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1

Tritton, Kelvin. Variable data printing. Leatherhead: PIRA, 2003.

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2

Components, Philips. Variable capacitors: Data handbook. London: Philips Components, 1993.

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3

Christine, Bachrach, National Survey of Family Growth (U.S.) y National Center for Health Statistics (U.S.), eds. National survey of family growth, cycle III: Sample design, weighting, and variance estimation : this report describes the procedures used to select the sample. Hyattsville, Md: U.S. Dept. of Health and Human Services, Public Health Service, National Center for Health Statistics, 1985.

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4

Salomon, David. Variable-length Codes for Data Compression. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-959-0.

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5

Albert, Boggess y Stewart James 1941-, eds. Single variable CalcLabs with Derive: For Stewart's fourth edition, Calculus, Single variable calculus, Calculus--early transcendentals, Single variable calculus--early transcendentals. Pacific Grove, CA: Brooks/Cole Pub., 1999.

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6

Thomas' calculus: Single variable. Harlow: Addison-Wesley, 2009.

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7

Center, Goddard Space Flight, ed. Nickel-cadmium cell design variable program data analysis. Greenbelt, MD: National Aeronautics and Space Administration, Goddard Space Flight Center, 1985.

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8

Hirsch y Coxford. Predicting from Data: An alternative unit for representing and analyzing two-variable data. New York, NY: McGraw-Hill/Glencoe, 1995.

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9

Bizon, Thomas P. Real-time transmission of digital video using variable-lengthbcoding. [Washington, DC: National Aeronautics and Space Administration, 1993.

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10

Bizon, Thomas P. Real-time transmission of digital video using variable-lengthbcoding. [Washington, DC: National Aeronautics and Space Administration, 1993.

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11

Bizon, Thomas P. Real-time transmission of digital video using variable-lengthbcoding. [Washington, DC: National Aeronautics and Space Administration, 1993.

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12

1941-, Stewart James, ed. Single variable CalcLabs with the TI-89/82: For Stewart's fourth edition, Calculus, Single variable calculus, Calculus--early transcendentals, Single variable calculus--early transcendentals. Pacific Grove, CA: Brooks/Cole Pub., 1999.

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13

Lindgren, Sue A. Justice variable passthrough data, 1988: Anti-drug abuse formula grants. [Washington, D.C.]: U.S. Dept. of Justice, Office of Justice Programs, Bureau of Justice Statistics, 1990.

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14

Hollis, Selwyn L. Multivariable CalcLabs with Mathematica: For Stewart's fourth edition, Calculus, Single variable calculus, Calculus--early transcendentals, Single variable calculus--early transcendentals. Pacific Grove, CA: Brooks/Cole Pub., 1999.

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15

Bossert, Walter. On Some Variable Population Size Issues in Welfare Economics. Frankfurt am Main: athenaum Verlag, 1989.

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16

Bossert, Walter. On some variable population size issues in welfare economics. Frankfurt am Main: Athenäum, 1989.

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17

Barrow, N. J. Reactions with variable-charge soils. Dordrecht: M. Nijhoff, 1987.

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18

1941-, Stewart James, ed. CalcLabs with Mathematica: For Stewart's Single variable calculus, 5th ed. Pacific Grove, CA: Thomson Brooks/Cole, 2003.

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19

McCarter, Joan H. Discovering calculus with graphing calculators: A manual of projects to enhance the understanding of single variable calculus. New York: J. Wiley, 1992.

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20

1942-, Papliński A. P. y Gibbard M. J. 1937-, eds. Numerical operations with polynomial matrices: Application to multi-variable dynamic compensator design. Berlin: Springer-Verlag, 1992.

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21

Provder, Theodore, ed. Detection and Data Analysis in Size Exclusion Chromatography. Washington, DC: American Chemical Society, 1987. http://dx.doi.org/10.1021/bk-1987-0352.

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22

1939-, Provder Theodore, American Chemical Society. Division of Polymeric Materials: Science and Engineering. y American Chemical Society Meeting, eds. Detection and data analysis in size exclusion chromatography. Washington, DC: The Society, 1987.

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23

Gregory, Alan. Detecting information from directors' trades: Signal definition and variable size effects. London: London School of Economics, Financial Markets Group, 1996.

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24

Rosbury, Keith D. Generalized particle size distribution for use in preparing size specific particulate emission inventories. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, 1986.

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25

S, Eagleson Peter y United States. National Aeronautics and Space Administration., eds. Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data. Cambridge, Mass: Ralph M. Parsons Laboratory, Hydrology and Water Resource Systems, Dept. of Civil Engineering, School of Engineering, Massachusetts Institute of Technology, 1990.

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26

Statistical Methods for Integrated Cancer Genomic Data Using a Joint Latent Variable Model. [New York, N.Y.?]: [publisher not identified], 2018.

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27

S, Eagleson Peter y United States. National Aeronautics and Space Administration., eds. Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data. Cambridge, Mass: Ralph M. Parsons Laboratory, Hydrology and Water Resource Systems, Dept. of Civil Engineering, School of Engineering, Massachusetts Institute of Technology, 1990.

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28

Webb, Robert S. A global data set of soil particle size properties. Washington, D. C: NASA, 1991.

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29

P, Lodge James, Chan T. L y American Industrial Hygiene Association, eds. Cascade impactor: Sampling & data analysis. Akron, OH: American Industrial Hygiene Association, 1986.

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30

To know and to love god. The Very last designer's guide to digital, on-demand, and variable-data color printing. Alexandria: Digital Printing Council, 2000.

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31

Han, Zhuangzhuang. Three New Studies on Model-data Fit for Latent Variable Models in Educational Measurement. [New York, N.Y.?]: [publisher not identified], 2019.

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32

Hogan, Seamus. Some explorations, using Canadian data, of the S-variable in Akerlof, Dickens and Perry (1996). Ottawa: Bank of Canada, 2000.

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33

John, Jones. Farm real estate: Historical series data, 1950-92. Washington, DC: U.S. Dept. of Agriculture, Economic Research Service, 1993.

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34

United States. Dept. of Agriculture. Economic Research Service., ed. Farm real estate: Historical series data, 1950-85. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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35

Poppe, Lawrence J. APSAS, an automated particle size analysis system. [Reston, Va.]: Dept. of the Interior, U.S. Geological Survey, 1985.

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36

Massachusetts. Health Resources Statistics Unit. Hospital bed & discharge data book. Boston, Mass: Massachusetts Dept. of Public Health, 1987.

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37

Hughes, William O. Statistical analysis of a large sample size pyroshock test data set. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1997.

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38

Teske, Milton E. Correlation of the USDA Forest Service drop size distribution data base. Princeton, NJ: Continuum Dynamics, Inc., 1992.

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39

Hughes, William O. Statistical analysis of a large sample size pyroshock test data set. Cleveland, Ohio: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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40

Hughes, William O. Statistical analysis of a large sample size pyroshock test data set. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1997.

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41

Alexander, David. Key success factors for introducing a variable data colour press into a traditional commercial print enterprise. London: LCP, 2003.

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42

Skeel, Robert D. A method for the spatial discretization of parabolic equations in one space variable. Urbana, IL (1304 W. Springfield Ave., Urbana 61801): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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43

Nicholson, James H. Calculator enhancement for single-variable calculus: A manual of applications using the HP-28S calculator. San Diego: Harcourt Brace Jovanovich, 1991.

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44

Inc, Bergwall Productions. Collect/Record Variable Data. Delmar Pub, 1994.

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45

Eaton, Kent. When do Subnational Policy Challenges Succeed? Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198800576.003.0002.

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This chapter elaborates the book’s theoretical framework by focusing on the three critical variables—structural, institutional, and coalitional—that help explain the outcome of the two types of subnational policy challenges conceptualized in Chapter 1. It argues that a subnational jurisdiction’s structural significance is critical for the ability to influence the national policy regime (the second type of policy challenge), while its institutional capacity is essential for the defense of ideologically deviant subnational policy regimes (the first type of policy challenge). The third variable, internal and external coalitional strength, matters for both types of challenges. After situating these hypotheses relative to a variety of political science literatures, the chapter then introduces the Bolivian, Ecuadorian, and Peruvian cases by focusing on the similarities that make these countries a productive site for small-N comparison. The chapter also scores each country on the dependent variable and describes the book’s data-collection methods.
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46

Salomon, David. Variable-Length Codes for Data Compression. Springer, 2007.

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47

Variable-length Codes for Data Compression. Springer, 2007.

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48

Dummy Variable Creation: Handling Qualitative Data. India: Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630800.

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49

Abdelkhalik, Ossama. Algorithms for Variable-Size Optimization. Taylor & Francis Group, 2022.

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50

Johnston, Edward William. A variable step-size LMS algorithm. 1988.

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