LEADER 01062nam a2200301 i 4500 001 991001092869707536 005 20020507183130.0 008 931127s1986 uk ||| | eng 020 $a0521317134 035 $ab10799916-39ule_inst 035 $aLE01306907$9ExL 040 $aDip.to Matematica$beng 082 0 $a512.4 084 $aAMS 16A08 (1985) 084 $aAMS 16A33 (1985) 084 $aQA521.4.J37 100 1 $aJategaonkar, A. V.$057661 245 10$aLocalization in Noetherian rings /$cA. V. Jategaonkar 260 $aCambridge :$bCambridge University Press,$c1986 300 $axi, 324 p. ;$c23 cm 490 0 $aLondon Mathematical Society lecture note series,$x0076-0552 ;$v98 650 0$aNoetherian rings 907 $a.b10799916$b23-02-17$c28-06-02 912 $a991001092869707536 945 $aLE013 16A JAT11 (1986)$g1$i2013000073743$lle013$o-$pE0.00$q-$rl$s- $t0$u1$v0$w1$x0$y.i10903756$z28-06-02 996 $aLocalization in Noetherian rings$983011 997 $aUNISALENTO 998 $ale013$b01-01-93$cm$da $e-$feng$guk $h0$i1 LEADER 01709nam 2200553 a 450 001 9910779960703321 005 20230607212951.0 010 $a0-313-00080-8 035 $a(CKB)111056485487342 035 $a(OCoLC)50016754 035 $a(CaPaEBR)ebrary5004454 035 $a(SSID)ssj0000199604 035 $a(PQKBManifestationID)11204121 035 $a(PQKBTitleCode)TC0000199604 035 $a(PQKBWorkID)10196341 035 $a(PQKB)10514998 035 $a(MiAaPQ)EBC3000159 035 $a(Au-PeEL)EBL3000159 035 $a(CaPaEBR)ebr5004454 035 $a(OCoLC)50175373 035 $a(EXLCZ)99111056485487342 100 $a20000504d2001 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMcCormick of Rutgers$b[electronic resource] $escholar, teacher, public historian /$fMichael J. Birkner 210 $aWestport, Conn. $cGreenwood Press$d2001 215 $a1 online resource (263 p.) 225 1 $aStudies in historiography,$x1046-526X ;$vno. 6 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-313-30356-8 320 $aIncludes bibliographical references (p. [205]-218) and index. 410 0$aStudies in historiography ;$vno. 6. 606 $aHistorians$zUnited States$vBiography 606 $aEducators$zUnited States$vBiography 615 0$aHistorians 615 0$aEducators 676 $a973/.07/202 676 $aB 700 $aBirkner$b Michael J.$f1950-$0872280 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910779960703321 996 $aMcCormick of Rutgers$93805063 997 $aUNINA LEADER 05731nam 2200829 a 450 001 9910815808903321 005 20200520144314.0 010 $a9781118735572 010 $a1118735579 010 $a9781118691861 010 $a1118691865 010 $a9781118735640 010 $a1118735641 035 $a(CKB)2550000001102599 035 $a(EBL)1315864 035 $a(SSID)ssj0000917900 035 $a(PQKBManifestationID)11483966 035 $a(PQKBTitleCode)TC0000917900 035 $a(PQKBWorkID)10893234 035 $a(PQKB)10562601 035 $a(DLC) 2013017551 035 $a(Au-PeEL)EBL1315864 035 $a(CaPaEBR)ebr10734639 035 $a(CaONFJC)MIL505082 035 $a(CaSebORM)9781118735572 035 $a(MiAaPQ)EBC1315864 035 $a(OCoLC)841518549 035 $a(OCoLC)869214322 035 $a(OCoLC)ocn869214322 035 $a(Perlego)999757 035 $a(EXLCZ)992550000001102599 100 $a20130805d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDemand-driven forecasting $ea structured approach to forecasting /$fCharles W. Chase, Jr 205 $a2nd ed. 210 $aHoboken, N.J. $cWiley$dc2013 215 $a1 online resource (386 p.) 225 1 $aWiley & SAS Business Series 300 $aIncludes index. 311 08$a9781118669396 311 08$a1118669398 311 08$a9781299738317 311 08$a1299738311 320 $aIncludes bibliographical references and index. 327 $aDemand-Driven Forecasting; Contents; Foreword; Preface; Acknowledgments; About the Author; Chapter 1 Demystifying Forecasting: Myths versus Reality; DATA COLLECTION, STORAGE, AND PROCESSING REALITY; ART-OF-FORECASTING MYTH; END-CAP DISPLAY DILEMMA; REALITY OF JUDGMENTAL OVERRIDES; OVEN CLEANER CONNECTION; MORE IS NOT NECESSARILY BETTER; REALITY OF UNCONSTRAINED FORECASTS, CONSTRAINED FORECASTS, AND PLANS; NORTHEAST REGIONAL SALES COMPOSITE FORECAST; HOLD-AND-ROLL MYTH; THE PLAN THAT WAS NOT GOOD ENOUGH; PACKAGE TO ORDER VERSUS MAKE TO ORDER; "DO YOU WANT FRIES WITH THAT?"; SUMMARY; NOTES 327 $aChapter 2 What Is Demand-Driven Forecasting?TRANSITIONING FROM TRADITIONAL DEMAND FORECASTING; WHAT'S WRONG WITH THE DEMAND-GENERATION PICTURE?; FUNDAMENTAL FLAW WITH TRADITIONAL DEMAND GENERATION; RELYING SOLELY ON A SUPPLY-DRIVEN STRATEGY IS NOT THE SOLUTION; WHAT IS DEMAND-DRIVEN FORECASTING?; WHAT IS DEMAND SENSING AND SHAPING?; CHANGING THE DEMAND MANAGEMENT PROCESS IS ESSENTIAL; COMMUNICATION IS KEY; MEASURING DEMAND MANAGEMENT SUCCESS; BENEFITS OF A DEMAND-DRIVEN FORECASTING PROCESS; KEY STEPS TO IMPROVE THE DEMAND MANAGEMENT PROCESS 327 $aWHY HAVEN'T COMPANIES EMBRACED THE CONCEPT OF DEMAND-DRIVEN?Key Points; SUMMARY; NOTES; Chapter 3 Overview of Forecasting Methods; UNDERLYING METHODOLOGY; DIFFERENT CATEGORIES OF METHODS; HOW PREDICTABLE IS THE FUTURE?; SOME CAUSES OF FORECAST ERROR; SEGMENTING YOUR PRODUCTS TO CHOOSE THE APPROPRIATE FORECASTING METHOD; New Products Quadrant; Niche Brands Quadrant; Growth Brands Quadrant; Harvest Brands Quadrant; SUMMARY; NOTE; Chapter 4 Measuring Forecast Performance; "WE OVERACHIEVED OUR FORECAST, SO LET'S PARTY!"; PURPOSES FOR MEASURING FORECASTING PERFORMANCE 327 $aSTANDARD STATISTICAL ERROR TERMSSPECIFIC MEASURES OF FORECAST ERROR; OUT-OF-SAMPLE MEASUREMENT; FORECAST VALUE ADDED; SUMMARY; NOTES; Chapter 5 Quantitative Forecasting Methods Using Time Series Data; UNDERSTANDING THE MODEL-FITTING PROCESS; INTRODUCTION TO QUANTITATIVE TIME SERIES METHODS; QUANTITATIVE TIME SERIES METHODS; MOVING AVERAGING; EXPONENTIAL SMOOTHING; SINGLE EXPONENTIAL SMOOTHING; HOLT'S TWO-PARAMETER METHOD; HOLT'S-WINTERS' METHOD; WINTERS' ADDITIVE SEASONALITY; Multiplicative versus Additive Seasonality; SUMMARY; NOTES; Chapter 6 Regression Analysis; REGRESSION METHODS 327 $aSIMPLE REGRESSIONCORRELATION COEFFICIENT; COEFFICIENT OF DETERMINATION; MULTIPLE REGRESSION; DATA VISUALIZATION USING SCATTER PLOTS AND LINE GRAPHS; CORRELATION MATRIX; MULTICOLLINEARITY; ANALYSIS OF VARIANCE; F-TEST; ADJUSTED R2; PARAMETER COEFFICIENTS; t-TEST; P-VALUES; VARIANCE INFLATION FACTOR; DURBIN-WATSON STATISTIC; INTERVENTION VARIABLES (OR DUMMY VARIABLES); REGRESSION MODEL RESULTS; KEY ACTIVITIES IN BUILDING A MULTIPLE REGRESSION MODEL; CAUTIONS ABOUT REGRESSION MODELS; SUMMARY; NOTES; Chapter 7 ARIMA Models; PHASE 1: IDENTIFYING THE TENTATIVE MODEL; Stationarity 327 $aAnalysis of the Autocorrelation Plots 330 $aAn updated new edition of the comprehensive guide to better business forecasting Many companies still look at quantitative forecasting methods with suspicion, but a new awareness is emerging across many industries as more businesses and professionals recognize the value of integrating demand data (point-of-sale and syndicated scanner data) into the forecasting process. 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