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‘Teaching and Researching Autonomy – 2nd edition’ by Phil Benson is one of the books in the series ‘Applied Linguistics in Action’ edited by Christopher Candlin and David R. Hall. The editors in the General Editor’s Preface claim that this version of the book provides a clear, up-to-date and accessible, authoritative account of topics within applied linguistics. Indeed it is so. The book focuses on the issues and challenges autonomy as a key concept in language education puts before teachers and researchers in the field and provides them with the contextual theoretical knowledge and concepts as well as the tools they may need in carrying out their own practice research in spite of the very evasive nature of the core idea.
The 2014 edition shows that business is clear that we must keep up the push for progress. Reform of the education system needs to clearly set out what we want our schools to deliver – young people with not only knowledge and skills but also with the characteristics and behaviours that set them up for success in life and work – and effectively hold schools to account against this. We’ve got to make sure that all young people receive an education that will enable them to fulfil their potential – in every school across the UK, whatever the area and whoever is in charge. This year’s survey also shows us that many businesses remain concerned about the challenges they have with filling the roles of the future that will require more high-skilled employees. Beyond schools, a more responsive and demand-led system of skills development is needed to overcome this. The increased take up of advanced and higher apprenticeships and commitment to funding training at higher levels is a promising development, but it’s
Pearson model is utilized to discover the connection between the traits x and y in view of the estimation of r. Pearson helps us to discover how intently an attribute is related to different attributes. We have a dataset of almost 100 clients portraying their review time and exam execution with the assistance of 5 attributes. In view of the examination of information, we can choose which attribute can be considered and which attribute can be dismissed. For instance, in Pearson technique, if the estimation of r is more than 0.5 then the attributes are thought to be emphatically related and if it is beneath 0.3 the traits are inadequately related. Correlation between two variables in Spearman is equivalent to the rank estimation of two variables in Pearson correlation; while linear relationships are assessed in Pearson correlation, monotonic relationships are evaluated in spearman’s correlation.
Abstract. Unbiased plotting position formulas are discussed to fit the Pearson Type 3 distribution (PIII). The best quantile estimate made from the plotting position should be unbiased and should have the smallest root means square error among all such estimates. Probability plot correlation coefficient (PPCC) is used to evaluate goodness of fit to test the PIII distribution hypothesis. Results obtained using the annual maximum flow data from Peninsular of Malaysia based on PPCC show the plotting position formulas consistently produced linear probability plots with correlation coefficient near to one. Based on root mean square error (RMSE) and root mean absolute error, the Weibull formula performs better than the other formulas.
Most existing binary classification methods target on the optimization of the overall clas- sification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introduced in this context as a novel sta- tistical framework for handling asymmetric type I/II error priorities. It seeks classifiers with a minimal type II error and a constrained type I error under a user specified level. This article is the first attempt to construct classifiers with guaranteed theoretical perfor- mance under the NP paradigm in high-dimensional settings. Based on the fundamental Neyman-Pearson Lemma, we used a plug-in approach to construct NP-type classifiers for Naive Bayes models. The proposed classifiers satisfy the NP oracle inequalities, which are natural NP paradigm counterparts of the oracle inequalities in classical binary classifica- tion. Besides their desirable theoretical properties, we also demonstrated their numerical advantages in prioritized error control via both simulation and real data studies.
This paper proposes a novel Pearson-type quasi maximum likelihood estimator (QMLE) of GARCH(p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non- Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not the heavy- tailed but also the skewed innovations. Under the stationarity and weak moment conditions, the 15
Based on Pearson's correlation coefficient statistical inference often focuses on testing the null hypothesis that the true correlation coefficient ρ is equal to 0, based on the value of the sample correlation coefficient r. It derives a confidence interval that, on repeated sampling, has a given probability of containing ρ. Thus, the go track app is effectively analyzed using Pearson and spearman correlation and control chart.
operating environment and in the presence of interfering tar- gets. The local sensors are assumed to be identical or diﬀerent CFAR processors taking their own decisions about the pres- ence of a target. Such binary information is subsequently sent to a fusion centre for the final decision which is taken accord- ing to “AND” or “OR” fusion logic. In , the performance of single CFAR detectors is addressed for the case of homo- geneous Pearson background. However, as in many practical situations, the radar system is expected to work in nonnom- inal disturbance situations. This has motivated us to investi- gate the performances in more general scenarios and extend their results to distributed CFAR systems. Thus, we have con- sidered the presence in the local sensor reference windows of spurious targets. The performances assessment, conducted via Monte Carlo simulations have shown that the distributed systems, especially the combination of diﬀerent CFAR pro- cessors when the clutter is modelled as positive alpha-stable measurements and using OR fusion rule, oﬀer robustness proprieties against multiple targets.
Thus the implicit numerical diﬃculties for truncation of any conﬁg- uration density motivate two areas of investigation: ﬁrst, continue the numerical approach started by (Koev and Edelman (2006)) with the conﬂuent hypergeometric functions and extend it to the case of some conﬁguration series type, as Pearson VII, Bessel, Logistic, for exam- ple; or second, propose a theoretical approach for solving the problem analytically (see Caro-Lopera et al (2009)).
Developing products and services to meet the unique needs of underserved communities requires full-scale business model innovation, new product formats and technology bases, unconventional distribution channels and alliances, and creative revenue models. To help catalyze and support this innovation, in 2015 we prepared to launch the Pearson Tomorrow’s Markets Incubator – a £1m initiative designed to help Pearson employees bring new product and service ideas that profi tably serve low-income consumers to market. The Incubator will support employees as they collect evidence to build the case for their ideas, develop and test prototypes and – if there’s a clear path to profi tability – help take them to market. Ultimately, our goal is to tap into the creativity of the Pearson people who have a deep understanding of unmet customer needs in the markets where they live and work, and to provide them with a platform to propose, develop, and test relevant product and service ideas.
In this note we suggest some statistical interpretations of KL divergence which can help our understanding of this important theoretical concept. Section 2 reviews the well-known connec- tions with maximum likelihood and expected log-likelihood ratios. Section 3 shows how the Neyman–Pearson lemma gives a new interpretation of D and gives an alternative proof of the non-negativity property. Section 4 overviews the understanding of the asymmetry of D from the information geometric point of view. An extension of the argument is mentioned with relation to discriminant analysis in Section 5.