Functions Modeling Change, 6th edition prepares students for Calculus by stressing conceptual understanding and the connections among mathematical ideas. The authors emphasize depth of understanding rather than breadth of coverage. Each function is presented symbolically, numerically, graphically and verbally (the Rule of Four.) Students are encouraged to create mathematical models that relate to the world around them and are exposed to a large number of real-world applications, examples, and problems.

Work more effectively and check solutions as you go along with the text! This Student Solutions Manual provides complete solutions to selected problems from Connally's Functions Modeling Change, 2nd Edition. These solutions will help you develop strong problem solving skills. From the Calculus Consortium based at Harvard University, Functions Modeling Change, 2nd Edition prepares readers for the study of calculus, presenting families of functions as models for change. These materials stress conceptual understanding and multiple ways of representing mathematical ideas. The focus throughout is on those topics that are essential to the study of calculus and these topics are treated in depth.

This text provides a strong foundation to precalculus that focuses on a small number of key topics thereby emphasising depth of understanding rather than breath of coverage. It provides a solid way to motivate concepts and develop critical thinking skills. The new fourth edition emphasises functions as models of change. It contains superior exercises and applications that motivate the concepts students can use to fully grasp precalculus

A revision of the best selling innovative Calculus text on the market. Functions are presented graphically, numerically, algebraically, and verbally to give readers the benefit of alternate interpretations. The text is problem driven with exceptional exercises based on real world applications from engineering, physics, life sciences, and economics. Revised edition features new sections on limits and continuity, limits, l'Hopital's Rule, and relative growth rates, and hyperbolic functions.

"The text is suitable for a typical introductory algebra course, and was developed to be used flexibly. While the breadth of topics may go beyond what an instructor would cover, the modular approach and the richness of content ensures that the book meets the needs of a variety of programs."--Page 1.

The Rule of Four: Each function is represented symbolically, numerically, graphically, and verbally. Functions as models of change is the central theme. Exceptional Problems: Examples and problems based on real data help students create mathematical models to help them understand their world. An appropriate number of drill problems are included to assist students in learning techniques. The problems are varied and some are more challenging. Most cannot be done by following a template in the text. Allows for a broad range of teaching styles. This text is flexible enough for use in large lecture halls, small classes, or in group or lab settings. Focuses on fewer topics than is customary, but each topic is treated in greater depth. Only those topics essential to the study of calculus are included. Reflects the spirit of the standards established by the Mathematical Association of America (MAA) and the American Mathematical Association of Two-Year Colleges (AMATYC), and meets the recommendations of the National Council of Teachers of Mathematics (NCTM) Assumes technology has a place in modern mathematics. This text takes full advantage of technology when appropriate, although no specific technology is emphasized. It is important for students to learn how and when to use technology as a tool, as well as its limitations. However the focus of the text is on conceptual understanding not technology.

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Bernard Rosner's FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. Most methods are illustrated with specific instructions as to implementation using software either from SAS, Stata, R, Excel or Minitab. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.