LEADER 03632nam 2200601 450 001 9910796964903321 005 20200520144314.0 010 $a1-000-98142-8 010 $a1-000-97616-5 010 $a1-003-44756-2 010 $a1-62036-578-2 035 $a(CKB)4100000005116687 035 $a(Au-PeEL)EBL5447542 035 $a(CaPaEBR)ebr11591366 035 $a(OCoLC)1044792455 035 $a(MiAaPQ)EBC5447542 035 $a(EXLCZ)994100000005116687 100 $a20180724d2018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 04$aThe analytics revolution in higher education $ebig data, organizational learning, and student success /$fedited by Jonathan S. Gagliardi, Amelia Parnell, and Julia Carpenter-Hubin ; foreword by Randy L. Swing 205 $aFirst edition. 210 1$aSterling, Virginia :$cStylus Publishing, LLC,$d[2018] 210 4$d©2018 215 $a1 online resource (240 pages) 311 $a1-62036-576-6 320 $aIncludes bibliographical references and index. 327 $aThe analytics revolution in higher education / Jonathan S. Gagliardi -- Higher education decision support : building capacity, adding value / Daniel Cohen-Vogel -- Cultural and organizational structures and functions of institutional research / Julia Carpenter-Hubin and Jason Sullivan -- Data analytics for student success : elaborate profusion of institutional research into student affairs / Amelia Parnell -- The IT-IR nexus : three essentials for driving institutional change through data and analytics / Timothy Chester -- Pursuit of analytics and the challenge of organizational change management for institutional research / Angela Y. Baldasare -- Enrollment to career : dynamics of education and the workforce / Stephanie Bond-Huie -- State system research : increasing products, data, roles, and stakeholders / Angela Bell -- Moving from data to action : changing institutional culture and behavior / Desdemona Cardoza and Jeff Gold -- New and emerging frameworks for decision analytics : a data governance perspective / Ronald L. Huesman and Steve A. Gillard -- Examining how the analtics revolution matters to higher education policy makers : data analytics, systemness, and enabling student success / Jason E. Lane -- Evolving from reflective to predictive : Montgomery Community College and analytics / Celeste Schwartz, Kent Phillipe, David Kowalski, and Angela Polec -- Unpacking the messiness of harnessing the analytics revolution / Jonathan S. Gagliardi. 606 $aEducation, Higher$xResearch$zUnited States$xData processing 606 $aEducation, Higher$xResearch$zUnited States$xStatistical methods 606 $aEducational statistics$zUnited States 606 $aUniversities and colleges$zUnited States$xAdministration$xData processing 615 0$aEducation, Higher$xResearch$xData processing. 615 0$aEducation, Higher$xResearch$xStatistical methods. 615 0$aEducational statistics 615 0$aUniversities and colleges$xAdministration$xData processing. 676 $a378.007 702 $aSwing$b Randy L. 702 $aParnell$b Amelia R. 702 $aGagliardi$b Jonathan S. 702 $aCarpenter-Hubin$b Julia 712 02$aAssociation for International Research, 712 02$aAmerican Council on Education, 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910796964903321 996 $aThe analytics revolution in higher education$93798931 997 $aUNINA LEADER 04148nam 2200565 450 001 9910822513503321 005 20230803195633.0 010 $a1-61547-217-7 035 $a(CKB)2670000000547041 035 $a(EBL)1652816 035 $a(MiAaPQ)EBC1652816 035 $a(Au-PeEL)EBL1652816 035 $a(CaPaEBR)ebr10850209 035 $a(CaONFJC)MIL582361 035 $a(OCoLC)874322302 035 $a(EXLCZ)992670000000547041 100 $a20140404h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aExcel 2013 for scientists /$fDr. Gerard M. Verschuuren ; Shannon Mattiza, cover design 205 $aRevised & expanded third edition. 210 1$aUniontown, Ohio :$cHoly Macro! Books,$d2014. 210 4$d©2014 215 $a1 online resource (321 p.) 225 1 $aExcel for Professionals series 300 $aIncludes index. 311 $a1-61547-025-5 327 $aCover; Title page; Copyright page; Contents; About the Author; Introduction; Part 1: General Spreadsheet Techniques; Chapter 2: The Fill Handle; Chapter 3: Relative vs. Absolute Cell References; Chapter 4: Range Names; Chapter 5: Nested Functions; Part 1 Exercises; Part 2: Data Analysis; Chapter 7: Subtotals; Chapter 8: Summary Functions; Chapter 9: Unique Lists; Chapter 10: Data Validation; Chapter 11: Conditional Formatting; Chapter 12: Filtering Tools; Chapter 13: Lookups; Chapter 14: Working with Trends; Chapter 15: Fixing Numbers; Chapter 16: Copying Formulas 327 $aChapter 17: Multi-cell ArraysChapter 18: Single-cell Arrays; Chapter 19: Date Manipulation; Chapter 20: Time Manipulation; Part 2 Exercises; Part 3: Plotting Data; Chapter 22: A Chart's or Graph's Data Source; Chapter 23: Combining Chart Types; Chapter 24: Changing Graph Locations; Chapter 25: Templates and Defaults; Chapter 26: Axis Scales; Chapter 27: More Axes; Chapter 28: Error Bars; Chapter 29: More Bars; Chapter 30: Line Markers; Chapter 31: Interpolation; Chapter 32: Graph Formulas; Part 3 Exercises; Part 4: Regression and Curve Fitting; Chapter 34: Nonlinear Regression 327 $aChapter 35: Curve FittingChapter 36: Sigmoid Curves; Chapter 37: Predictability; Chapter 38: Correlation; Chapter 39: Multiple Regression: Linear Estimates; Chapter 40: Reiterations and Matrixes; Chapter 41: Solving Equations; Chapter 42: What-If Controls; Chapter 43: Syntax of Functions; Chapter 44: Worksheet Functions; Part 4 Exercises; Part 5: Statistical Analysis; Chapter 46: Types of Distributions; Chapter 47: Simulating Distributions; Chapter 48: Sampling Techniques; Chapter 49: Test Conditions and Outliers; Chapter 50: Estimating Means; Chapter 51: Estimating Proportions 327 $aChapter 52: Significant MeansChapter 53: Significant Proportions; Chapter 54: Significant Frequencies; Chapter 55: More on Chi-Squared Testing and Box-Cox Power; Chapter 56: Analysis of Variance; Part 5 Exercises; Index 330 $aWith examples from the world of science, this reference teaches scientists how to create graphs, analyze statistics and regressions, and plot and organize scientific data. Scientists can learn the tips and techniques of Excel-and tailor them specifically to their experiments, designs, and research. They will learn when to use NORMDIST vs NORMSDist and CONFIDENCE vs Z, how to keep data-validation lists on a hidden worksheet, use pivot tables to chart frequency distribution, generate random samples with various characteristics, and much more. Ideal for students and professionals alike, this hand 410 0$aExcel for Professionals series 606 $aComputer software$xDevelopment 606 $aElectronic spreadsheets 615 0$aComputer software$xDevelopment. 615 0$aElectronic spreadsheets. 676 $a005.1 700 $aVerschuuren$b G. M. N$g(Geert M. 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