gn the ate pouitical « = Ascope of stat Was p umanily Limited collection of data welated to data of tien Ff the county f fuy. by the eect. Jour ge popula of the @un YaMnG militauy © 4 phe eanuecst CerbuUA F populaten rducded by the ErpelowA of with the ‘ceormstucen of Sn trda,an efficent Ayskm SfAHAICA gover me nt fou revhaps, ene of ancl wealth wa, C6! egypt in connaticn (INOUA yuan as t mae of nial @ admintsbiatrue A ted even «00D ft AG Os ALEUNG the HetGr of rduagupta Uaurya in’ WOB.C., peatles Stats © ( ots & deaths even befoxe 300 B.C) aue available ‘in Kautalya’s puthashashtua, The econ of land aguitel Lue & wealth Atats Were wntaind by © & mal. the tand and Heventie mitaistes tn the Hen oF Akhay i St Catuuy AD em Cntwuy saw he ap litation of stats {or Me collect@a of the data uelated to Me movement o heaventy hoaivs, slaw @ panes to con & prediction of KUCpres. B know about thevt posit ex i wegistuation of bowhs ¥WA Crtaweg utttrressect London, A Statistics G@ptain Jen r 1p Stats WUAS, 1620, known as 17 qf 2 study Me V7 man te make pe backbone the pith and deat Up aah pucbability of the so-Called CML FT el utntth Ula on the | OF ger 17h Contueey, The #Hench alevelopect 7 sal 623 and T.Teumat, after mathe 0 wespon dence hetueen th ernselves sh, saccade tn solving the pxeblem youn Reseanrhers ti the development _ 6 of stats, englishmen like fants { 2 utho gave the concep of eQHEssIOn Kaul POuWsen 1857 wre founded the Gounclation analysis. he fauaue test dines of fit, 5t1 Kenald_ A. Fisher 1890 6 applied Atathae (a vaselety Tela such 1 genetics § psychology education , agsialtivte ete, and whe 4 Hightly tuned aa the yath ex of satire, } — __ Stbguilan weaning of Atatistts ipwioap yhe weyd siati tis & commonly usec th tue bse A) Plutal — Ff means (lection of numeucod {ack Like sfatcsties of populatron , Buta , deaths , peice puceduchOn otc. Jul — stati means A teed ox methact usect fh Collection, analyak and Antcupu an? ; eat of cata 4 ; i . DE On * Accaudin tateme: enqutiy ~ Aaoud be defi analys: data” Functic 42 Epato « (as = t = Acouding to f oy» bia statements of , it bility placed in sielaton 1ench vicauding t ¢ o @ud Layers seg Pre ; >) nction ¢ f 5 sah 3tie rere > &x, ono jac a wof stat b fo expe louyn Of numbeus 4© a in the them make them Mone [ith the helf of st afastics like unempleyment honesty » also be changed infe feumute 2 Compausen © Sati whe hel of 1 Me mean tg anne in quantitative funy ou CL, ta com” of © gc Boy f a cenclud 3 ual ma 7 e people nau hy AEM ple t mi ci the 6thev may © DA [ew cu but when a A com gs onsn n © newt counties Sey U a wutld get lem gact @ uelation & PS frog moll ye method of, sect sith Ek las ly ne noe . efatin quocia won Ee ia oe Wr ojo een M ie egos ae and ©35 1 foumetate fotivies Statistical methects tan be wsect Jon Bucing Politics like inpou out of ab (us op oui fencer planning & administrative pé Bn ONE founulatet y anilysing the wllected data the matey, 12 sed not only for the analysis of the cusuent fats but Ase sHimation of future yack by uing fhe thteupolation, exhiapolatian and ote. [ae C5.cleas with agquegate items & not with Ql units Fou 24..the Income of a family 0B ESODD acesn't convéy satétial meaning tuhile the statement an inceme o ts 73050 fed Ment con SO familes Ys STatisnal sense deals only utith. quantitative data, Hate phenemeng like “hones » POVEELEL et Cannot be analysed Sratitially unless these attuibutes aug assigned! Aumtable quantita. TIVE MCOAUMES | * dfatintal Jau ane fiue eply wn at avounge tus of Atatistics ave not URIVEMA QU a 4pplable. Whese may not be tuue i Ud Q Patbialay tndtvidual. + AMititis Gn be mistued & mizinteu wehEed/, > He linitakon Of stabs 3 thal ta Gee, a be misused, he Mite MAY QuBe due to Aeveyal MEAONS, HOY OG. Luhen CENliustorns aue bared ” Incemplefe info, ou ave duawn by unskilled) . MV ES TIFTnue ation @) @llectian of datas Fis the backbone of siatisticol en qubbuys* wllection of | aata is net in puoper lawn Teen the conclusion Auawun Con Neved be welable the sounce Of data may be p* eumary OA ondary (é) Ouganisation é 3 ws genewally or anaed NM 2. ect of tabulation intewp Ayre @llec é eee ze, (t's tabulated , the © byt fo aviange the ee eA cle % tolumr [OH as the sentation yesentect y complete cla f gd a eithet {o Pk of data A ConceNne data wpougn ey a ou guaphs ea cee Analysds : ey Ht once *he date B 5 euganied 6 G purcsented , TE mani ta necé whep On obje cS ae: ant 3. Gia re UG A Tl Wuaion + recs to PEPE a te to hive at ceutar oe toda { qnalysn oa rte if Ff nd n easUHA > ts, 4 ak aa tile aiid , 3e emi- quostile n cle viation, Hani rn y witile reviation m oofficrent of ¥ vautance & MGS el Han soos a Geviation, Pr 2 COMM est kelly (te) skewnehs sconetatior, egae © @.) pntenpxteeet a 7 od & to “he, (ast ut 9 ti tical, oe4 netan Y Analysed. fhe fnroupctaver Of OO GU ad CAsYy For & sequiues ia Pi Les aue not PHopalg C he aniily sch A CnguUitly | Heapcrat he unre cojerttv of ae yail classifitation * ’ > Bala ceoltectien anil dassif! a ¢ Genit sat, cleats with tre applitation 4 a methads 18 specific Pade If we : | cg “mate phe investmen?# Ou Vif? e has to ke i ( oe special recPnid tc ay Og ppl battstics thw meth eels 79 ‘Yo achieve me appiianen e/ 2 t specific pHoblern Gs population, natenal nme, 1ages, price etc, The applied Stars Can be claisigiec! tints yollauung 3 D Sescuiptive Staristtcs 2 t's cenceaned with the ic & uthich natwrally Nolin die Daeame Be Past, the business “atatBHic, ase applied 4 descuiptive stats as they aye cence ned wrth the analyst, mensuxemenh& pusentation bis facts. thus descpiive Sats deals uP py the exixtin hots & yiguites au Yack: of Yhe a) 29. populatien Stats déiuss /he Nate, 9, causes of pepulation Auning if peniod tke (ensus of 2001 shows the population of India upto 2004 only. * Ga Papal Inductive stati nes 2 Ht Hees 10 the foucdishng, puedithions, eatin On judgement rib [ox Lp hike on the Mee Ae lem sampling technique: the cenclusfen ved thucugh sindem sampling aue supporteel by stuong markematical quountls 2s such these @S$ OF STATISTICS | nes compl btAtAtA systematic MANNE jhe unp2t Jiguues na in Yauch a {oum that they ase Ce Human mond cannet undeustand e,°8 the Im ond ges af ene; Wie na fu" oth a to AIMpIyt b comple data Wm NN matic abn wuepuenentarien, Av suememben the “aruclents, but no ks whe stimaley DOs ~)1 SIE puesond yacls tna clef HE se 3 . @ndit , define. OM) € Sats Ep hms ‘oT of WunatB BeinE € thus Di so eyed COnlusiends Me >; i q fh Alakc! Tne pucduckion of paddy gat te ¥ / tonnes (tn B02: poids 7 SOUL gives @ definile tnfo., theo a ump Wes Like #he Popul: of indigh av Se, ware LB ner nities but ot the popul of mda & GuOUUAG aT pat stats bcuz fF CONVveys GA MCAN NG ¥ “ifOPES COMpartsEp 2 s ene af Fhe imp. yur 4 A1G6.Tt helps th paung tne data wat. Hime location, # ase heir ts 7 Compane Gne phenomena, ulith the orheu. The Vauious means of CompauGen aue HOFOS , QUCHAGA , HALE , LO- Gfrcnts ek, £9. > the liuacy uate th INADA stOOd AF 36%, A c00U as aGguinst B4% th 2003. 4 tt hes tn foumulating & resting G_hYypophesea 2 Afathtial methecls aue helpful to develap ne thenules. He abe helps En yeunulating a festic hypothes4. Fox eg. we can veuify the te dupply with the help of 0h. dimilarty the AUC, 64 goars of New Theale Gan be known asily uth the help oo Stara cal alate, it heirs to “pnecacine hE Ufecl Wf HBC th inks se on AWiINGS & invesymenk & i? pHovide widante i the polite eC {oun ulation of Rew theauies & Se Tt {OUees futune Couey the methods aue vy Y pltwe dis on the aap OE wi, rae of m co! ee OTE hens Of Coens Econditions. Aappna) memods Hey, 17 tre ey mulatTor? ou futuye polises by anatys ins past & pue sent end?) 5B making pegpetio by ou fun. 7he Arye You XPHG Z HWE es Ne feidien is highly APhearues stabatical fel usepel {© { JOUCCAMAG {eoto a giver 1e4/ tudy AN ONnGetAy PHOLES cheb lity of decision makihg by ng alteunaliv€e couse of acon tna TRG PHOCCSS Lele ching an approprak Guessing Ihe Causes & pyobable 1 cutain CAUACHERT STIS. in given situations italy the destue to undewstand an Anew phenemena fu tial hata ase the eur process of measwutag, Counhis may tead to Bee) uUpeak & PHOb/Em adue measuucble, quantifiable coun fable, cl lhile cenduchig a AUUeY th @ stucly, inves develops a method te ask seveual qubspens deal with the vatiety of chawactes tie, of the Fver PEpulahicn | univeise © due teumedl Ad Variab lex [Oveg.> GCrsumex behaviouy, féL 10. 2., f Jeb sans/action, AeInking (smoking Aabjts leadeuhip abilities etc, : The chefte of data célletion methed from Q Pastiulan souue depends “pon the Yacrlities QAVAhAbS the extend o ACUMALY, HQUiNed Gr analyse, the expeutie of the invesmoatey, the , g & chseuviratau deosn't a beh aviOut as opseuvation studies, the invang ad he uecouds the metimes, mechantaa lelectiontc ucceud destied cata. Diveused ns of paupitulan adease could! be cbseavaronal study westions , inst ifecuu. dc ce Gu Ovex © time is fo be tenclucted eftheu {ace- to-4a , Diueck 7Areuviewta asle eKPENATY Consuming a big sample of Hespencdants J : personally mn fey vieutect An teH vieut es biase come tn the uiay should! | duch infeuyrecus 1 at the x, Be arages ‘sesearch 10 handle { sruatenad fae Py cd wet of quest | edhe Tee generiad seation of qMestiona que — ait —multiple choice »-open- ended question ons fou extracting 760 Y¥ts a {OUMUM e Tugel nespondants Ztomous (Yes/No s1esponse) 4(nochoices‘ Seondary Sata dewces ? reondohy Oata neyens Fo ¢hase date which have een, purpose offer than the Besides Neuspape of Auch info are & Collected! Canlion {OH wome Analysts curnentig beng undentaken, } business Magazines, Ether SOUnES 2 exteunal Secondary Sata ee 8 ‘ Ht atucles cendua) AtatAHtal eug, data, national fA awcounts Atatatics which contas estimates of 2 national income fou several years, gaoutth Hote, wale en mojo eonomic Acts, Auch as Gi FULUtUre Tndustry, tuacte ett. Wholesale puice Index published by the office of Konoryc asa, nist of commeie G inclustry, COMUMCH pike index, KA! bul economic survey, the vartour hombeus F Ommence, BSE , NSE elt. Yateunal Aecencary Mata dources? hte. genouated within an wug. in the puocess of Metre business activities aue mefewted 10 ad inteunal secondary dato. =Anantial A[Cs, proctuetion, quality cenbel € dales wecoucls axe examples of such data GES & ADVANTAGES -advANTAGES >auny to collece Involves lessen Hine 6 Cost haps can be elentifred easily & steps an be taken paiemptly to oveucome the “same DISADVANTAGES | peel ogre Uni} i yt EY iit of employers, gross aales*G-p. etc. of — hale of mecswxement may also be different. te cleclonod™ by VaNGUA Co, mouy have break. a. : . are * kei the ne, ® 15-20%.& 40 6N. 10 a 4 4{.(, who have euvan tas 167 above , such date ts o re tse+ may, not be Hi stared in rem ett. be dijferert baeakup of €/02, (01% ing to know the nb ihe such above , such cate. A selecting a samp process Gs Her nults Called sample bit aue made about the pop Gastance a pe tne ‘Hantom act of peop! the pucpe He, Poe may get fiom auditor selects Ht the sample mean fe “i Ye cseaws conclu jood cons on of f youth: eMeel wagon of + cl onarantty stead of atrempry uation, ta de f counting biol ¢ mandom aC Fime uequiaed fo Gntack the wi of ceutain #5 | 3. Hestuut Hue natue Ag 2 gy y membey o4 element & ‘nas an equal d ine lent elected! again & agers when a fom the populaation-Fe duow & need a compere dS conte so ney eath eve cample ute A the population &element can be B Glled pram sample ef 5) sample 6 Aelected b gxOUps aL Clusteu au do nod hal €4.3 om" A an ouc pHoduct yy Computer O4 Atuategre 1ocedtuuse Gives UA CVE PopulaHen an equa the sample cattle fuar populah wrth POL J homogengua compaiedl a Whole. Thus, population & rio mufually € / ups Gallet aue ueleva ppHopurate to the sample called a sample q alfa 64 guoup in wopention Ye. tts srte. As the topontiond sampling pxocedume t the No.of element ih each stuaira pHopeution as the populatror ¢ h non -pH0ope tonal & the epposite. Thus samp PHotcediuie B mexe efficent than he simple Hanilom sampuhs becuse you the same sample Aize,we ger moue HEepuescn tativencss JOM each imp. segment of rhe population : () Cuter Sampling Method 2 TAG noun, OA QUR 4ampling method uthFa has been divideot to meet the puobiem of cart.d nadeqase sampling Home, The entive population to be analy fs divider inte small chunks elements 6 a sample of the deatued! noRn oleme f 4, called 9 ‘a mple § cons! 1179 A= ie [so ao ,thws,@ a samy d “sysionatie yy jhixough the k= rdentih eel by, UG popu! ibtion Oi vrentttgi§ cuameorn nlhs Bon heobability Metned @€nvénience Sampling * I thes puocedune, units to be inctucted ip this sanople tye aelecke at the convenience af the | Investig ato Mathew than any by any pxcspect{iad, Probability of being saected , tat e.g.» a Atucent Ter KG puoject en food! habits among adults ay wae hia Guin fuiencls th the college to constivute a 4ample bequse they ave easily available & Will ¢ Pauticipate fey Uitte 64 no cast, btheu, examples Que publ opinion 4xuyvey Conducted by any p TWehannel neay the “ofhuag station, hus os ow 7 a mouket. tts not passrhie valuate the Sepuesentativeness of the population sample Cbtacned fuem pha Fecrnique & hence PACQUITEN A gs AGU bE taken tr infexpuering the s1esutt omf Me (envenfent sample that ane used te make PHN aby OD hesiposiye in OUMATE, 0 ee eneten dyer ase Ph toebiatn information ye, % who uit! be 1, beauye ve the desine F240. 04 beaunse they éatafy to dome Bie Aent by “eseachey, i oO Tudgemental damplti 8 Tt invewes the Acle cts, Her : NAAN b inn bal position tc ovitle she deschert FE it's based on the aki & expeuence afm tavestigates, to select uthéhaample & at Hime & where, 50 thod the Prfistnfueences duc Mite 3 Coumect, the Jedgemen te) sarn pip & used _ i unlid nb.of Hespenclant, have yhe inf ilesmecteelPe tn 7 Such Ca an, * We Puiporeless & nol Lfer fea sary May Contain the geneualé oad dust re fact Pe one ee ee “espendants wihoave casiiy awn Validity of tho sample Mesuitts depencts ese ie vse ment of the investigates tx choo. @ Quaia dumpling 2 a « foun of pucpeutionale staatifiec! ste plicg & whic lpre adele psopextion of elements aue sampled frcrm diffe gups: ine population z. but en convenience basis, Yn oTheH WEN Y pre banrpling & the Adection uespendants Ue SIE? the investigatey although G9 Making auch / Jo make election, helahe must ensue that each, sespendanH satisfies ceutain aurea uthith & essential pou the atudy eg. > the Tnvestigator may choose _t0_inkeuview {O.utemen in such a way that & of them intome ofp? ROOCDD sof them, have biw BIOvODD- P26Dood & est OF 4 them éheuld 1d EUS 10 mtn & have annual m Dey Tp annual fame j, below FIOpUDD. Furtheum eve, SOME Hon 4b | be blw Qs 36 yes. of age sothows ees: phe balance coh tk yea None CLASSIFICATION & TABULATION OF DATA Classifftation i phe puccess of cumant ing data into eee e ges ee, to heads commen : chomateuisticn eu separating ae into di bf s ee - A e auits Ahus, classrfication fmpsesre> Mle Sein. in Oi of BRO digf> classea y ewhit of the e Yicteumnined Tue , objectives a wahat f the inquiy. of stuclen' a QUE sewed in ev Hee | ae be classigfed! en th bos of 9 stale Of edorgurgrreas » sel’ giOr, 1h hetghis weg hts @ $0 Ou.thus, the ame e4 (on asst | the uni. acoudin cutheuia JO" eg, the YM Uthic, Beh of dati can he cla C5 tudy the Hela ke ification of given date w.s.4. 2 ov ool tat ‘ation puesent the huge uaw data Ba cd AB stad? compHhansh le dA © attempts to highlight the 4 uke the date Uitetes Er pariGon : TOaHen enable Us TE make Meaning Compasions depencling en the bas classification 69: the dase 445 04 CT eiQ & p IG Hatlon of students 7 1g 1O gencle &6 enable Us to make 9 Compauative stucly f{ the pxivilence of unr edu Ships 2 MoYe gender of the siden & pre culty joined” t th Unk. wil enable Uv che Helationship, blw these & CHifenin & tO Study Ao Facilitates the Atafatical tueatment of the daty 2 the auseangement of ote uniformity available yp Juuyhew analy 46, Inteupretation 4 b 1 the velum@nous Ketaiaoo data into solofively homogenous fi UOL OY Claas 16 ‘theby pts. cA Alnilavitg Peas + my midst divers, open cus CEA NOMOQeN?h a makes I- oy Protesing like atculation, data, aiff PPS. 64 lassen Bc nc of ways based En any 7 MetegnGable physitat, social ou mental chiud eulatic i. athith exhibits Variation IMG» el elements of ba lo Get whe given data, the yack tht dass will Olfffe t Gn +; eof another lass wi,t. dome Chavachecisy alled the bas ev wipoua tou dassifiatios Funclions he gi ndonse: the data qTypes OF CLASSIFICATIONS Haron, b Ceeguaph jcal Class bas nal data like fat ine yfold of agri ultra in SOW etc. Fou eg hectaue {or cliffs ountArer given below Anca usA Rkatan Russia china Ay Hla Audan LAE 2, Chuoneteg ical COs Classification 2 Sn thes, the | ne data aue Classi {feo Siete) in mme example & pxlc ue induatuial ConceHn ie aij 2 pexio a big bis house diff. YOu, we any country 4o4 iff» Years . FollQLuing ie one which shows the pe pueden of Yn to t tf decades. Population Gh O«)“y 3. Yualijative ¢ n 4 Whonwe dal Bre clautitd te some qualiasive _ DHEA eT ue norcapasle of quantrayie measoement employment inked ip the. classification 3 Fw qualrtahve a1 descuipive with attuibules.%n qualitative class,, the data ate ws fo the puesenee jabsence of the tiibutes tn the given oe ple classet7ed inte only wuts Gn CH! a like (h puesence /aksence amg. vouteus Unita, the lass ipitation & feumecl ar simple O4 dit tomeua g agiven population o at honest |dishones? , male/gemale , employed | unemployed, beautizul)not- beatiful, Houseve> if the given pop.G Clamified into maue than @ Classes w.sid,a given athitbute, H's dard 70 be manigeld classi#ication. £9. fou the atlat buke o; intelligence, verrto.w dasses may be,day, gen ius, very MIELL , AVG ink, below ag & dull. 4> Quantitative dassi-ticn 2 Yh the data aue dassified en the basts i ees whith & capable of quantitative measudement = like age, hetght , weg ht 7) PHUCEA , PHOoduttfon ete, B teumed a quantitative lass rfrection , The. Quoustrative phenomena undeus tu & known a vadable and hence thas dossiitoHon b dometimes also called classifrcatien by vaiobles. Ff vauiable & a te eg phenomena uncle stully like mouks th a rest, helor ta] uebhk of student by a class, wages of woukew th a Ui of femrd as varrable, thase VHA les whith an Take all the possible values integuct a well a aliona! th_a given HaNge Are ttrmed 4s continuous varloble £4. age of Student A a Adheal, because aN ace"@an take all poste yatues a TF (an like ya. these va values cui toured a marks TH da cue te take only FREQUE! totadivid Serres « uhete | as dati the dat aAfeu data | useful the mi data | euder auna a as 7 Can be measured H (actions of 4 tke ys.,dmon ths, day? , niinuler, a€ ‘enc. etc fake al the posible these variables unable to values witha a Given specified Mange ate Rel os cecil AA fontinuou Cowie leas marks tn a fest out ef (OD4PRP Ff stucent x dawiete vartable alace in pits case, MaHks (ap take only Integsal valucs fiom 0- le 4 f FREQUENCY DISTRIBUTION € TABULATION to Individual * exsles of indiviclugl ebseuvation Aa SE dahove ems ane lated singulaxty after © sting ucahect pom lating them th ain the table below G cat data. “the belew puesentah cu foun doen't give Us ang @ & Hothex Confusing to wesentation of the below nage them th Mild % & called the AG OUGENGE data in 16 uf rageumation aie the mind . A bette data weuld be to aHHany exude of magnitude whic quuaying of cata no» Rel q 2 3 4 5 6 a/uselshhs i aa ¢ diah les 4 gaat 10 tse NA valueMagaire wele Aeules, He Wika a aue pre sented frat exnce mensuuemen t of units ly Inclitakdl. fhe 40% fig Bay: bl of off 9 7A él Ads LEP Oi ate | i > Othe UAs eS 5 At e bw the lourey & eu omit of the 4 the Class inten Val, the Class [0~ 20 ,the clos frieuval B 10 (80-10), Formuln 2 f= L-S TKO. Cag) * clos inlewvar 4 laugest item °.S= smallest Hem °K=no.of Classes IM 69. if the marks of 50 atu dents axe varia between | 60 GB if Me WAnt toy uM F class then the cl0bs injeuyol ena ct axe 60-10 I ae Cass Yntewval + the diffe ‘= ft depen Tata & the No.of ch Magnitude of the class infowe the HaNge of thé the uande ti the diff» blw the lag the smatieat ehseuvaton tn the given date Fou 0g. i the Hange of Mauka of a Gup , { q ; : 2 016 H we dexive te ‘es. then fhe Mag nitude be h class tnkouwal utor YI0= 5. Aclaw interval gencally be i mul 4 a0 dunth uould be handle @ Jacilitak cemputatior given a fermula jou dlckeumus Atuuges ha the no. f ¢ A the total no. of ehacuvation A 10D, no.of Ua%ser weuldl be 1+B3nxF (ql0v) 64 4+ (3-328X @)= you a) Aiuuges has dhe given jounula {on dereurtini-s the magnitude oj Has intewal. Re _ Range | 1+ 33a LogN wheue, =magnituae of class Tateuval (0g = logan thin of total no. of observ. a q ye fyy wh equal swig the stuuges (ounule , clasity Pr bans the ji. ctata of, hows Apent fh wouking by SO workers (Or peulod af amenth fh @ (eitain faloxy,1s9) tsi 124 tug (ey, 161 tas les 162 183 Is 7 164 WW 133 +36 140 tu| 1u3 14y We [10 3 No.of Classea N= 1 + 3-322 logsO 4 3:322 * 16985 +S 6435 6 6437 =¥ Gas Fnkeuval 30-5F SS- 80 80-105 105-130 0-155 1SS-1BO (80-205 oy 199 114 30, 104 43 03 18 164 62 ay WG 4 YO, 162 leg 164 (6s (64 las 18 126 42, 144 121 122 (ey 187 195 (97 203 204 108 qu 146 13 er (4g yq (st (SB (sé Is 161 t Alas trdeuval N= Rauge n = 204-30 = 4 F a = 2Wes7 =0. = 21498 a2e8 ree a MI 1 HAL | HH 111) HA HH HH HHI Gis) Make f%eq, détuibutian e ks él ey 40 aly SF $4 44 94 ou 2 163 34) 4s 40 y 34 38 86 Go ae 62 68 a2 | 88 65 a4 vo10-16% =1498 “ee Mass srteu vat - Sat Cans ar Fuequency uu—yu m q Sisal HLL 7 Chet HLH 8 34-84 an E ow HHI DIAGRAMS A diaguam & a vbual {oun 4 ieenia Wott fal date, Diacuiarts wef eee types af clevites Auch as haus, cincles, MAPS > f cawtoguams etc. Dhese devites can fake many atuactive four, atufetly speaking these ate not hic devices. Diagsams do ner aclel Any new, 46 the statistical (ads, but they “exhibit dhe seats mere clearly » An eudinaxy ma unolewtand pretu4es Gidiaguon ngewe Ary the fiquycs. Me we of Aicag Hans 0 AecomurG mes moxe popular in the puesent rime. meantnPOVANTAGES te fabs bh the mind + Attactive d tmpuesstve and 010ak raliig to Phe eye. af the Headoua he, 'uc Moyo Appec AP he of the Meadour Ahey fand them v-castly Yen a lay r n under ie iano ie ae SN TA OUR T AIS OEY L can LER OA may Agfud infaences f4om if, Ge Ais at fro Wowie Vistough numeuita. ' ay Go Thue dia 3 ay Ge oh tough Biya die ileite, Ose 3 Weesal ap licahiity & & 4 guide tn CCO,, atmart alt Sirsa life asa geod g bls , social instifutions make data simple & can be wemembertel 8 Yy GA they Hendey (empauon an CAasy © way. ” ~ They provide meue thjo. than the data tha table ‘a. platy an imp. x6le ty medeun sh ampatgn. The neuspapes Jowinal. etc. ave Filled with diaguann LIMITATIONS + Oia, can't be GNnAlvyed yeeithe. + fia. Ahouw only apphior, values > Yt CXPAAEA only Utd, Yack To duaw a table & O24 but (Cnstuction of a dé; B NOF 40 case YH Ss Au plement tO Fabuiasy HESeN AKON. by or ean albetie tive to FF. p Fea > Small diff. i (auege MEAS Uver) E, i z| z nl Can't be studied 5G, $Q =, q 89. > the diff. bud Gos WO AhoLun th a dia Hhan, TYPES 'G Che dinensienay out, the dength duittth of the b The + @ d > JISIIPF A, J © LILEEEIIIII Id ct The les ay po aus ts Qs Qe ad 4G paws p p. 4 net ead fon ibalclehed The town bay + a THICK Wide line. ONS OnAH BUmped tant dda) = ooo&x ; - ca suitable data {40m out mT ts |> Simple Bau Diog.sam F } ‘s © Matty Ft aan be duawn eithey on houtzonfal eu vertial 5 —] base. Baws en heuieental hase ave moye COMMEN. « ee vt baw dig, & Ye to duaw @ euy to undeush yeas and. tr bls & Xo., t's commonly used pe Auau . bana, its Cin thousands) & kee is the The om mu Ques) Ouaw a subtable hay diagsan ®ou veil © COM fo uniteus} be multfp 7 pH datinguish differen component fom one ¢ diffevent colo |Ahade may be 9 en Gua \ Repsiesont the {ou ; A Male (M) fom Ale tf) bau Aiags1 On rm) ‘ + Buofit of Gmpany A the values given in e é opoxtion to al psolute gute on percentages duaun ena louring data in the suite 00 ao. 2000 | p00 “otal (Dlel bau cuegHaAmM ¢ 1GHAMS have been uu DHEENE Ahselute yatues but compauien cs sed en a selatve bad. The vaueus mpenen& aue expuessed as peycentage Feu dividing the bas , these Kage: auc LMU, th tha case, the haus aue of ul ( gach segmat shou the / tne totel Repuesent by & Kage ba dinguam the 5 felleuting data on investment fou the yfualG second Yyeart Plans. fon OO VS Aguiulhoe 359 160] 768 Suugahon YGe apa) Ado Sndustouy 26! 12%) 90F FAN Apo 6SuU 3007] (Ue Borigl AewvICEs | B06 Muay) Quy Miellenous 40 Wes) 300 S7otet &t60. \oot ¥3q3 Wd?— Sy ven of Hp rena cus vecaneing Te also ‘be dliyfuedt QE 360 deguces Ct fh Ae peler aking the x Pie 3 ots, a » OOS Whe cen. oF the wal). Re Ocean) or a wie | PT, ae Han eee F 2 ae a An lauctég we , vhactic be ; eat «pi V2g81aM) fp Mepulesent q dato, i aa ti) facific | Atlanke | Indian Aatchien bec 60 = 6 76 1180-4 % 360= 1g° 4-81 IS@.q x 360 4,°mae usect when tuto mponents have aue ke nectangle OF iA pHor auica of the Bho Ne, T He be values. Yt may be of 2 type: m diviled ectangul claguam, the uti th of ¢ ition value G) feucen tage Yo such a J & kept accouding to the prop the vauidus component of the into rage & xectangles aue divided phenomena production e GD Auaw a &AWimensfonal arag the Yollourtus dato expenditine Bep.he) YEN Gj F000 300 clothin education Howe “ent MiBCOLENCUABotution >) */ | Cumulative nage B) '’. ' Get itn) Cen) Ps cc TT 50 cc = // 1 ¢ arr ¢ | § | 666 691 to | tas 8-6 é | tary 10D _ 363 # GRAPHICAL REPRESENTATION ©F DATA % deteguau WA Fust like ¢ bax Maguay One of the Most Crip, é Useful meth og of Pxesciting PEGE Continues Ate & known a ‘nisto pant WOKS in teat ithe magnitude dotted along the the veutical Ger 2 In this ma. nitude of Of dads inketvgl hexizontal axa a the Peequency on 5. £ach 044 hay (outer G Uppey values Thi, GVEA US THO ‘vextiens WOU Lar HepHeentifg jug CNY , Uppeu e ‘Gj jethed ‘ogehh a prices oh is } Whee axe Clawer, and the LEGA K Ptopohionare to they Peequency, Histog ENON an block Aaguan OY Sabai *Uangle 4 24 A<ingle ane HAN)VG alec hautANGICS, A gle, YE ate & also Aas Suequency polygon 2 A berquency ABtuibution ¢ fan be sepuesentedt br, h&toguam. 4 Alpe Method of smoothing i histequary Ww A fuequency polyGr e by 7 the micpotat of each ith the mid point of the Yep cath wectangle by Atuaight lines. Tha & done rho that puequenty Aa huket polygor cause She asen Ff hGto Guam, oe ust equal to Fhe easily be Youn ‘ vt fuequenty cuHuve n12G the puequency pelygc tthe Bhaup twins due BUC 1 ency pry B Armootn yusithen 10 OA A hOAGE wesults Bro a conta MUCUS frequency GY £MOOth 0 fn hae Cniluced a bi samp be A12e Of &O § of a new puoduck yh a @ t launch, The SOY eA Aas AA Ponded en Pc OppHopy't.ke ae A 4s! Vous . eee? ee Fe ge Bo Mes E Boe? 29 8 aa MO By gt z if 1 41 3 UO W 2 :- DS aeeP es, 4, 4 pe 82D ux ES ewes un 4° ge a Bip 4° banat 5 hfe ts the elin o pas 3 present the same Ato G Hau YEGUEALL polygon i Gl feqeny Cab e 4 4 raked suuvey wuthih co pe the allepya 46 we empant x 2 3 q a oe 1 “7 _ = : a — Oqiv ne then oH tu mecli Show the \ freq inkeuPHC nbect AMb0thiAG Hh OGIVE then cumul then the + C4 Lumulative jue median , Priel pe Shown along x-axih& Cumulative reque the V-art 4h dating an egive, the cu equency tA plotted at the Uppey limit PM the sucrose Pris latey 7 together fo get an egive cuuve of comteucting Ogives. —* ex thaw ogive More than the less than cumuls fueg, due upper Claws beundauics of “the Hcy phe points ave je ned by @ Smooth pecee Ohas the shape of an Aongated & the mete than moue than aue plottect against low of the HeApective ise, sink due ZoIr free hand awive 6 has elongated § stS80 compa Wo. of companies 43 qu One (an locate the median by ayouir A ogiv less than meye than ogive. 4uom the infeurcetion phof these agives, a peupenditulay line touches X-axs whith is the value of the mectian the Avg. Annual pu ofits for fess thaw {Mo ulatry aguiency Cur 24 +21=5OWBO-21= 334 64 108 (st @uys BIE 386 One tho 5 s3C SH 4wequq2 335 424 BER 33s +4 262 458 194 HS-150 86 113 Be 86 + 38 17 or