The Nest of Engineers

Let’s see what you will learn: Stepwise regression + Non-linear regression + K nearest neighbor + Decision trees + Neural networks + Two-stage cluster analysis + Survival analysis.

What you will learn in this course? Perform advanced linear regression using predictive selection techniques + Perform any type of non-linear regression analysis + Prediction using the k nearest neighbor (KNN) technique. + Using binary trees (CART) for prediction (both regression and classification trees) + Using non-binary (CHAID) trees for prediction (both regression and classification trees) + Construction and training of a multilayer perceptron (MLP) + Construction and training of a radial basis neural network (RBF) + Perform two-way clustering analysis + Carrying out survival analysis using the Kaplan-Meier method + Performing survival analysis using Cox regression + Validation of prediction techniques (KNN, trees, neural networks) using validation set and cross-validation approach + Save a predictive analytics model and use it to predict future new data.

The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.

Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.

After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single-level and multilevel data.

The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment. With R, it is possible to fit a variety of latent variable models in the multilevel context, including factor analysis, structural models, item response theory, and latent class models. The third edition also includes new sections in Chapter 11 describing two useful alternatives to standard multilevel models, fixed effects models and generalized estimating equations. These approaches are particularly useful with small samples and when the researcher is interested in modeling the correlation structure within higher-level units (e.g., schools). The third edition also includes a new section on mediation modeling in the multilevel context, in Chapter 11.

This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.

Various didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform.

Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment $\textsf{R}$ is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations.

Features of Mathworks Matlab: Perform a variety of complex mathematical calculations and heavy + Development environment for managing code, files, and data + Explore ways to achieve this solution + A variety of mathematical functions for linear algebra, statistics, Fourier analysis, optimization, filtering, numerical integration and ... + Drawing two-dimensional and three-dimensional graphics functions for visualizing data as + Design and construction of user interfaces under the programming languages ​​C ++, C or Java + Ability to test and measure the exact functions and graphs + The possibility of communication signal processing, image and video + There are various Jbhabzarhay engineering companies for specific applications such as + Telecommunications, control, fuzzy, estimates, statistics, data collection, simulation systems.

James Ramsay, Giles Hooker, Spencer Graves ... 202 pages - Publisher: Springer; (April, 2010) - Language: English - ISBN-10: 0387981845 - ISBN-13: 978-0387981840.

The book provides an application-oriented overview of functional analysis, with extended and accessible presentations of key concepts such as spline basis functions, data smoothing, curve registration, functional linear models and dynamic systems. Functional data analysis is put to work in a wide a range of applications, so that new problems are likely to find close analogues in this book. The code in R and Matlab in the book has been designed to permit easy modification to adapt to new data structures and research problems.

From the BackCover: Scientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume in the UseR! Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems.  It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase functional data analysis techniques. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. This book is accompanied by additional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language. The companion 'fda' package for R includes script files to reproduce nearly all the examples in the book including all but one of the 76 figures.

Statistics for Business Students (ISBN: 978-1-63795-762-2) brings the teaching of statistics into the 21st century. Rather than focusing on mathematics behind formulae, this textbook uses two most used platforms to teach the subject, and these are Microsoft Excel and IBM SPSS. The emphasis is on applications, understanding what the functions deliver, and how to interpret the results.

A brief content of this 756-page book is: Preface + Chapter 1 Data Visualisation + Chapter 2 Descriptive statistics + Chapter 3 Probability distributions + Chapter 4 Sampling distributions + Chapter 5 Point and interval estimates + Chapter 6 Hypothesis testing + Chapter 7 Parametric hypothesis tests + Chapter 8 Nonparametric tests + Chapter 9 Linear correlation and regression analysis + Chapter 10 Introduction to time series data, long term forecasts and seasonality + Chapter 11 Short and medium-term forecasts + Appendices + Index.

Lawrence S. Meyers, Glenn C. Gamst, A. J. Guarino ... 736 pages - Publisher: Wiley; (August, 2013) ... Language: English - ISBN-10: 1118357019 - ISBN-13: 978-1118357019.

Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS: Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output. Designed as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. The book provides in-depth chapter coverage of: IBM SPSS statistical output - Descriptive statistics procedures - Score distribution assumption evaluations - Bivariate correlation - Regressing (predicting) quantitative and categorical variables -Survival analysis -t Test - ANOVA and ANCOVA - Multivariate group differences - Multidimensional scaling - Cluster analysis - Nonparametric procedures for frequency data. Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.