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This book creates a balance between the theory, practical applications, and computer implementation behind Regression--one of the most widely used techniques in analyzing and solving real world problems. The book begins with a thorough explanation of prerequisite knowledge with a discussion of Simple Regression Analysis including the computer applications. This is followed by Multiple Regression--a widely used tool to predict a response variable using two or more predictors. Since the analyses of regression models involve tedious and complex computations, complete computer analysis including the interpretation of multiple regression problems along with the model adequacy tests and residual analysis using widely used computer software are presented. The use of computers relieves the analyst of tedious, repetitive calculations, and allows one to focus on creating and interpreting successful models. Finally, the book extends the concepts to Regression and Modeling. Different models that provide a good fit to a set of data and provide a good prediction of the response variable are discussed. Among models discussed are the nonlinear, higher order, and interaction models, including models with qualitative variables. Computer analysis and interpretation of computer results are presented with real world applications. We also discuss all subset regression and stepwise regression with applications. Several flow charts are presented to illustrate the concepts. The statistical concepts for regression, computer instructions for the software-- Excel and MINITAB--used in the book and all of the data files used can be downloaded from the website link provided.

Regression analysis --- Data processing. --- coefficient of correlation --- correlation --- dependent variable --- dummy variable --- independent variable --- interaction model --- least squares estimates --- least squares prediction equation --- linear regression --- multiple coefficient of determination --- multiple regression and modeling --- nonlinear models --- regression line --- residual analysis --- scatterplot --- second-order model --- stepwise regression

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Some in the social sciences argue that the same logic applies to both qualitative and quantitative methods. In A Tale of Two Cultures, Gary Goertz and James Mahoney demonstrate that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. They identify and discuss major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, Goertz and Mahoney also seek to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. This book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points.

Social sciences --- Political sociology --- Political science --- Mass political behavior --- Political behavior --- Sociology --- Administration --- Civil government --- Commonwealth, The --- Government --- Political theory --- Political thought --- Politics --- Science, Political --- State, The --- Research --- Methodology. --- Sociological aspects --- 2 x 2 tables. --- David Hume. --- Fundamental Principle of Variable Transformation. --- Fundamental Problem of Causal Inference. --- Fundamental Tradeoffs. --- Hooke's law. --- Principle of Conceptual Opposites. --- Principle of Conceptual Overlap. --- Principle of Unimportant Variation. --- additive-linear causal model. --- aggregation technique. --- asymmetry. --- case selection. --- case studies. --- cases. --- categories. --- causal complexity. --- causal effects. --- causal heterogeneity. --- causal inference. --- causal mechanism. --- causal model. --- causal models. --- causal-process observations. --- causality. --- causation. --- cause. --- causes-of-effects approach. --- characteristics. --- concepts. --- conceptualization. --- constant conjunction definition. --- control variables. --- counterfactual analysis. --- counterfactual definition. --- counterfactuals. --- cross-case analysis. --- data analysis. --- data transformations. --- data-set observations. --- definitions. --- dependent variable. --- effects-of-causes approach. --- empirical testing. --- equifinality. --- error. --- experiments. --- fuzziness. --- fuzzy-set analysis. --- fuzzy-set transformations. --- generalization. --- hypothesis testing. --- indicators. --- individual case analysis. --- individual cases. --- inferential statistics. --- logging. --- logic. --- meaning retention. --- measurement. --- membership functions. --- methodological pluralism. --- minimum rewrite rule. --- mixed-method research. --- multimethod research. --- multiple causation. --- natural language. --- necessary condition. --- nonoccurrence. --- occurrence. --- opposites. --- perfect predictors. --- political science. --- probability theory. --- process tracing. --- qualitative research. --- quantitative research. --- regression. --- scale types. --- scope conditions. --- semantic transformations. --- semantics. --- set theory. --- set-theoretic causal model. --- set-theoretic generalization. --- social science research. --- social sciences. --- sociology. --- standardization. --- static causal asymmetry. --- statistical analysis. --- statistical method. --- statistical model. --- statistics. --- sufficient condition. --- symmetry. --- translation problems. --- typologies. --- variable transformations. --- within-case analysis. --- within-model responses.