{"product_id":"an-introduction-to-statistical-modelling-9780470711019","title":"An Introduction to Statistical Modelling","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eStatisticians rely heavily on making models of 'causal situations' in order to fully explain and predict events. Modelling therefore plays a vital part in all applications of statistics and is a component of most undergraduate programmes.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eSeries preface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Models in data analysis.\u003c\/p\u003e \u003cp\u003e1.2 Populations and samples.\u003c\/p\u003e \u003cp\u003e1.3 Variables and factors.\u003c\/p\u003e \u003cp\u003e1.4 Observational and experimental data.\u003c\/p\u003e \u003cp\u003e1.5 Statistical models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Distributions and inference.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Random variables and probability distributions.\u003c\/p\u003e \u003cp\u003e2.2 Probability distributions as models.\u003c\/p\u003e \u003cp\u003e2.3 Some common distributions.\u003c\/p\u003e \u003cp\u003e2.4 Sampling distributions.\u003c\/p\u003e \u003cp\u003e2.5 Inference.\u003c\/p\u003e \u003cp\u003e2.6 Postscript.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Normal response and quantitative explanatory variables: regression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Motivation.\u003c\/p\u003e \u003cp\u003e3.2 Simple regression.\u003c\/p\u003e \u003cp\u003e3.3 Multiple regression.\u003c\/p\u003e \u003cp\u003e3.4 Model building.\u003c\/p\u003e \u003cp\u003e3.5 Model validation and criticism.\u003c\/p\u003e \u003cp\u003e3.6 Comparison of regressions.\u003c\/p\u003e \u003cp\u003e3.7 Non-linear models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Normal response and qualitative explanatory variables: analysis of variance.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Motivation.\u003c\/p\u003e \u003cp\u003e4.2 One-way arrangements.\u003c\/p\u003e \u003cp\u003e4.3 Cross-classifications.\u003c\/p\u003e \u003cp\u003e4.4 Nested classifications.\u003c\/p\u003e \u003cp\u003e4.5 A general approach via multiple regression.\u003c\/p\u003e \u003cp\u003e4.6 Analysis of covariance.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Non-normality: the theory of generalized linear models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 The generalized linear model.\u003c\/p\u003e \u003cp\u003e5.3 Fitting the model.\u003c\/p\u003e \u003cp\u003e5.4 Assessing the fit of a model: deviance.\u003c\/p\u003e \u003cp\u003e5.5 Comparing models: analysis of deviance.\u003c\/p\u003e \u003cp\u003e5.6 Normal models.\u003c\/p\u003e \u003cp\u003e5.7 Inspecting and checking models.\u003c\/p\u003e \u003cp\u003e5.8 Software.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Binomial response variables: logistic regression and related method.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Binary response data.\u003c\/p\u003e \u003cp\u003e6.2 Modelling binary response probabilities.\u003c\/p\u003e \u003cp\u003e6.3 Logistic regression.\u003c\/p\u003e \u003cp\u003e6.4 Related methods.\u003c\/p\u003e \u003cp\u003e6.5 Ordered polytomous data.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Tables of counts and log-linear models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Data mechanisms and distributions.\u003c\/p\u003e \u003cp\u003e7.3 Log-linear models for means.\u003c\/p\u003e \u003cp\u003e7.4 Models for contingency tables.\u003c\/p\u003e \u003cp\u003e7.5 Analysis.\u003c\/p\u003e \u003cp\u003e7.6 Applications.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Further topics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Continuous non-normal responses.\u003c\/p\u003e \u003cp\u003e8.3 Quasi-likelihood.\u003c\/p\u003e \u003cp\u003e8.4 Overdispersion.\u003c\/p\u003e \u003cp\u003e8.5 Non-parametric models.\u003c\/p\u003e \u003cp\u003e8.6 Conclusion: the art of model building.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402415677783,"sku":"9780470711019","price":37.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470711019.jpg?v=1730480331","url":"https:\/\/bookcurl.com\/products\/an-introduction-to-statistical-modelling-9780470711019","provider":"Book Curl","version":"1.0","type":"link"}