Suchergebnisse

Basic Concepts / Postiglione Paolo, Benedetti Roberto and Piersimoni Federica -- Spatial Sampling Designs / Pantalone Francesco and Benedetti Roberto -- Including Spatial Information in Estimation from Complex Survey Data / Pantalone Francesco and Ranalli Maria Giovanna -- Yield Prediction in Agriculture: A Comparison Between Regression Kriging and Random Forest / Nissi Eugenia and Sarra Annalina -- Land Cover/Use Analysis and Modelling / Carfagna Elisabetta and Di Fonzo Gianrico -- Farm Surveys (Piersimoni Salvioni Andreano) (Occorre renderlo -- molto spatial) -- Exploring Spatial Point Pattern in Agriculture / Andreano M Simona and Mazzitelli Andrea -- Spatial Analysis of Farm Data / Cartone Alfredo and Panzera Domenica -- Spatial Econometric Modelling of Farm Data / Billè Anna Gloria, Salvioni Cristina and Vidoli Francesco -- Areal Interpolation Methods: The Bayesian Interpolation Method / Panzera Domenica -- Small Area Estimation of Agricultural Data / Bertarelli Gaia, Schirripa Spagnolo Francesco, Salvati Nicola and Pratesi Monica -- Cross-sectional Spatial Regression Models for Measuring Agricultural [beta]-convergence / Cartone Alfredo and Postiglione Paolo -- Spatial Panel Regression Models in Agriculture / Postiglione Paolo.

A hands-on, readable guide to machine learning with R. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights and make new predictions. The 3rd edition is fully updated to R 3.6 and features newer and better libraries, advice on ethical and bias issues, and an ...

Introduction and background -- Exploratory data analysis and graphics -- Deterministic functions for ecological modeling -- Probability and stochastic distributions for ecological modeling -- Stochastic simulation and power analysis -- Likelihood and all that -- Optimization and all that -- Likelihood examples -- Standard statistics revisited -- Modeling variance -- Dynamic models -- Afterword -- Appendix: Algebra and calculus basics.

"This book is an overview of how practitioners can work with data from the US Census Bureau using the R programming language. The book will focus on data acquisition and wrangling as well as data analysis and modeling of aggregate and individual-level decennial Census and American Community Survey data. It is designed to be useful for both beginners and advanced users of both R and Census data. Additionally, the book will take advantage of cutting-edge developments in both data wrangling and spatial data analysis in R"--

The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.