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Melodrama: Genre, Style and Sensibility is designed as an accessible overview of one of the most popular genres at undergraduate Film Studies. The book identifies three distinct but connected concepts through which it is possible to make sense of melodrama; either as a genre, originating in European theatre of the 18th and 19th century, as a specific cinematic style, epitomised by the work of Douglas Sirk or as a sensibility that emerges in the context of specific texts, speaking to and reflecting the desires, concerns and anxieties of audiences. Films discussed include All That Heaven Allows, Safe, Fear Eats the Soul, Black Narcissus, Suddenly Last Summer and Rebel Without a Cause. Each chapter includes overviews of key essays, analyses of significant and widely studied films and includes an annotated reading list.

• Sales Rank: #1078965 in Books
• Brand: Brand: Wallflower Press
• Published on: 2005-01-19
• Original language: English
• Number of items: 1
• Dimensions: 7.90" h x .39" w x 6.06" l, .52 pounds
• Binding: Paperback
• 144 pages

Features

• Used Book in Good Condition

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0 of 0 people found the following review helpful.
Five Stars
By hadar
Good book

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This book is intended as a textbook for a first-year Ph. D. course in mathematics for economists and as a reference for graduate students in economics. It provides a self-contained, rigorous treatment of most of the concepts and techniques required to follow the standard first-year theory sequence in micro and macroeconomics. The topics covered include an introduction to analysis in metric spaces, differential calculus, comparative statics, convexity, static optimization, dynamical systems and dynamic optimization. The book includes a large number of applications to standard economic models and over two hundred fully worked-out problems.

• Sales Rank: #453990 in Books
• Published on: 2000-01-28
• Original language: English
• Number of items: 1
• Dimensions: 9.96" h x 1.69" w x 6.97" l, 3.15 pounds
• Binding: Paperback
• 848 pages

Review
'The textbook is highly recommended to graduate students of economics. Furthermore, it provides a useful mathematical reference for researchers in economics.' Roland Fahrion, Zentralblatt MATH

'... an extensive introduction into the mathematic needed in the field of economics.' Simulation News Europe

About the Author
Universidad autonoma de barcelona

Most helpful customer reviews

27 of 29 people found the following review helpful.
Second Best Optimal
By D. W. MacKenzie
'Mathematical Methods' is the best math econ text you can buy. It does a far better job of explaining math modeling than Takayama or Simone and Blume. It reads better than Chiang. Its' broad coverage of techniques should be enough to satisfy most any instructor.
It starts off by running through some important basics- set theory, Venn diagrams, proofs. It then works up to calculus and optimization. It could use some more game theory, and should ditch the section on ISLM. The main strength of this book is that, unlike other math econ texts, one can read and understand it without prior knowledge of advanced mathmatics.
Of course, nobody really needs to learn all, or even most of, the math in this book. To get credentails as an economist, students must jump through many a mathematical hoop. This book helps students through this better than any of the alternatives. It has a reasonable paperback price too. Do not expect to have much fun reading 'Mathematical Methods'. Just bear in mind that there are far worse books to use in studying math econ.

23 of 26 people found the following review helpful.
A good overview
By Dr. Lee D. Carlson
Mathematical economics has been around for about 175 years, although as a discipline it has only been recognized for about five decades. Professional economists have had various levels of confidence in its validity and applicability, and mathematical economists have been criticized for the esoteric nature of the mathematics they deploy and some have been ostracized from academic departments for this very reason. This book emphasizes the mathematical tools, these being primarily the theory of optimization and dynamical systems, but the author does find time to discuss applications. Some of these could be classified as "classical" applications, but some are very contemporary in their scope and intersect the work done in financial engineering.

Part 1 of the book introduces the reader to the necessary background in real analysis, topology, differential calculus, and linear algebra. All of this mathematics is straightforward and can be found in many books.

In chapter 5, the author considers static economic models, which are described by collections of parametrized systems of equations. The equations are dependent on parameters describing the environment and `endogenous' variables. The goal is to find the values of the endogenous variables at equilibrium, and to find out if the equilibrium solutions are unique. In addition, it is interest to find out how the solution set changes when the parameters are changed. This is what the author calls `comparative statics'. Linear models are considered first, their analysis being amenable to the techniques of linear and multilinear algebra. The comparative statics for linear models is straightforward, with the shift in equilibrium as a parameter is change readily calculated. The comparative statics of nonlinear models involves the use of the implicit function theorem, and the author derives a formula for doing comparative statics in differentiable models. The discussion here, involving concepts such as transversality, critical points, regular values, and genericity, should be viewed as a warm-up to a more advanced treatment using differential topology.

The author studies static optimization in chapter 7, with the postulate of rationality assumed throughout. This allows the study of the behavior of economic agents to be reduced to a constrained optimization problem. The techniques of nonlinear programming are used to find solutions to the constrained optimization problem. Throughout this chapter one sees discussion of the ubiquitous `agent' who is embedded in a collection of possible environments, and is able to carry out a certain collection of actions.

The author finally gets to economic applications in chapter 8, wherein the author studies the behavior of a single agent under a set of restrictions imposed on it by its environment. This rather simplistic study is then generalized to the case of many interacting agents who are taken to be rational. The concept of `equilibrium', so entrenched in economic theory and economic modeling, makes its appearance here. In a condition of equilibrium, no agent has an incentive to change its behavior, and the actions of each individual are mutually compatible. Some of the usual concepts of equilibrium are discussed in the chapter, such as Walrasian equilibrium in exchange economies, and Nash equilibrium in game theory. The (subjective) preferences of consumers are modeled by binary relations and differentiable utility functions. The differentiability allows the techniques of chapter 7 to be used. The author asks the reader to work through some examples of `imperfect' competition at the end of the chapter.

After a straightforward review of dynamical systems in chapters 9 and 10, the author discusses applications of dynamical systems in chapter 11. He begins with a discussion of a dynamic IS-LM model, using assumptions on the evolution of the money supply, the formation of expectations, and price dynamics. This model consists of two first-order ordinary differential equations, and the author studies its fixed-point structure via a standard phase-space analysis. This analysis allows the author to study the effect of a change in parameters, such as change in the rate of money creation, i.e. the effects of a certain monetary policy. Also discussed are `perfect-foresight models', which address the difficult issue of boundary conditions in economic models based on dynamical systems. Two of these models are discussed, one is a stock price model based on the no-arbitrage principle from finance, and the other is a model of exchange-rate determination. The stock price model is the most interesting discussion in the book. It requires one to specify how expectations are formed, and, depending on how this is done, some very unexpected results occur. For example, if the agents have adaptive expectations, the author shows that the forecast error is predictable, and that agents who understand the structure of the model will have an incentive to deviate from the predicted behavior. This behavior on the part of the agents will invalidate the theory since the agents will have an incentive to compute the trajectory of prices, contrary to the assumption of the model. The author concludes that this is in direct conflict with the assumption that individuals are rational and maximize utility, i.e. that in a world without uncertainty, adaptive expectations are inconsistent with the assumption of rationality. The author avoids this problem by assuming that `perfect foresight' holds for the agents, i.e. the agents form expectations that are consistent with the structure of the model. He shows that the assumption of perfect foresight eliminates the inconsistency that was found in the adaptive expectations model. In the perfect foresight model, every agent uses the correct model to predict prices, and no agent has any incentive to act differently. The author then uses this model to study the response of share prices to a change in the tax rate on dividends. The rest of the chapter discusses neoclassical growth models and the software language Mathematica is introduced as a tool for solving nonlinear differential equations.

I did not read the last two chapters of the book, which cover dynamic optimization and its applications, and so I will omit their review.

0 of 0 people found the following review helpful.
Five Stars
By Fatima
Excellent

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• Sales Rank: #215921 in Books
• Published on: 2014-09-18
• Original language: English
• Number of items: 1
• Dimensions: 9.00" h x 1.00" w x 6.90" l, .0 pounds
• Binding: Paperback
• 384 pages

About the Author
Kerrie Meyler, System Center MVP, is the lead author of numerous System Center books in the Unleashed series, including System Center 2012 Configuration Manager Unleashed (2012), System Center Configuration Manager 2007 Unleashed (2009), System Center 2012 Operations Manager Unleashed (2013), System Center 2012 Orchestrator Unleashed (2013), and System Center 2012 Service Manager Unleashed (2014). She is an independent consultant with more than 17 years of Information Technology experience. Kerrie was responsible for evangelizing SMS while a Sr. Technology Specialist (TSP) at Microsoft. She was a member of the Management Insiders Group and has presented on System Center technologies at TechEd and MMS.

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Greg Ramsey, Enterprise and Client Management MVP, is the Enterprise Tools Strategist at Dell, Inc. He has a B.S. in Computer Sciences and Engineering from Ohio State University. Greg coauthored System Center Configuration Manager 2012 Unleashed (2012), Microsoft System Center 2012 Configuration Manager: Administration Cookbook (Packt, 2012), and System Center Configuration Manager 2007 Unleashed (2009). Greg is a cofounder of the Ohio SMS Users Group and the Central Texas Systems Management User Group.

Most helpful customer reviews

0 of 0 people found the following review helpful.
Awesome book
By Amazon Customer
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1 of 1 people found the following review helpful.
this is still an excellent guide to get to know all the many parts ...
By Jonathan Allen
This is out of date, with R2 SP1 out now, but still a very handy resource. The foundation has stayed mostly the same, this is still an excellent guide to get to know all the many parts of SCCM.

1 of 2 people found the following review helpful.
Too much fluff but still useful.
By Wizard
Too much fluff but still useful.

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The Finite Element Method: Basic Concepts and Applications

• Published on: 2005
• Original language: English
• Dimensions: .79 pounds
• Binding: Paperback

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This introductory textbook presents a variety of approaches and perspectives that can be employed to analyze any sample of discourse. The perspectives come from multiple disciplines, including linguistics, sociolinguistics, and linguistic anthropology, all of which shed light on meaning and the interactional construction of meaning through language use. Students without prior experience in discourse analysis will appreciate and understand the micro-macro relationship of language use in everyday contexts, in professional and academic settings, in languages other than English, and in a wide variety of media outlets.

Each chapter is supported by examples of spoken and written discourse from various types of data sources, including conversations, commercials, university lectures, textbooks, print ads, and blogs, and concludes with hands-on opportunities for readers to actually do discourse analysis on their own. Students can also utilize the book’s comprehensive companion website, with flash cards for key terms, quizzes, and additional data samples, for in-class activities and self-study.

With its accessible multi-disciplinary approach and comprehensive data samples from a variety of sources, Discourse Analysis is the ideal core text for the discourse analysis course in applied linguistics, English, education, and communication programs.

• Sales Rank: #734981 in Books
• Published on: 2013-12-22
• Original language: English
• Number of items: 1
• Dimensions: 8.90" h x 1.00" w x 5.90" l, .0 pounds
• Binding: Paperback
• 424 pages

Review

Strauss and Feiz break new ground by combining key elements of a cognitive perspective towards language with established DA traditions. The book is full of naturally occurring text from diverse sources, including blogs and e-texts, providing the reader many opportunities for carefully-crafted, self-guided DA. This is an innovative, thoughtful contribution to discourse studies which promises to prove an invaluable resource – an engaging, highly informative introduction to multiple approaches to discourse analysis. – Andrea Tyler, Professor of Linguistics, Georgetown University, USA

This volume offers an extraordinary sweep of the field of discourse analysis in highly accessible terms. In crystal clear, sophisticated fashion the authors ground fundamental concepts in a rich corpus of discourse drawn from vastly different languages. Discourse analysis comes alive as readers plunge into how knowledge, truth, emotions, relationships, power, and resistance are cast through talk and texts. – Elinor Ochs, Distinguished Professor of Anthropology, University of California Los Angeles, USA

About the Author

Susan Strauss is impassioned by language, literacy, imagery, and discourse. She has studied Spanish, French, Hebrew, Japanese, Korean, Persian, Amharic, German, Yiddish, and Quechua, and is fascinated by the ways in which these various systems of language combine to express tense and aspect, number, gender, shape, color, sensory perception, stance, opinion, affect, and emotion.

She teaches courses at Penn State in Discourse Analysis, Discourse Functional Grammar (English and other languages), Linguistic Anthropology, Second Language Writing, and Pedagogical Approaches to L2 Speaking/Listening.

Since 2012, she has been the mother of six school-aged children, all adopted from Ethiopia. Together they have been building foundations of literacy as "social cognition," foundations through which words and worlds connect to form new understandings about people and freedom, education and respect; about science, social studies, history, literature, and art.

She is currently working on a new book with Parastou Feiz, Cal State San Bernardino, and Xuehua Xiang, University of Illinois at Chicago, also to be published by Routledge, on the topic of discourse functional grammar. She is also authoring a book on her adoption story, detailing the institutional challenges of the adoption process itself and sharing the absolute joy, wonder, and awe of being a mother to six utterly amazing children.

Parastou Feiz is Associate Professor in the Department of English at California State University - San Bernardino.

Most helpful customer reviews

4 of 4 people found the following review helpful.
Excellent Textbook
By Applied Linguistics Professor
I recently used Strauss and Feiz (2014) in my discourse analysis class and enjoyed using it. What I liked the most about the book is its richly exemplified inductive approach, which worked well for my purpose of introducing students to discourse analysis both as an interdisciplinary field and as a research methodology/analytic lens. The book is also helpful with its coherent emphasis on the inseparability of meaning and form, and the indispensability of stance and perspective-taking in approaching discourse. With these themes coherently constructed in the book, students not only come to "know" about discourse analysis, but begin to view language beyond the surface, to analyze data, and to become intrigued in what discourse analysis can do and can discover.

The writing is thorough, insightful and relatable (with data excepts from children's books, radio news casts, extreme weather reports, pizza recipes and other such everyday, lively language occurrences).

Another major plus for this book is its organization and sequencing. This is the first book I have used without having to re-arrange its chapters to create better overall coherence, or skip chapters due to the bulk of materials. When teaching with this book, if one chapter doesn't get finished in its planned time, the discussions easily merge with the subsequent chapter.

The book comes with a very good set of suggested readings and a solid bibliography. A good portion of the article-length readings that I included for my students to read were selected from the "Suggestions for Further Reading" section of each chapter, which I found to be a carefully chosen collection that extend the discussion of the chapter or otherwise provide exemplary illustration of the use of DA to study diverse language phenomena.

1 of 1 people found the following review helpful.
Great book!
By Jocelyn
I bought this book for a graduate level class I took last year. It's easy to read and the exercises are all very interesting. LOVE IT!

2 of 3 people found the following review helpful.
... is very clear to explain many definitions accompained with good examples, amd exercises
By Tamara
It is very clear to explain many definitions accompained with good examples, amd exercises.

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INTRODUCTORY APPLIED BIOSTATISTICS provides a solid and engaging background for students learning to apply and appropriately interpret statistical applications in the medical and public health fields. The many examples drawn directly from the authors' remarkable clinical experiences with applied biostatistics make this text relevant, practical, and interesting for students. This flexible textbook encourages students to master application techniques by hand before moving on to computer applications, with SAS programming code and output for each technique covered in every chapter. The majority of the textbook addresses methods for statistical inference, including one- and two-sample tests for means and proportions, analysis of variance techniques, correlation, and regression analysis. For each topic, the book addresses methodology, including assumptions, statistical formulas, and appropriate interpretation of results.

• Sales Rank: #178783 in Books
• Published on: 2005-03-16
• Original language: English
• Number of items: 1
• Dimensions: 9.48" h x 1.19" w x 7.46" l, 2.56 pounds
• Binding: Hardcover
• 672 pages

Review
1. INTRODUCTION. 2. MOTIVATION. Introduction. Vocabulary. Population Parameters. Sampling and Sample Statistics. Statistical Inference. 3. SUMMARIZING DATA. Introduction. Background. Descriptive Statistics and Graphical Methods. Key Formulas. Statistical Computing. Problems. 4. PROBABILITY. Introduction. Background. First Principles. Combinations and Permutations. The Binomial Distribution. The Normal Distribution. Key Formulas. Applications Using SAS. Problems. 5. SAMPLING DISTRIBUTIONS. Introduction. Background. The Central Limit Theorem. Key Formulas. Applications Using SAS. Problems. 6. STATISTICAL INFERENCE: PROCEDURES FOR . Introduction. Estimating . Testing Hypotheses Concerning . Key Formulas. Statistical Computing. Problems. 7. STATISTICAL INFERENCE: PROCEDURES FOR ( 1- 2) Introduction. Statistical Inference Concerning ( 1- 2). Power and Samples Size Determination. Key Formulas. Statistical Computing. Problems. 8. CATEGORICAL DATA. Introduction. Statistical Inference Concerning p. Cross-tabulation Tables. Diagnostic Tests: Sensitivity and Specificity. Statistical Inference Concerning (p1-p2). Chi-Square Tests. Precision, Power and Sample Size Determination. Key Formulas. Statistical Computing. Problems. 9. COMPARING RISKS IN TWO POPULATIONS. Introduction. Effect Measures. Confidence Intervals for Effect Measures. The Chi-Square Test of Homogeneity. Fisher's Exact Test. Cox-Mantel-Haenzel Method. Precision, Power and Sample Size Determination. Key Formulas. Statistical Computing. Problems. 10. ANALYSIS OF VARIANCE. Introduction. Background Logic. Notation and Examples. Fixed vs. Random Effects Models. Evaluating Treatment Effects. Multiple Comparisons. Repeated Measures Analysis of Variance. Key Formulas. Statistical Computing. Problems. 11. CORRELATION AND REGRESSION. Introduction. Correlation Analysis. Simple Linear Regression. Multiple Regression Analysis. Logistic Regression Analysis. Key Formulas. Statistical Computing. Problems. 12. LOGISTIC REGRESSION ANALYSIS. Introduction. The Logistic Model. Statistical Inference for Simple Logistic Regression. Multiple Logistic Regression. ROC Area. Key Formulas. Statistical Computing. Problems. 13. NONPARAMETRIC TESTS. Introduction. The Sign Test (Two Dependent Samples Test). The Wilcoxon Signed-Rank Test (Two Dependent Samples). The Wilcoxon Rank Sum Test (Two Independent Samples). The Kruskal-Wallis Test (k Independent Samples). Spearman Correlation (Correlation between Variables). Key Formulas. Statistical Computing. Problems. 14. INTRODUCTION TO SURVIVAL ANALYSIS. Introduction. Incomplete Follow-Up. Time to Event. Survival Analysis Techniques. Appendix A: Introduction to Statistical Computing Using SAS. Introduction to SAS. The Data Step. Appendix B. Statistical Tables. Statistical Tables. SAS Programs used to generate table entries.

About the Author
Ralph D'Agostino, Sr. is Professor of Mathematics, Statistics, and Public Health at Boston University. He is a respected and widely published statistician with over 30 years of experience in running clinical trials and epidemiological research. He is a Senior Editor of Statistics in Medicine, Associate Editor of the American Journal of Epidemiology, on the editorial board of the Journal of Hypertension, and Fellow of the American Statistical Association and Epidemiologic Section of the American Heart Association. He is Co-Principal Investigator and Director of Data Management and Statistical Analysis for the Framingham Heart Study (a study collecting data on three generations that has been establishing the relation of risk factors that contribute to cardiovascular disease for over 50 years). He is also the Executive Director of Biometrics and Data Management for the Harvard Clinical Research Institute. He has been a consultant to the Food and Drug Administration since 1974, and has served on a number of drug and devices advisory committees. His interests are in biostatistical methods, robust procedures, longitudinal data analysis and multivariate data analysis. Dr. D'Agostino has received numerous awards, including the Food and Drug Administration Commissioner's Special Citation in 1981 and 1995.). He is co-author of five books in various fields of statistical methodology.

Lisa Sullivan is an Associate Professor of Biostatistics at the School of Public Health, Associate Professor of Mathematics and Statistics at Boston University, and Assistant Dean for Undergraduate Education in Public Health at Boston University. She received both her M.A. and Ph.D. from Boston University. She has won numerous awards for excellence in teaching and her research interests include applied biostatistics, longitudinal data analysis, design and analysis of clinical trials, and hierarchical modeling. She spends the majority of her time in the Boston University Statistics and Consulting Unit working on the Framingham Heart Study. Her recent research has focused on developing health risk appraisal functions to quantify individuals' risks of developing cardiovascular disease. She has published dozens of articles in prestigious periodicals such as the JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, NEW ENGLAND JOURNAL OF MEDICINE, and STATISTICS IN MEDICINE. Outside of work, Lisa enjoys running and cooking.

Alexa Beiser is Professor of Biostatistics in the School of Public Health at Boston University. She received her M.A. from University of California at San Diego, and her Ph.D. from Boston University. Her research interests include clinical trials methodology, statistical computing, and survival analysis. Dr. Beiser joined the Framingham Study in 1994 after spending many years collaborating on a variety of pediatric research projects. She is primarily involved in the investigation of risk factors for stroke, dementia, and Alzheimer's Disease using data collected as part of the Framingham Study. Dr. Beiser's foremost methodological interest is in estimation of lifetime risk of disease. Dr. Beiser has published articles in the NEW ENGLAND JOURNAL OF MEDICINE, the JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, STATISTICS IN MEDICINE, STROKE and NEUROLOGY. She enjoys reading, traveling and spending time with her four children.

Most helpful customer reviews

40 of 42 people found the following review helpful.
great introductory book covering all the key topics
By Michael R. Chernick
In contrast to the first reviewer I loved this book! It should be pointed that I am reviewing this first official edition in hard cover with a CD-ROM while the first reviewer reviewed a preliminary edition in paperback with no CD and published in 2005 whereas this text has a 2006 copyright date.
The authors are all statistics professors at Boston University with Ralph D'Agostino Sr. the most well-known. They have a great deal of experience as teachers researchers and consultants and it shows in the material and the clarity of presentation. Also to their credit, they provide numerous worked out examples in the text and plenty of exericises at the end of the chapters that can be given as homework problems. The other reviewer complained about not having any answers given in the back of the book. I question how valuable that would be. The in-text exercises and the clear exposition in the text , along with an instructor, should be enough for the students to gain enough confidence to try the homework problems with some confidence that they are doing them right! Also the instructor should have a solutions manual to work from or should put one together to go over the homework in class and/or in tutorial sections. Sometime students can waste a lot of time trying to force their solution to match the answer in the back of the book. What if there is a error or typo in the book's answer?

I like the way the authors present the material and the fact that they illustrate the methodology with real examples and SAS software. Many of the analyses are done using the Framingham Heart Study data which is a well designed cohort study with thus far a long 50 year follow-up on the surviving original cohorts. We benefit from the authors' intimate knowledge of this data snd their ability to illustrate a variety of biostatistical methods using it. There is also an interesting follow-up study that was conducted with the offspring of the original Framington cohorts that is discussed in the book.

The book starts with a motivating chapter. I did a similar thing in my introductory book for health science majors. Students in the health sciences are not very interested in math and may have a preconceived notion that statistics is boring mathematics and not relevant to their work. These ideas are very much off base and a good introductory chapter can dispell these notions. The next three chapters are standard to almost any first statistics course starting with ways to summarize data for description. This includes a section on statistical computing snd an introduction to the Framingham study and its data.

Next an introductory chapter on probability is given that includes the basics and some important combinatorial ideas needed to understand the binomial distribution. Then the binomial and normal distributions are presented. This chapter and all subsequent chapters have a section on statistical computing using SAS. Appendix A provides the basic components of SAS that are needed to run analyses in SAS along with sample code and sample output.

Next Chapter 4, as part of a logical progression, covers sampling distributions and the central limit theorem. Chapter 5 covers inference for a single sample mean and includes discussion of power and precision in the important practical problem of deciding how many subjects are needed to draw proper inferences from the trial. This deserves mention because many elementary statistics books avoid this topic and yet it is always one of the first questions a statistician is faced with when he is designing a clinical trial.

Chapter 6 does essentially the same thing as 5 but for two sample problems where the difference of two means is often the key parameter. In the two sample problem there is the added complication of whether or not the two population variances are equal and/or the two sample sizes are equal. Methods are available for all of these situations. Also matched pair designs involve two correlated populations of equal size and can often improve precision over designs that use independent populations to compute the mean difference. Such analyses often come up in clinical trials when baseline and final values are compared for the same subject and for pre-intervention and post-intervention tests in educational trianing studies. Again power and precision the SAS procedures and the analysis of the Framingham Heart Study are given.

The remaining chapters are Categorical Data (Chapter 7) Comparing risks in two populations (Chapter 8), Analysis of Variance (Chapter 9), Correlation and Regression (Chapter 10), Logistic Regression (Chapter 11), Nonparametric Tests (Chapter 12), and Introduction to Survival Analysis (Chapter 13). These are important chapters commonly included in introductory biostatistics texts but chapters such as 8, 10 and 12 are often not included in a general introductory statistics course.

In addition to Appendix A on SAS, Appendix B provides the traditional statistical tables but also shows why the are no longer really necessary since all these table entries can be generated using software. In this case the authors demonstrate it with SAS. And finally, Appendix C provides additional information and data documentation on the Framingham Heart Study.

This text would make a great course text for medical students and health science majors and could even be used as a first biostatistics course for either masters or PhD level statistics students who are interested in biostatistics. It also makes a good reference book for statisticians and biostatisticians.

0 of 0 people found the following review helpful.
Five Stars
By Heath
Great condition

9 of 9 people found the following review helpful.
Introductory Applied Biostatistics
By Tony
This is a great textbook for an introduction to biostatistics. My "required" textbook for my Introduction to Biostatistical Methods was way to technical for me to understand. I got this book to translate and really learn the basics (I have little mathmatics background). Its geared to those who may not have had any statistics backgrounds and easy to read.

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