This book presents the proceedings of the International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA) 2023. The conference provides an interdisciplinary forum for researchers, professional engineers and scientists, educators and technologists to discuss, debate and promote research and technology in the upcoming areas of computing, information, communication and their applications. Some of the topics include the Internet of Things (IoT), wireless communications, image and video processing, parallel and distributed computing, and smart grid applications, among others. The book discusses these emerging research areas, providing a valuable resource for researchers and practicing engineers alike.

1 Audience Students seeking master's degrees in applied statistics in the late 1960s and 1970s typically took a year-long sequence in statistical methods. Popular choices of the course text book in that period prior to the availability of high­ speed computing and graphics capability were those authored by Snedecor and Cochran, and Steel and Torrie. By 1980, the topical coverage in these classics failed to include a great many new and important elementary techniques in the data analyst's toolkit. In order to teach the statistical methods sequence with adequate coverage of topics, it became necessary to draw material from each of four or five text sources. Obviously, such a situation makes life difficult for both students and instructors. In addition, statistics students need to become proficient with at least one high-quality statistical software package. This book can serve as a standalone text for a contemporary year-long course in statistical methods at a level appropriate for statistics majors at the master's level or other quantitatively oriented disciplines at the doctoral level. The topics include both concepts and techniques developed many years ago and a variety of newer tools not commonly found in textbooks.

Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners

Logistic regression in R Studio tutorial for beginners. You can do Predictive modeling using R Studio after this course.

Logistic regression in Python tutorial for beginners. You can do Predictive modeling using Python after this course.

Complete step by step guide on logistic regression in SPSS including interpretation and visualization

The routes to many important outcomes including diseases and ultimately death as well as financial credit consist of multiple complex pathways containing interrelated events and conditions. We have historically lacked effective methodologies for identifying these pathways and their non-linear and interacting features. This book focuses on recursive partitioning strategies as a response to the challenge of pathway characterization.

Complete Regression Analysis with Python

Statistics for Lawyers presents the science of statistics in action at the cutting edge of legal problems. A series of more than 90 case studies, drawn principally from actual litigation, have been selected to illustrate important areas of the law in which statistics has played a role and to demonstrate a variety of statistical tools. Some case studies raise legal issues that are being intensely debated and lie at the edge of the law.

Predictive Analytics - Learn R syntax for step by step logistic regression model development and validations

The contributions collected in this book have been written by well-known statisticians to acknowledge Ludwig Fahrmeir's far-reaching impact on Statistics as a science, while celebrating his 65th birthday. The contributions cover broad areas of contemporary statistical model building, including semiparametric and geoadditive regression, Bayesian inference in complex regression models, time series modelling, statistical regularization, graphical models and stochastic volatility models.

Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners.