Statistical Principles and Techniques in Scientific and Social Research

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The book consists of ten chapters, with the first five chapters focusing mainly on the underlying logic of statistical reasoning and less on specific statistical techniques. This is illustrated with real-life examples and the usual dice, coin and playing card examples. The second chapter is concerned with populations, samples and data summary, discussing e. Population models are the topic of the third chapter, with a focus on describing the most common probability distributions and methods for estimating the parameters of these, especially the maximum likelihood method.

Chapter 4 is devoted to the frequentist approach to statistical inference, especially discussing confidence intervals and hypothesis tests, while Chapter 5 discusses Bayesian and other approaches to statistical inference. The remaining five chapters of the book provide a closer look at specific statistical techniques, but still with a focus on the rationale and objectives of the techniques rather than on technical details.

Chapter 6 discusses linear models and least squares, including correlation, simple and multiple linear regression models, model building, ANOVA, and the general linear model, as well as discussing observational versus experimental data and the design of experiments. Chapter 7 considers the assumptions of the linear regression model and how to check and remedy these, while Chapter 8 is concerned with association between variables, including partial correlations, latent variable models, and factor analysis methodology.

The exploration of complex data sets is the topic of Chapter 9, using principal component analysis, canonical variate analysis, canonical correlation analysis, cluster analysis, and discriminant analysis. Chapter 10, finally, is devoted to miscellaneous special topics, such as repeated measurements, time series, extreme values, and survival data analysis.

The book is well-written, easy to read, and gives a good non-technical overview of both statistical reasoning and most of the important statistical techniques in use today, with mathematical formulas used only when necessary. It would be valuable reading for any non-statistician that wants to get deeper insights into what statistics is all about. On the contrary, Bayesian methods are rarely used in applied statistics, due to the inherent subjectivity of Bayesian inference, which many scientists feel uncomfortable with.

Apart from this minor complaint, this book should serve as a useful non-technical guide to statistical reasoning and techniques. Andreas Rosenblad andreas. His primary research interest is in bootstrap methods and quantile regression with applications in biostatistics. Skip to main content.

Social Science Research: Principles, Methods, and Practices - Open Textbook Library

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Statistical Principles and Techniques in Scientific and Social Research

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