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Mathematics --- Mathematics --- Mathematics --- Statistics --- Statistics --- Statistics --- Uncertainty --- Uncertainty --- Uncertainty --- QA 273 Probabilities --- QA 273 Probabilities --- QA 273 Probabilities

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Statistics --- Computer Graphics --- Social sciences --- QA 276.4 Mathematical statistics. Data processing --- 681.3*G --- Mathematics of computing --- 681.3*G Mathematics of computing --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Statistics&delete& --- Graphic methods&delete& --- Computer programs --- Stata. --- Sciences sociales --- Graphic methods --- Computer programs. --- Méthodes graphiques --- Statistiques. --- Logigrammes --- Méthodes statistiques.

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While regression analysis traces the dependence of the distribution of a response variable to see if it bears a particular (linear) relationship to one or more of the predictors, nonparametric regression analysis makes minimal assumptions about the form of relationship between the average response and the predictors. This makes nonparametric regression a more useful technique for analyzing data in which there are several predictors that may combine additively to influence the response. (An example could be something like birth order/gender/and temperament on achievement motivation). Unfortunately, researchers have not had accessible information on nonparametric regression analysisuntil now. Beginning with presentation of nonparametric regression based on dividing the data into bins and averaging the response values in each bin, Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit. The book concludes with ways nonparametric regression can be generalized to logit, probit, and Poisson regression.

Mathematical statistics --- QA 278.2 .H67 2000 Regression analysis. Correlation analysis --- Regression analysis --- Nonparametric statistics --- #SBIB:303H10 --- #PBIB:2003.3 --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- Methoden en technieken: algemene handboeken en reeksen --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Nonparametric statistics. --- Regression analysis. --- Regression Analysis

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681.3 --- Computerwetenschap --- Informatica 681.3 --- Statistische analyse 519.23 --- #SERV: inv. Leuven --- 681.3*G3 --- #ECO:04.09:sectoren IT-technologie ICT internet --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 681.3* / / / / / / / / / / / / / / / / / / / / / / / / / / / / --- #WDIR:wbse --- #SBIB:303H4 --- #SBIB:303H60 --- AA / International- internationaal --- 654 --- Informatica in de sociale wetenschappen --- Bijzondere methoden: algemeen --- Informatieverwerking. Bureautica. --- Informatieverwerking. Bureautica --- QA 76.755 - Computer Software. Handbook --- Statistics --- Handbooks --- Software --- SAS

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This update to the classic book by ggplot2 creator Hadley Wickham upgrades the book to match the many upgrades to ggplot2 that have happened in the last five years. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to : superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales ; add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression ; save any ggplot2 plot (or part thereof) for later modification or reuse ; create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots ; approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot. This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page. New to this edition : brings the book up-to-date with ggplot2 2.1.0, including major updates to the theme system ; new scales, stats and geoms added throughout ; additional practice exercises ; a revised introduction that focuses on ggplot() instead of qplot() ; updated chapters on data and modeling using tidyr, dplyr and broom.

Graphic methods. --- R (Computer program language) --- GNU-S (Computer program language) --- Domain-specific programming languages --- Graphics --- Graphs --- Geometrical drawing --- Least squares --- Mathematics --- Mechanical drawing --- Data Interpretation, Statistical --- Systems Analysis --- Software --- Computer Imaging, Graphics & Vision --- QA 76.755 - Computer Software. Handbook --- Graphic methods --- Statistics --- Computer graphics --- Visualization --- Mathematical statistics --- Statistiques. --- Statistique mathématique. --- Infographie --- Data visualisation. --- Logiciels. --- Statistiques