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Mean value analysis
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21	CLP(Intervals) Revisited 1 F. Benhamou D. McAllester P. Van Hentenryck Add to Reading List Source URL: cs.brown.edu Language: English - Date: 2006-08-07 20:12:27 Software engineering Interval arithmetic Constraint logic programming Local consistency Antiderivative Interval Mean value theorem Constraint satisfaction problem Constraint algorithm Constraint programming Mathematics Mathematical analysis
22	On stochastic comparisons of population densities and life expectancies Add to Reading List Source URL: www.demographic-research.org Language: English - Date: 2010-02-11 11:26:24 Statistics Mathematics Mathematical analysis Stochastic ordering Life expectancy Life table Mean value theorem Itō diffusion Actuarial science Demography Population
23	X Power Distribution in Randomized Weighted Voting: the Effects of the Quota Joel Oren, University of Toronto, Canada Yuval Filmus, Institute of Advanced Study, USA Yair Zick, Carnegie-Mellon University, USA Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2015-01-06 11:43:22 Cooperative games Game theory Voting systems Probability theory Fair division Shapley value Normal distribution Mean Weighted voting Statistics Mathematics Mathematical analysis
24	C:/Users/Alex/Desktop/Documents/zedpapers/sharingnonatomicpoliticalo13/nonatomicto13oooooo4.dvi Add to Reading List Source URL: alexcoram.files.wordpress.com Language: English - Date: 2013-09-22 15:58:29 Cooperative games Fair division Shapley value Mean value theorem Logarithm Intermediate value theorem Mathematical analysis Mathematics Game theory
25	10 Risks in Value-at-Risk Standard Value-at- Risk (VaR) methodology pretends to superior identiﬁcation of the risks of large market losses. Instead, it indulges the common fallacy of extrapolating from small losses. It Add to Reading List Source URL: cupola.columbia.edu Language: English - Date: 2015-04-20 02:55:38 Statistical inference Mathematical finance Estimation theory Kurtosis Standard deviation Variance Value at risk Mean squared error Percentile Statistics Data analysis Summary statistics
26	Some Very Basic Concepts Reviewed in the First Chapter of Howell. Variables A variable is a quantity that can take on different values. A constant is a quantity that is always of the same value. Discrete variable Add to Reading List Source URL: core.ecu.edu Language: English - Date: 2013-09-04 22:10:27 Statistical inference Data analysis Logical syntax Sample mean and sample covariance Descriptive statistics Parameter Mean Statistic Expected value Statistics Summary statistics Variables
27	Hydrol. Earth Syst. Sci., 14, 2167–2175, 2010 www.hydrol-earth-syst-sci.netdoi:hess © Author(sCC Attribution 3.0 License. Hydrology and Add to Reading List Source URL: www.hydrol-earth-syst-sci.net Language: English - Date: 2014-12-04 02:43:05 Statistical inference Quantile Root-mean-square deviation Normal distribution Mean squared error Errors and residuals in statistics Generalized extreme value distribution Statistics Estimation theory Regression analysis
28	This article was published in an Elsevier journal. The attached copy is furnished to the author for non-commercial research and education use, including for instruction at the author’s institution, sharing with colleag Add to Reading List Source URL: www.umass.edu Language: English - Date: 2009-09-07 14:24:32 Data analysis Singular value decomposition Psychometrics Factor analysis Principal component analysis Weighted mean Variance STING Pearson product-moment correlation coefficient Statistics Multivariate statistics Parametric statistics
29	On MMSE Properties and I-MMSE Implications in Parallel MIMO Gaussian Channels Ronit Bustin Dept. Electrical Engineering Technion−IIT Technion City, Haifa 32000 Add to Reading List Source URL: www.ece.ust.hk Language: English - Date: 2010-04-27 05:21:46 Control theory Matrix theory Singular value decomposition Linear algebra Data analysis Eigenvalues and eigenvectors Minimum mean square error State space Covariance matrix Algebra Statistics Mathematics
30	Achievement Objective Investigate situations that involve elements of chance: – calculating and interpreting expected values and standard deviations of discrete random variables; Exemplar 1 Add to Reading List Source URL: new.censusatschool.org.nz Language: English - Date: 2012-09-17 21:35:12 Probability and statistics Summary statistics Variance Standard deviation Expected value Probability distribution Random variable Mean Statistics Probability theory Data analysis