usg eural etwork Reza Rae a Shapour Mohammad a ad Mohammad

More information

MOMENTS EQUALITIES FOR NONNEGATIVE INTEGER-VALUED RANDOM VARIABLES

MOMENTS EQUALITIES FOR NONNEGATIVE INTEGER-VALUED RANDOM VARIABLES MOMENTS EQUALITIES FOR NONNEGATIVE INTEGER-VALUED RANDOM VARIABLES MOHAMED I RIFFI ASSOCIATE PROFESSOR OF MATHEMATICS DEPARTMENT OF MATHEMATICS ISLAMIC UNIVERSITY OF GAZA GAZA, PALESTINE Abstract. We preset

More information

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring No-lfe surace mathematcs Nls F. Haavardsso, Uversty of Oslo ad DNB Skadeforskrg Repetto clam se The cocept No parametrc modellg Scale famles of dstrbutos Fttg a scale famly Shfted dstrbutos Skewess No

More information

Method for Assessment of Sectoral Efficiency of Investments Based on Input-Output Models 1

Method for Assessment of Sectoral Efficiency of Investments Based on Input-Output Models 1 Global Joural of Pure ad Appled Mathematcs. ISSN 0973-1768 Volume 12, Number 1 (2016), pp. 19-32 Research Ida Publcatos http://www.rpublcato.com Method for Assessmet of Sectoral Effcecy of Ivestmets Based

More information

Regional Workshop on use of Sampling for Agricultural Census and Surveys May, 2012, Bangkok, Thailand

Regional Workshop on use of Sampling for Agricultural Census and Surveys May, 2012, Bangkok, Thailand Regoal Worksop o use of Samplg for Agrcultural Cesus ad Surveys 4-8 ay, 0, Bagkok, Talad REFERECE ATERIAL O SAPLIG ETHODS Food ad Agrculture Orgazato of te Uted atos Statstcs Dvso Idex I. SAPLIG SCHEES.

More information

Statistics for Economics & Business

Statistics for Economics & Business Statistics for Ecoomics & Busiess Cofidece Iterval Estimatio Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for the mea ad the proportio How to determie

More information

Linear regression II

Linear regression II CS 75 Mache Learg Lecture 9 Lear regresso II Mlos Hauskrecht mlos@cs.ptt.eu 539 Seott Square Lear regresso Fucto f : X Y Y s a lear combato of put compoets f ( w w w w w w, w, w k - parameters (weghts

More information

6. Loss systems. ELEC-C7210 Modeling and analysis of communication networks 1

6. Loss systems. ELEC-C7210 Modeling and analysis of communication networks 1 ELEC-C72 Modelg ad aalyss of commucato etwors Cotets Refresher: Smple teletraffc model Posso model customers, servers Applcato to flow level modellg of streamg data traffc Erlag model customers, ; servers

More information

Comparison between the short-term observed and long-term estimated wind power density using Artificial Neural Networks.

Comparison between the short-term observed and long-term estimated wind power density using Artificial Neural Networks. Comparso betwee the short-term observed ad log-term estmated wd power desty usg Artfcal Neural Networks. A case study S Velázquez, JA. Carta 2 Departmet of Electrocs ad Automatcs Egeerg, Uversty of Las

More information

THE NPV CRITERION FOR VALUING INVESTMENTS UNDER UNCERTAINTY

THE NPV CRITERION FOR VALUING INVESTMENTS UNDER UNCERTAINTY Professor Dael ARMANU, PhD Faculty of Face, Isurace, Baks ad Stock xchage The Bucharest Academy of coomc Studes coomst Leoard LACH TH CRITRION FOR VALUING INVSTMNTS UNDR UNCRTAINTY Abstract. Corporate

More information

Uncertainties in building acoustics

Uncertainties in building acoustics Ucertates buldg acoustcs Volker Phskalsch-Techsche Budesastalt, 388 Brauschweg, Budesallee 00, Germa, {volker.wttstock@ptb.de}, The ucertates assocated wth the arbore soud sulato are vestgated. Startg

More information

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Copyrght 203 IEEE. Reprted, wth permsso, from Dgzhou Cao, Yu Su ad Huaru Guo, Optmzg Mateace Polces based o Dscrete Evet Smulato ad the OCBA Mechasm, 203 Relablty ad Mataablty Symposum, Jauary, 203. Ths

More information

The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm

The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm Joural of Software Egeerg ad Applcatos, 013, 6, -9 do:10.436/jsea.013.67b005 Publshed Ole July 013 (http://www.scrp.org/joural/jsea) The Costraed Mea-Semvarace Portfolo Optmzato Problem wth the Support

More information

Ranking and Aggregation of factors affecting companies attractiveness

Ranking and Aggregation of factors affecting companies attractiveness Rakg ad Aggregato of factors affectg compaes attractveess Zoumpola Dkopoulou Faculty of Computer Scece Hasselt Uversty Depebeek, Belgum zoumpola.dkopoulou@studet.uhasselt.be Elpk Papageorgou Departmet

More information

Profitability and Risk Analysis for Investment Alternatives on C-R Domain

Profitability and Risk Analysis for Investment Alternatives on C-R Domain roftablty ad sk alyss for Ivestmet lteratves o - Doma Hrokazu Koo ad Osamu Ichkzak Graduate School of usess dmstrato, Keo Uversty 4-- Hyosh, Kohoku-ku, Yokohama, 223-826, Japa Tel: +8-4-64-209, Emal: koo@kbs.keo.ac.p

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

A Coverage Probability on the Parameters of the Log-Normal Distribution in the Presence of Left-Truncated and Right- Censored Survival Data ABSTRACT

A Coverage Probability on the Parameters of the Log-Normal Distribution in the Presence of Left-Truncated and Right- Censored Survival Data ABSTRACT Malaysa Joural of Mathematcal Sceces 9(1): 17-144 (015) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: http://espem.upm.edu.my/joural A Coverage Probablty o the Parameters of the Log-Normal

More information

. (The calculated sample mean is symbolized by x.)

. (The calculated sample mean is symbolized by x.) Stat 40, sectio 5.4 The Cetral Limit Theorem otes by Tim Pilachowski If you have t doe it yet, go to the Stat 40 page ad dowload the hadout 5.4 supplemet Cetral Limit Theorem. The homework (both practice

More information

Math 373 Fall 2013 Homework Chapter 4

Math 373 Fall 2013 Homework Chapter 4 Math 373 Fall 2013 Hoework Chapter 4 Chapter 4 Secto 5 1. (S09Q3)A 30 year auty edate pays 50 each quarter of the frst year. It pays 100 each quarter of the secod year. The payets cotue to crease aually

More information

DEPARTMENT OF ECONOMICS

DEPARTMENT OF ECONOMICS ISSN 089-2642 ISBN 0 7340 2586 6 THE UNIVERSITY OF MELBOURNE DEPARTMENT OF ECONOMICS RESEARCH PAPER NUMBER 930 MARCH 2005 INDIRECT TAXATION AND PROGRESSIVITY: REVENUE AND WELFARE CHANGES by Joh Creedy

More information

Making Even Swaps Even Easier

Making Even Swaps Even Easier Mauscrpt (Jue 18, 2004) Makg Eve Swaps Eve Easer Jyr Mustaok * ad Ramo P. Hämäläe Helsk Uversty of Techology Systems Aalyss Laboratory P.O. Box 1100, FIN-02015 HUT, Flad E-mals: yr.mustaok@hut.f, ramo@hut.f

More information

Portfolio Optimization: MAD vs. Markowitz

Portfolio Optimization: MAD vs. Markowitz Rose-Hulma Udergraduate Mathematcs Joural Volume 6 Issue 2 Artcle 3 Portfolo Optmzato: MAD vs. Markowtz Beth Bower College of Wllam ad Mary, bebowe@wm.edu Pamela Wetz Mllersvlle Uversty, pamela037@hotmal.com

More information

Quantitative Portfolio Theory & Performance Analysis

Quantitative Portfolio Theory & Performance Analysis 550.447 Quattatve Portfolo heory & Performace Aalyss Week February 11, 2013 Cocepts (fsh-up) Basc Elemets of Moder Portfolo heory Assgmet For Feb 11 (hs Week) ead: A&L, Chapter 2 ( Cocepts) ead: A&L, Chapter

More information

ETSI TS V1.2.1 ( )

ETSI TS V1.2.1 ( ) TS 0 50-6 V.. (004-0) Techcal Specfcato Speech Processg, Trasmsso ad Qualty Aspects (STQ); QoS aspects for popular servces GSM ad 3G etworks; Part 6: Post processg ad statstcal methods TS 0 50-6 V.. (004-0)

More information