Statistical assumptions

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  • Many people find statistics challenging, but most statistics professors do not. As a result, it is sometimes hard for our professors and the authors of statistics textbooks to make statistics clear and practical for business students, managers, and executives. Business Statistics Demystified fills that gap. We begin slowly, introducing statistical concepts without mathematics.

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  • In addition to covering statistical methods, most of the existing books on equating also focus on the practice of equating, the implications of test development and test use for equating practice and policies, and the daily equating challenges that need to be solved. In some sense, the scope of this book is narrower than of other existing books: to view the equating and linking process as a statistical estimation task.

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  • We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translation quality? We present empirical results casting doubt on this common, but unproved, assumption. Using a state-ofthe-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. ...

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  • A serious bottleneck of comparative parser evaluation is the fact that different parsers subscribe to different formal frameworks and theoretical assumptions. Converting outputs from one framework to another is less than optimal as it easily introduces noise into the process. Here we present a principled protocol for evaluating parsing results across frameworks based on function trees, tree generalization and edit distance metrics. This extends a previously proposed framework for cross-theory evaluation and allows us to compare a wider class of parsers. ...

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  • Many current approaches to statistical language modeling rely on independence a.~sumptions 1)etween the different explanatory variables. This results in models which are computationally simple, but which only model the main effects of the explanatory variables oil the response variable. This paper presents an argmnent in favor of a statistical approach that also models the interactions between the explanatory variables. The argument rests on empirical evidence from two series of experiments concerning automatic ambiguity resolution. ...

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  • Chapter 14 - Simple linear regression analysis. After mastering the material in this chapter, you will be able to: Explain the simple linear regression model, find the least squares point estimates of the slope and y-intercept, describe the assumptions behind simple linear regression and calculate the standard error,...

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  • Chapter 15 - Multiple regression and model building. After mastering the material in this chapter, you will be able to: Explain the multiple regression model and the related least squares point estimates, explain the assumptions behind multiple regression and calculate the standard error, calculate and interpret the multiple and adjusted multiple coefficients of determination,...

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  • This book was motivated by the author’s experience in teaching accounting at postgraduate level (MBA and MSc) at Aston Business School and in-house training provided for non-financial managers in many organizations to introduce them to the use of financial tools and techniques. My own education as an accountant was aimed at achieving professional recognition and emphasized an uncritical acceptance of the tools and techniques that I was taught.

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  • CHAPTER 25 Variance Estimation: Should One Require Unbiasedness? There is an imperfect analogy between linear estimation of the coefficients and quadratic estimation of the variance in the linear model. This chapter sorts out the principal commonalities and differences, a task obscured by the widespread but unwarranted imposition of the unbiasedness assumption.

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  • CHAPTER 39 Random Regressors. Until now we always assumed that X was nonrandom, i.e., the hypothetical repetitions of the experiment used the same X matrix. In the nonexperimental sciences, such as economics, this assumption is clearly inappropriate.

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  • CHAPTER 45 Flexible Functional Form. So far we have assumed that the mean of the dependent variable is a linear function of the explanatory variables. In this chaper, this assumption will be relaxed. We first discuss the case where the explanatory variables are categorical variables.

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  • CHAPTER 29 Constrained Least Squares. One of the assumptions for the linear model was that nothing is known about the true value of β. Any k-vector γ is a possible candidate for the value of β. We ˜ used this assumption e.g. when we concluded that an unbiased estimator By of β ˜ must satisfy BX = I.

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  • The text of this report was prepared by a team of authors from ITU’s Strategy and Policy Unit (SPU) led by Lara Srivastava, comprising Tim Kelly, Chin Yung Lu and Lucy Yu. The statistical tables were drawn from the ITU Information Society Statistics Database. Kenichi Yamada assisted with their compilation. The world map of Information Society Statistics was done by Youlia Lozanova. The cover design is by the ITU Publications Production Division. The report has benefited from the input and comments of many people to whom we owe our thanks.

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  • Chapter 5 - Classical linear regression model assumptions and diagnostics. In this chapter, students will be able to understand: Describe the steps involved in testing regression residuals for heteroscedasticity and autocorrelation, explain the impact of heteroscedasticity or autocorrelation on the optimality of OLS parameter and standard error estimation, distinguish between the Durbin--Watson and Breusch--Godfrey tests for autocorrelation,...

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  • CHAPTER 4 Selecting Crystal Ball Assumptions This chapter reviews basic concepts of probability and statistics using graphics from Crystal Ball’s distribution gallery, a portion of which is shown in Figure 4.1. If you have not had a class in basic probability and statistics at some point in your life or you need a refresher on these topics

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  • This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumptions that limit their potential effectiveness.

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  • Characterizing the Performance of Adaptive Filters 19.3 Analytical Models, Assumptions, and Definitions System Identification Model for the Desired Response Signal • Statistical Models for the Input Signal • The Independence Assumptions • Useful Definitions 19.4 Analysis of the LMS Adaptive Filter Mean Analysis • Mean-Square Analysis 19.5 Performance Issues Basic Criteria for Performance • Identifying Stationary Systems • Tracking Time-Varying Systems Normalized Step Sizes • Adaptive and Matrix Step Sizes • Other Time-Varying Step Size Methods 19.

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  • The term model refers to a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates. Good definition? Let’s read more. Today we will start from something very important: Some guidance for model risk management Board of Governors of the Federal Reserve System Office of the Comptroller of the Currency SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT Banks rely heavily on quantitative analysis and models in most aspects of financial decision making.

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  • Most statistical machine translation systems rely on composed rules (rules that can be formed out of smaller rules in the grammar). Though this practice improves translation by weakening independence assumptions in the translation model, it nevertheless results in huge, redundant grammars, making both training and decoding inefficient. Here, we take the opposite approach, where we only use minimal rules (those that cannot be formed out of other rules), and instead rely on a rule Markov model of the derivation history to capture dependencies between minimal rules. ...

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  • Kỹ thuật kiểm định thống kê có thông số (parametric tests) thường đòi hỏi 1 số giả định (assumptions) về dân số được khảo sát, đặc biệt nhất là tính phân phối bình thường, biến số được đo ở thang khoảng (interval) hoặc thang tỉ số (ratio), và mẫu ngẫu nhiên và độc lập.

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