Reform: School structure • Concerns about the quality of education • By 1970 greater equity in distribution of school resources • Concern about gap between US and other countries: • National Commission on Education Excellence concerned that the US falling behind due to teacher quality, training, not enough homework, length of school day • Need for competition: monopoly of local schools results in very little improvement in quality
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Academic Excellence -- Accreditation School School of of Education Education School School of of Pharmacy Pharmacy Accreditation Accreditation Council Council for for Pharmacy Pharmacy Education Education School of Dental Medicine National National Council Council for for the the Accreditation Accreditation of of Teacher Teacher Education (NCATE) Education (NCATE) National National Association Association of of School School Psychologists Psychologists American American Speech-Language-and-Hearing Speech-Language-and-Hearing Assn. Assn. American American Dental Dental Association Association School School of of Engineering Engineering (ABET) (ABET) Accreditation Accreditation Board Board for for Engineering Engineering Technology Technology American American Council Council for for Construction Construction Education Education 44 44 Baccalaureate Baccalaureate Degree Degree Options Options The The Graduate Graduate School School 45 45 Masters Masters Degrees Degrees Doctoral/Professional Doctoral/Professional Degrees Degrees Pharmacy Pharmacy and and Dental Dental Medicine Medicine Nursing and Education Engineering Nursing and Education Engineering School School of of Nursing Nursing CCNE CCNE Commission Commission on on Collegiate Collegiate Nursing Nursing Education Education College College of ofArts Arts and and Sciences Sciences National NationalAssociation Association of of Schools Schools of of Music, Music, Voice, Voice, and and Piano; Piano; National NationalAssociation Association of of Schools Schools of ofArt Art and and Design; Design; School of Business (AACSB) –Association Association to to American American Chemical Chemical Society; Society; Advance Collegiate Schools of Business Advance Collegiate Schools of Business American AmericanArt Art Therapy; Therapy; Association AssociationAccrediting Accrediting Council Council in in Journalism Journalism and and Mass Mass Communication; Communication; Council Council on on Social Social Work Work Education Education National National Association Association of of Schools Schools of of Public PublicAffairs Affairs and andAdministration; Administration; National NationalAssociation Association of of Schools Schools of of Theatre Theatre
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C1 01TBM Three blind mice 3 SO1 35SSS Sing a song of sixpence .18 .14 02TLP This little pig went to market DO1 03DDD Diddle diddle dumpling my son John 07OMH Old Mother Hubbard 04LMM Little Miss Muffet 30HDD Hey diddle diddle 06SPP See a pin and pick it up 08JSC Jack Sprat could eat no fat C2 ffa=39LCS 09HBD Hush baby. Daddy is near 10JAJ Jack and Jill went up the hill 05HDS Humpty Dumpty C11 12OWF There came an old woman from France 11OMM One misty moisty morning 01TBM Three blind mice 13RRS A robin and a robins son 15PCD Great A. little a 02TLP This little pig went to market 14ASO If all the seas were one sea 21LAU The Lion and the Unicorn 03DDD Diddle diddle dumpling my son John 16PPG Flour of England 22HLH I had a little husband 04LMM Little Miss Muffet 17FEC Here sits the Lord Mayor 28BBB Baa baa black sheep 06SPP See a pin and pick it up 18HTP I had two pigeons bright and gay 36LTT Little Tommy Tittlemouse 10JAJ Jack and Jill went up the hill 23MTB How many miles is it to Babylon 37MBB Here we go round mulberry bush 13RRS A robin and a robins son 25WOW There was an old woman 38YLS If I had as much money as I could tell 14ASO If all the seas were one sea 26SBS Sleep baby sleep 39LCS A little cock sparrow 16PPG Flour of England 27CBC Cry baby cry 41OKC Old King Cole 17FEC Here sits the Lord Mayor C21 ffa=21LAU 29LFW When little Fred went to bed 42BBC Bat bat, come under my hat 18HTP I had two pigeons bright and gay 32JGF Jack, come give me your fiddle 48OTB One two, buckle my shoe 05HDS Humpty Dumpty 23MTB How many miles is it to Babylon 33BFP Buttons, a farthing a pair 50LJH Little Jack Horner 11OMM One misty moisty morning 25WOW There was an old woman 43HHD Hark hark, the dogs do bark .26 15PCD Great A. little a 32JGF Jack, come give me your fiddle 44HLH The hart he loves the high wood DO4 .46 21LAU The Lion and the Unicorn 33BFP Buttons, a farthing a pair 45BBB Bye baby bunting .19 22HLH I had a little husband 28BBB Baa baa black sheep 43HHD Hark hark, the dogs do bark 46TTP Tom Tom the pipers son 36LTT Little Tommy Tittlemouse 44HLH The hart he loves the high wood 47CCM Cocks crow in the morn SO8 41OKC Old King Cole 37MBB Here we go round mulberry 46TTP Tom Tom the pipers son 49WLG There was a little girl 50LJH Little Jack Horner 38YLS If I had as much money SO9 47CCM Cocks crow in the morn .28 2.2 SO2 08JSC Jack Sprat 42BBC Bat bat, come under my hat 49WLG There was a little girl 39LCS A little cock sparrow C12 48OTB One two, buckle my shoe .42 C111 09HBD Hush baby. Daddy is near C211 ffa=37 03DDD Diddle diddle dumpling my son John .41 12OWF There came old woman France 06SPP See a pin and pick it up 26SBS Sleep baby sleep 05HDS Humpty Dumpty 2 10JAJ Jack and Jill went up the hill 27CBC Cry baby cry 11OMM One misty moisty morning 13RRS A robin and a robins son .31 1.3 29LFW When little Fred went to bed 15PCD Great A. little a 14ASO If all the seas were one sea 45BBB Bye baby bunting 22HLH I had a little husband 16PPG Flour of England SO15 36LTT Little Tommy Tittlemouse 1.53 18HTP I had two pigeons bright and gay 37MBB Here we go round mulberry .42 29LFW When little Fred went bed 23MTB How many miles is it to Babylon SO3 38YLS If I had as much money 25WOW There was an old woman 48OTB One two, buckle my shoe C121 ffa=29 46TTP Tom Tom pipers DO3 32JGF Jack, come give me your fiddle SO10 33BFP Buttons, a farthing a pair 09HBD Hush baby. Daddy is near 01TBM Three blind mice SO11 42BBC Bat bat, come undert 43HHD Hark hark, the dogs do bark 26SBS Sleep baby sleep DO2 17FEC Here sits the Lord Mayor 21LAU The Lion and the Unicorn 44HLH The hart he loves the high wood 27CBC Cry baby cry .38 47CCM Cocks crow in the morn 45BBB Bye baby bunting 02TLP This little pig C2111 ffa=15 SO14 49WLG There was a little girl .1.6 04LMM Little Miss Muffet .36 05HDS Humpty Dumpty C1111 12OWF The came ol woman France 15PCD Great A. little a 1.8 03DDD Diddle diddle dumpling my son John 22HLH I had a little husband 06SPP See a pin and pick it up 36LTT Little Tommy Tittlemouse SO4 13RRS A robin and a robins son 38YLS If I had as much money 16PPG Flour of England 10JAJ Jack and Jill went up the hill 48OTB One two, buckle my shoe 18HTP I had two pigeons bright and gay TO1 23MTB How many miles is it to Babylon SO12 32JGF Jack, come give me your fiddle 14ASO If all the seas 33BFP Buttons, a farthing a pair 11OMM One misty moisty SO13 25WOW There was an old woman 43HHD Hark hark, the dogs do bark 44HLH The hart he loves 37MBB Here we go rnd mulberry 47CCM Cocks crow in the morn 49WLG There was a little girl C2111 seems to be lullabys? no gaps C2111 seems to focus on .3 1.3 SO5 C11111 extremes? (big and small) 03DDD Diddle diddle dumpling my son John 13RRS A robin and TO2 06SPP See a pin and pick it up 16PPG Flour of England 23MTB How many miles to Babylon 18HTP I had two pigeons bright and gay 33BFP Buttons, a farthing a pair Notes: 32JGF Jack, come give me your fiddle 43HHD Hark hark, the dogs do bark 47CCM Cocks crow in the morn In text mining, just about any document is eventually going to be an 49WLG There was a little girl C111111 SO6 outlier due to the fact that we are projecting high dimension (44 here) onto 06SPP See a pin and pick it up 16PPG Flour of England SO7 03DDD Diddle diddle dumpling dimension=1. Thus the ffa will almost always be an outlier in LAvgffa. 18HTP I had two pigeons bright and gay 47CCM Cocks crow in the morn 32JGF Jack, come give me your fiddle MG44d60w A-FFA dendogram
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Understanding the Equity Summary Score Methodology 2 3. Calculate – The normalized analysts’ recommendations and the accuracy weightings are combined to create a single score. For the largest 1,500 stocks by market capitalization, these scores are then forcibly ranked against all the other scores to create a standardized Equity Summary Score on a scale of 0.1 to 10.0 for the 1,500 stocks. This means that there will be a uniform distribution of scores provided by the model thereby assisting investors in evaluating the largest stocks (in terms of Understanding the Equity Summary Score Methodology Provided By 2 capitalization), which typically make up the majority of individual investors’ portfolios. Finally, smaller cap stocks are then slotted into this distribution without a force ranking, and may not exhibit the same balanced distribution. The Equity Summary Score and associated sentiment ratings by StarMine are: 0.1 to 1.0 ‐ very bearish 1.1 to 3.0 ‐ bearish 3.1 to 7.0 ‐ neutral 7.1 to 9.0 ‐ bullish 9.1 to 10.0 ‐ very bullish Other Important Model Factors:  An Equity Summary Score is only provided for stocks with ratings from four or more independent research providers.  New research providers are ramped in slowly by StarMine to avoid rapid fluctuations in Equity Summary Scores. Indep. research providers that are removed from Fidelity.com will similarly be ramped out slowly to avoid rapid fluctuations. Notes on Using the Equity Summary Score: The Equity Summary Score and sentiment ratings are ratings of relative, not absolute forecasted performance. The StarMine model anticipates that the highest rated stocks, those labeled “Very Bullish” as a group, may outperform lower rated groups of stocks. In a rising market, most stocks may experience price increases, and in a declining market, most stocks may experience price declines  Proper diversification within a portfolio is critical to the effective use of the Equity Summary Score. Individual company performance is subject to a broad range of factors that cannot be adequately captured in any rating system.  Larger differences in Equity Summary Scores may lead to differences in future performance. The sentiment rating labels should only be used for quick categorization. An 8.9 Bullish is closer to a 9.1 Very Bullish than a 7.1 Bullish.  For a customer holding a stock with a lower Equity Summary Score, there are many important considerations (for example, taxes) that may be much more important than the Score.  The Equity Summary Score by StarMine does not predict future performance of underlying stocks. The Equity Summary Score model has only been in production since August 2009 and therefore no assumptions should be made about how the model will perform in differing market conditions. Understanding the Equity Summary Score Methodology Provided By 3 How has the Equity Summary Score performed? Transparency is a core value at Fidelity, and that is why StarMine provides Fidelity with a view of the historical aggregate performance of the Equity Summary Score across all covered stocks each month. You can use this to obtain insight into the performance and composition of the Equity Summary Score. In addition, the individual stock price performance during each period of the Equity Summary
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VOMmean w F=(DPP-MN)/4 Concrete4150(C, W, FA, Ag) 0 1 1 1 5 1 6 1 7 1 8 4 med=14 9 1 10 1 11 2 12 1 13 5 14 1 15 3 med=18 16 3 17 4 18 1 19 3 20 9 21 4 22 3 23 7 24 2 med=40 25 4 26 8 27 7 28 7 med=56 29 10 30 3 31 1 32 3 33 6 med=61 34 4 35 5 37 2 38 2 40 1 42 3 43 1 44 1 45 1 46 4 ______ CLUS 4 gap=7 49 1 56 1 [52,74) 0L 7M 0H CLUS_3 58 1 61 1 65 1 66 1 69 1 ______ gap=6 71 1 77 1 [74,90) 0L 4M 0H CLUS_2 80 1 83 1 ________ gap=14 86 1[0.90) 43L 46 M 55H 100 1 [90,113) 0L 6M 0H CLUS_1 103 1 105 1 108 2 112 1 _____________At this level, FinalClus1={17M} 0 errors C1 C2 C3 C4 med=10 med=9 med=17 med=21 med=23 med=34 med=33 med=57 med=62 med=71 med=71 med=86 CLUS 4 (F=(DPP-MN)/2, Fgap2 _______ 0L 0M 3H CLUS 4.4.1 gap=7 0 3 =0 0L 0M 4H CLUS 4.4.2 gap=2 7 4 =7 9 1 [8,14] 1L 5M 22H CLUS 4.4.3 1L+5M err H 10 12 11 8 gap=3 12 7 ______ 0L 0M 4H CLUS 4.3.1 gap=3 15 4 =15 18 10 0L 0M 10H CLUS 4.3.2 gap=3 21 3 =18 22 7 ______ 23 2 [20,24) 0L 10M 2H CLUS 4.7.2 gap=2 25 2 [24,30) 10L 0M 0H CLUS_4.7.1 26 3 27 1 28 2 gap=2 29 1 31 3 CLUS 4.2.1 gap=2 32 1 [30,33] 0L 4M 0H Avg=32.3 34 2 0L 2M 0H CLUS 4.2.2 gap=6 40 4 =34 ______ 0L 4M 0H CLUS_4.2.3 gap=7 47 3 =40 52 1 0L 3M 0H CLUS_4.2.4 gap=5 53 3 =47 54 3 55 4 56 2 57 3 ______ gap=2 58 1 [50,59) 12L 1M 4H CLUS 4.8.1 L60 2 8L 0M 0H CLUS_4.8.2 61 2 [59,63) gap=2 62 4 ______ =64 2L 0M 2H CLUS 4.6.1 gap=3 64 4 [66,70) 10L 0M 0H CLUS 4.6.2 67 2 gap=3 68 1 71 7 ______ gap=7 72 3 [70,79) 10L 0M 0H CLUS_4.5 79 5 5L 0M 0H CLUS_4.1.1 gap=6 85 1 =79 87 2 [74,90) 2L 0M 1H CLUS_4.1 1 Merr in L Median=0 Avg=0 Median=7 Avg=7 Median=11 Avg=10.7 Median=15 Avg=15 Median=18 Avg=18 Median=22 Avg=22 2H errs in L Median=26 Avg=26 Median=31 Median=34 Avg=34 Median=40 Avg=40 Median=47 Avt=47 Accuracy=90% Median=55 Avg=55 1M+4H errs in Median=61.5 Avg=61.3 Median=64 Avg=64 2 H errs in L Median=67 Avg=67.3 Median=71 Avg=71.7 Median=79 Avg=79 Median=87 Avg=86.3 Suppose we know (or want) 3 clusters, Low, Medium and High Strength. Then we find Suppose we know that we want 3 strength clusters, Low, Medium and High. We can use an antichain that gives us exactly 3 subclusters two ways, one show in brown and the other in purple Which would we choose? The brown seems to give slightly more uniform subcluster sizes. Brown error count: Low (bottom) 11, Medium (middle) 0, High (top) 26, so 96/133=72% accurate. The Purple error count: Low 2, Medium 22, High 35, so 74/133=56% accurate. What about agglomerating using single link agglomeration (minimum pairwise distance? Agglomerate (build dendogram) by iteratively gluing together clusters with min Median separation. Should I have normalize the rounds? Should I have used the same Fdivisor and made sure the range of values was the same in 2nd round as it was in the 1st round (on CLUS 4)? Can I normalize after the fact, I by multiplying 1st round values by 100/88=1.76? Agglomerate the 1st round clusters and then independently agglomerate 2nd round clusters? CONCRETE
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