Audi A4

Buying a car is a personal choice that has become a more complex decision because of advances in technology, and reliability issues that are haunting some car makers. Many consumers look to Zack Spencer, the host of Driving Television, for straightforward, no-nonsense, expert advice. In Motormouth, you will find out which vehicles are the safest, most reliable, and best value for your hard-earned dollar. In an easy-to-understand format, you will get: Fuel economy ratings Pros and cons for performance, handling, comfort, and ease-of-use Standard safety features J.D. Power Initial Quality and Dependability scores Base warranty information Engine specifications Pricing for base models Reviews of option packages and trim levels Zack's Top Picks for each category Zack provides insider buying tips to help you, whether you are buying privately, off the internet, or making the rounds to different dealers. He also advises you on your decision to lease, purchase or finance. At your fingertips are strategies and lessons learned from people's adventures in car buying, some with happy endings and others not-so-happy. From a fuel-sipping family friendly hauler to a rubber-burning luxury sports car, you can rely on Motormouth 2011 edition for the information you need to make a wise purchase decision. Go prepared and don't get stuck with a lemon. Take Motormouth along for the ride.

If you’re curious about how things work, this fun and intriguing guide will help you find real answers to everyday problems. By using fundamental math and doing simple programming with the Ruby and R languages, you’ll learn how to model a problem and work toward a solution. All you need is a basic understanding of programming. After a quick introduction to Ruby and R, you’ll explore a wide range of questions by learning how to assemble, process, simulate, and analyze the available data. You’ll learn to see everyday things in a different perspective through simple programs and common sense logic. Once you finish this book, you can begin your own journey of exploration and discovery. Here are some of the questions you’ll explore: Determine how many restroom stalls can accommodate an office with 70 employees Mine your email to understand your particular emailing habits Use simple audio and video recording devices to calculate your heart rate Create an artificial society—and analyze its behavioral patterns to learn how specific factors affect our real society

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms.

Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures

Gain proficiency in R programming Load and manipulate data frames, and "tidy" them using tidyverse tools Conduct statistical analyses and draw meaningful inferences from them Perform modeling from numerical and textual data Generate data visualizations (numerical and spatial) using ggplot2 and understand what is being represented

Every professional needs to perform statistical analysis in some form of the other. In order to perform this task various software tools are available. Majority of them are paid software. R programming which is an open source tool can be used to perform statistical analysis. Since it is an open source tool many front end GUI’s are available to make the job easier for the user. In this book the most popular GUI RStudio is used. RStudio is a most powerful GUI front end for R programming which has been designed to use all the features of this language with ease. This book has been authored with a novice user in mind. Various steps in statistical analysis have been explained in detail using a large number of screenshots. Codes used have been clearly illustrated. The book has been structured in such a manner to ensure that basic concepts have been clearly explained with the help of screenshots before taking on challenging analytical problems. Towards the end of the book the reader is provided with an additional resource which gives out all the codes used in this book as well as those additional ones that have not found their place in the book. Learning R coding is not difficult provided the reader spends time practicing the same. The reader is encouraged to execute all the codes provided in the R_code manual which has been provided at the end of the book. R programming can be compared to that of SPSS (the popular statistical analytical tool) as far as its ability to perform statistical analysis. One tip the author wishes to provide to the reader who is attempting to make data entry within the RStudio environment. It is always better to import data into RStudio for performing data analysis. Data can be imported from Excel , google spread sheets etc. The reader is encouraged to download the install the software and libraries that have been described in the book and to try them out. Advantages of R Programming : 1. It is a powerful statistical tool 2. It is open source and hence it is free 3. It is an excellent tool that can be used to perform visual analysis of a dataset. It can created different types of charts and graphs, thereby facilitating accurate analysis of data

Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields. Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It’s self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences.