Basics of qualitative research: Grounded theory procedures and techniques

While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.

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Statistical Comparisons of Classifiers over Multiple Data Sets

Introduction 1. Woman's Place in Man's Life Cycle 2. Images of Relationship 3. Concepts of Self and Morality 4. Crisis and Transition 5. Women's Rights and Women's Judgment 6. Visions of Maturity References Index of Study Participants General Index

In a Different Voice. Psychological Theory and Women’s Development. Cambridge, MA (Harvard University Press) 1982.

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

1. Variable Distributions 2. Bivariate Regression Analysis 3. Basics of Multiple Regression 4. Regression Criticism 5. Fitting Curves 6. Robust Regressions 7. Logit Regression 8. Principle Components and Factor Analysis

Regression with Graphics: A Second Course in Applied Statistics

Introduction to STATA data graphs frequency distributions and univariate statistics T tests, anova, and nonparametric comparisons bivariate regression multiple regression regression diagnostics fitting curves robust regression logistics regression principal components and factor analysis list of some Stata commands Monte Carlo and Bootstrap Methods.

Statistics with Stata

U.S. public opinion regarding climate change has become increasingly polarized in recent years, as partisan think tanks and others worked to recast an originally scientific topic into a political wedge issue. Nominally “scientific” arguments against taking anthropogenic climate change seriously have been publicized to reach informed but ideologically receptive audiences. Reflecting the success of such arguments, polls have noted that concern about climate change increased with education among Democrats, but decreased with education among Republicans. These observations lead to the hypothesis that there exist interaction (non-additive) effects between education or knowledge and political orientation, net of other background factors, in predicting public concern about climate change. Two regional telephone surveys, conducted in New Hampshire (n = 541) and Michigan (n = 1, 008) in 2008, included identical climate-change questions that provide opportunities to test this hypothesis. Multivariate analysis of both surveys finds significant interactions. These empirical results fit with theoretical interpretations and several other recent studies. They suggest that the classically identified social bases of concern about the environment in general, and climate in particular, have shifted in recent years. Narrowcast media, including the many Web sites devoted to discrediting climate-change concerns, provide ideal conduits for channeling contrarian arguments to an audience predisposed to believe and electronically spread them further. Active-response Web sites by climate scientists could prove critical to counterbalancing contrarian arguments.

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Education, politics and opinions about climate change evidence for interaction effects

V. contemporary advances and classic advice for analyzing mediating and moderating variables

Best practices in quantitative methods for developmentalists: V. Contemporary advances and classic advice for analyzing mediating and moderating variables.

Lawrence C. Hamilton

Papers

Regression with Graphics: A Second Course in Applied Statistics

Statistics with Stata

Education, politics and opinions about climate change evidence for interaction effects

V. contemporary advances and classic advice for analyzing mediating and moderating variables

Best practices in quantitative methods for developmentalists: V. Contemporary advances and classic advice for analyzing mediating and moderating variables.