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HS curriculum for Algebra II
HS curriculum for Algebra II

... a. Construct and compare linear, quadratic, and exponential models and solve problems. (CCSS: F-LE) i. Distinguish between situations that can be modeled with linear functions and with exponential functions. (CCSS: F-LE.1) 1. Prove that linear functions grow by equal differences over equal intervals ...
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Sharp estimation of the almost-sure Lyapunov exponent for the

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Nonparametric Triangular Simultaneous Equations Models with

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On the origin of the WD1145 disk and pollution

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Key to the first midterm, first version

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Binary Integer Programming in associative data models

... associative data model, and this thesis analyzes different tools and methods for solving 0-1 integer programs as well as examines their applicability to the computational engine behind these softwares. The first parts are dedicated to a theoretical background on mathematical optimization, linear pro ...
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On Lattices, Learning with Errors, Random Linear Codes, and

... best known polynomial time algorithms for them yield only mildly subexponential approximation factors [24, 38, 5]. It is conjectured that there is no classical (i.e., non-quantum) polynomial time algorithm that approximates them to within any polynomial factor. Lattice-based constructions of one-way ...
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The Optimal Draw-down for a Charity

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Semi-analytical solution of dam-resevoir interaction in the

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Mathematical Tools for Image Collections Outline Problems

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Generalized linear model

In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression. They proposed an iteratively reweighted least squares method for maximum likelihood estimation of the model parameters. Maximum-likelihood estimation remains popular and is the default method on many statistical computing packages. Other approaches, including Bayesian approaches and least squares fits to variance stabilized responses, have been developed.
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