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Business Mathematics was written to meet the needs of a twenty-first century student. It takes a systematic approach to helping students learn how to think and centers on a structured process termed the PUPP Model (Plan, Understand, Perform, and Present). This process is found throughout the text and in every guided example to help students develop a step-by-step problem-solving approach. This textbook simplifies and integrates annuity types and variable calculations, utilizes relevant algebraic symbols, and is integrated with the Texas Instruments BAII+ calculator. It also contains structured exercises, annotated and detailed formulas, and relevant personal and professional applications in discussion, guided examples, case studies, and even homework questions.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Textbooks Business mathematics
- Resource Type:
- e-book
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e-book
The University of North Georgia Press and Affordable Learning Georgia bring you Accounting I. Well-written and straightforward, Principles of Financial Accounting is a needed contribution to open source pedagogy in the business education world. Written in order to directly meet the needs of her students, this textbook developed from Dr. Christine Jonick’s years of teaching and commitment to effective pedagogy. Features: Peer reviewed by academic professionals and tested by students Over 100 charts and graphs Instructional exercises appearing both in-text and for Excel Resources for student professional development This textbook is an Open Education Resource. It can be reused, remixed, and reedited freely without seeking permission.
- Subjects:
- Finance and Accounting
- Keywords:
- Accounting Textbooks
- Resource Type:
- e-book
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e-book
Data structures and algorithms are among the most important inventions of the last 50 years, and they are fundamental tools software engineers need to know. But in my opinion, most of the books on these topics are too theoretical, too big, and too bottom-up: Too theoretical: Mathematical analysis of algorithms is based on simplifying assumptions that limit its usefulness in practice. Many presentations of this topic gloss over the simplifications and focus on the math. In this book I present the most practical subset of this material and eliminate the rest. Too big: Most books on these topics are at least 500 pages, and some are more than 1000. By focusing on the topics I think are most useful for software engineers, I kept this book under 250 pages. Too bottom-up: Many data structures books focus on how data structures work (the implementations), with less about how to use them (the interfaces). In this book, I go “top down”, starting with the interfaces. Readers learn to use the structures in the Java Collections Framework before getting into the details of how they work. Finally, many present this material out of context and without motivation: it’s just one damn data structure after another! I try to alleviate the boredom by organizing the topics around an application—web search—that uses data structures extensively, and is an interesting and important topic in its own right. This application also motivates some topics that are not usually covered in an introductory data structures class, including persistent data structures, with Redis, and streaming algorithms. This book also presents basic aspects of software engineering practice, including version control and unit testing. Each chapter ends with an exercise that allows readers to apply what they have learned. Each exercise includes automated tests that check the solution. And for most exercises, I present my solution at the beginning of the next chapter. This book is intended for college students in computer science and related fields, as well as professional software engineers, people training in software engineering, and people preparing for technical interviews. I assume that the reader knows Java at an intermediate level, but I explain some Java features along the way, and provide pointers to supplementary material. People who have read Think Java or Head First Java are prepared for this book.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Java (Computer program language) Textbooks Data structures (Computer science)
- Resource Type:
- e-book
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e-book
The goal of this book is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best features of Mathematics, Engineering, and Natural Science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating trade offs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.The single most important skill for a computer scientist is problem-solving. By that I mean the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That’s why this chapter is called “The way of the program.”
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Programming languages (Electronic computers) Computer programming Textbooks C (Computer program language)
- Resource Type:
- e-book
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e-book
The goal of this book is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best features of Mathematics, Engineering, and Natural Science. Like mathematicians,computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating trade offs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.The single most important skill for a computer scientist is problem-solving. By that I mean the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That’s why this chapter is called “The way of the program.”
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e-book
Business Ethics is designed to meet the scope and sequence requirements of the single-semester business ethics course. This title includes innovative features designed to enhance student learning, including case studies, application scenarios, and links to video interviews with executives, all of which help instill in students a sense of ethical awareness and responsibility.
- Subjects:
- Business Ethics
- Keywords:
- Business ethics Textbooks
- Resource Type:
- e-book
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e-book
This course uses the basic principles of biology and earth science as a context for understanding environmental policies and resource management practices. Our planet is facing unprecedented environmental challenges, from oil spills to global climate change. In ENSC 1000, you will learn about the science behind these problems; preparing you to make an informed, invaluable contribution to Earth's future. I hope that each of you is engaged by the material presented and participates fully in the search for, acquisition of, and sharing of information within our class.
- Subjects:
- Environmental Sciences
- Keywords:
- Textbooks Environmental sciences
- Resource Type:
- e-book
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e-book
Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.
- Subjects:
- Psychology and Mathematics and Statistics
- Keywords:
- Statistics -- Computer programs R (Computer program language) Textbooks Statistics Social sciences -- Statistical methods
- Resource Type:
- e-book
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e-book
We hope readers will take away three ideas from this book in addition to forming a foundationof statistical thinking and methods. (1) Statistics is an applied field with a wide range of practical applications. (2) You don't have to be a math guru to learn from real, interesting data. (3) Data are messy, and statistical tools are imperfect. But, when you understand the strengths and weaknesses of these tools, you can use them to learn about the real world. Textbook overviewThe chapters of this book are as follows: 1. Data collection. Data structures, variables, and basic data collection techniques. 2. Summarizing data. Data summaries and graphics. 3. Probability. The basic principles of probability. 4. Distributions of random variables. Introduction to key distributions, and how the normal model applies to the sample mean and sample proportion. 5. Foundation for inference. General ideas for statistical inference in the context of estimating the population proportion. 6. Inference for categorical data. Inference for proportions using the normal and chisquare distributions. 7. Inference for numerical data. Inference for one or two sample means using the t distribution, and comparisons of many means using ANOVA. 8. Introduction to linear regression. An introduction to regression with two variables. Instructions are also provided in several sections for using Casio and TI calculators.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Textbooks Statistics
- Resource Type:
- e-book
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e-book
We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods. (1) Statistics is an applied field with a wide range of practical applications. (2) You don't have to be a math guru to learn from interesting, real data. (3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world. Textbook overview The chapters of this book are as follows: 1. Introduction to data. Data structures, variables, summaries, graphics, and basic data collection techniques. 2. Foundations for inference. Case studies are used to introduce the ideas of statistical inference with randomization and simulations. The content leads into the standard parametric framework, with techniques reinforced in the subsequent chapters.1 It is also possible to begin with this chapter and introduce tools from Chapter 1 as they are needed. 3. Inference for categorical data. Inference for proportions using the normal and chi-square distributions, as well as simulation and randomization techniques. 4. Inference for numerical data. Inference for one or two sample means using the t distribution, and also comparisons of many means using ANOVA. A special section for bootstrapping is provided at the end of the chapter. 5. Introduction to linear regression. An introduction to regression with two variables. Most of this chapter could be covered immediately after Chapter 1. 6. Multiple and logistic regression. An introduction to multiple regression and logistic regression for an accelerated course. Appendix A. Probability. An introduction to probability is provided as an optional reference. Exercises and additional probability content may be found in Chapter 2 of OpenIntro Statistics at openintro.org. Instructor feedback suggests that probability, if discussed, is best introduced at the very start or very end of the course.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Textbooks Statistics
- Resource Type:
- e-book