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Titlebook: Learning Regression Analysis by Simulation; Kunio Takezawa Book 2014 Springer Japan 2014 Akaike‘s Information Criterion (AIC).basic concep

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書(shū)目名稱(chēng)Learning Regression Analysis by Simulation
編輯Kunio Takezawa
視頻videohttp://file.papertrans.cn/583/582782/582782.mp4
圖書(shū)封面Titlebook: Learning Regression Analysis by Simulation;  Kunio Takezawa Book 2014 Springer Japan 2014 Akaike‘s Information Criterion (AIC).basic concep
描述.The standard approach of most introductory books for practical statistics is that readers first learn the minimum mathematical basics of statistics and rudimentary concepts of statistical methodology. They then are given examples of analyses of data obtained from natural and social phenomena so that they can grasp practical definitions of statistical methods. Finally they go on to acquaint themselves with statistical software for the PC and analyze similar data to expand and deepen their understanding of statistical methods..This book, however, takes a slightly different approach, using simulation data instead of actual data to illustrate the functions of statistical methods. Also, R programs listed in the book help readers realize clearly how these methods work to bring intrinsic values of data to the surface. R is free software enabling users to handle vectors, matrices, data frames, and so on..For example, when a statistical theory indicates that an event happens with a 5 % probability, readers can confirm the fact using R programs that this event actually occurs with roughly that probability, by handling data generated by pseudo-random numbers. Simulation gives readers populat
出版日期Book 2014
關(guān)鍵詞Akaike‘s Information Criterion (AIC); basic concepts of linear algebra; basic concepts of statistics; l
版次1
doihttps://doi.org/10.1007/978-4-431-54321-3
isbn_softcover978-4-431-56143-9
isbn_ebook978-4-431-54321-3
copyrightSpringer Japan 2014
The information of publication is updating

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Simple Regression,. where . ( = ..) is a residual. This process yields the regression equation: . where .. is the intercept and .. is the gradient (slope). Each data point is represented as . Values such as .. and .. are called regression coefficients. The “ . ” (hat) of . and . indicates that these values are estimates.
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Linear Mixed Model, are column matrices for which the lengths are .. and can be expressed in the form: . Here, {..} (1 ≤ . ≤ ..) are observations of the .-th treatment (1 ≤ . ≤ .); {..} (1 ≤ . ≤ .) are realizations from .(0, ..) (normal distribution with mean 0 and variance ..).
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a frames, and so on..For example, when a statistical theory indicates that an event happens with a 5 % probability, readers can confirm the fact using R programs that this event actually occurs with roughly that probability, by handling data generated by pseudo-random numbers. Simulation gives readers populat978-4-431-56143-9978-4-431-54321-3
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