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Książka nawiązuje do dynamicznie rozwijającej się na całym świecie w badaniach reprezentacyjnych oraz spisach powszechnych metody kalibracji. Podjęta tematyka stanowi próbę wypełnienia dostrzegalnej luki badawczej i jest pierwszym całościowym spojrzeniem na poruszaną w niej problematykę. W polskiej literaturze przedmiotu brak jest bowiem kompleksowego opracowania poświęconego kalibracji. Ma to tym większe znaczenie, że kalibracja jako metoda estymacji będzie odgrywać w praktyce badań statystycznych coraz większą rolę. Podejście kalibracyjne przedstawiono z uwzględnieniem wieloletnich doświadczeń autora będących pokłosiem zastosowań tej techniki w praktyce badań statystycznych. Rozważono też możliwości wykorzystania tego podejścia w kontekście rozwoju statystyki publicznej w Polsce. Ukazano szeroki wachlarz zagadnień dotyczących kalibracji w badaniach z brakami odpowiedzi czy w spisach realizowanych metodą mieszaną. Analizie poddano także podejście funkcyjne w procesie wyznaczania wag kalibracyjnych, wspomagane modelem i w ujęciu hybrydowym. Prezentowana książka ma charakter metodologiczno-empiryczny, dotyczy zarówno teorii estymacji, jak i jej praktycznych zastosowań. W warstwie metodologicznej opisany został aktualny stan badań naukowych w obszarze kalibracji z uwzględnieniem szczegółowego przeglądu literatury, który może stanowić punkt wyjścia do rozważań dla innych autorów chcących zająć się prezentowaną problematyką. W warstwie empirycznej, korzystając z rzeczywistych danych pochodzących z badań realizowanych przez Główny Urząd Statystycznych, dokonano kompleksowej oceny wag i miar jakości rozważanych estymatorów kalibracyjnych oraz ich zastosowań w rynku pracy.

Regression analysis.

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The definition of requested requirements by the companies represents one of the key aspects for the entrance of new professional figures. In particular, focusing the attention on food & beverage sector, in this study two job profiles are considered: pastry chef e pastry assistant. Data for this analysis are collected by The AdeccoGroup in Italy in 2016 and 2017. The personal competencies to make capable to face the growing flexibility of the profession are object of specified request cross-sectional to more economic sectors. After a brief description of the database content, the principal objective of the research is to report the most requested requirements for the companies. Other analysis are provided to show possible relationships among these requirements and the previous experience owned by candidates. Finally, a comparison is presented about the competencies requested by the two job figures using descriptive statistics and classification techniques.

Logistic regression analysis.

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Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented.The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference.In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book.The book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

Regression analysis. --- Anàlisi de regressió

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Build effective regression models in R to extract valuable insights from real data About This Book Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions Who This Book Is For This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful What You Will Learn Get started with the journey of data science using Simple linear regression Deal with interaction, collinearity and other problems using multiple linear regression Understand diagnostics and what to do if the assumptions fail with proper analysis Load your dataset, treat missing values, and plot relationships with exploratory data analysis Develop a perfect model keeping overfitting, under-fitting, and cross-validation into consideration Deal with classification problems by applying Logistic regression Explore other regression techniques – Decision trees, Bagging, and Boosting techniques Learn by getting it all in action with the help of a real world case study. In Detail Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – l...

R (Computer program language) --- Regression analysis.

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Sampling (Statistics) --- Linear models (Statistics) --- Regression analysis. --- Autoregression (Statistics) --- Regression analysis