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The evolution of a Frank Gehry building, from planning and design and architect-client interaction to construction; with color illustrations throughout.
The Power of Stata Graphics at Your Fingertips Whether you are new to Stata graphics or a seasoned veteran, this book teaches you how to use Stata to make high-quality graphs that stand out and enhance statistical results. With over 900 illustrated examples and quick-reference tabs, it offers a guide to creating and customizing graphs for any type of statistical data using either Stata commands or the Graph Editor. The author displays each graph example in full color with simple and clear instructions. He shows how to produce various types of graph elements, including marker symbols, lines, legends, captions, titles, axis labels, and grid lines. Reflecting the new graphics features of Stata, this thoroughly updated and expanded edition contains a new chapter that explains how to exploit the power of the new Graph Editor. This edition also includes additional examples and illustrates nearly every example with the Graph Editor.
The Mata Book: A Book for Serious Programmers and Those Who Want to Be is the book that Stata programmers have been waiting for. Mata is a serious programming language for developing small- and large-scale projects and for adding features to Stata. What makes Mata serious is that it provides structures, classes, and pointers along with matrix capabilities. The book is serious in that it covers those advanced features, and teaches them. The reader is assumed to have programming experience, but only some programming experience. That experience could be with Stata's ado language, or with Python, Java, C++, Fortran, or other languages like them. As the book says, "being serious is a matter of attitude, not current skill level or knowledge". The author of the book is William Gould, who is also the designer and original programmer of Mata, of Stata, and who also happens to be the president of StataCorp.
Here, from Pulitzer Prize–winning critic Paul Goldberger, is the first full-fledged critical biography of Frank Gehry, undoubtedly the most famous architect of our time. Goldberger follows Gehry from his humble origins—the son of working-class Jewish immigrants in Toronto—to the heights of his extraordinary career. He explores Gehry’s relationship to Los Angeles, a city that welcomed outsider artists and profoundly shaped him in his formative years. He surveys the full range of his work, from the Bilbao Guggenheim to the Walt Disney Concert Hall in L.A. to the architect’s own home in Santa Monica, which galvanized his neighbors and astonished the world. He analyzes his carefully crafted persona, in which an amiable surface masks a driving ambition. And he discusses his use of technology, not just to change the way a building looks, but to revolutionize the very practice of the field. Comprehensive and incisive, Building Art is a sweeping view of a singular artist—and an essential story of architecture’s modern era.
An illustrated examination of laboratory architecture and the work that it does to engage the public, recruit scientists, and attract funding. The laboratory building is as significant to the twenty-first century as the cathedral was to the thirteenth and fourteenth centuries. The contemporary science laboratory is built at the grand scales of cathedrals and constitutes as significant an architectural statement. The laboratory is a serious investment in architectural expression in an attempt to persuade us of the value of the science that goes on inside. In this lavishly illustrated book, Sandra Kaji-O'Grady and Chris L. Smith explore the architecture of modern life science laboratories, and the work that it does to engage the public, recruit scientists, and attract funding. Looking at the varied designs of eleven important laboratories in North America, Europe, and Australia, all built between 2005 and 2019, Kaji-O'Grady and Smith examine the relationship between the design of contemporary laboratory buildings and the ideas and ideologies of science. Observing that every laboratory architect and client declares the same three aspirations—to eliminate boundaries, to communicate the benefits of its research programs, and to foster collaboration—Kaji-O'Grady and Smith organize their account according to the themes of boundaries, expression, and socialization. For instance, they point to the South Australian Health and Medical Institute's translucent envelope as the material equivalent of institutional accountability; the insistent animal imagery of the NavarraBioMed laboratory in Spain; and the Hillside Research Campus's mimicry of the picturesque fishing village that once occupied its site. Through these and their other examples, Kaji-O'Grady and Smith show how the architecture of the laboratory shapes the science that takes place within it.
First Published in 2005. Routledge is an imprint of Taylor & Francis, an informa company.
Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University
Making buildings and infrastructure more sustainable represents a key and global challenge. This presents the results of research from the Australia Co-operative Research Centre for Construction Innovation, covering four themes, applied to regeneration as well as to new build: Sustainable Materials and Manufactures; Virtual Design, Construction and Management; Integrating Design, Construction and Facility Management over the Project Life Cycle; and Integration across spatial scales.
This textbook integrates the teaching and learning of statistical concepts with the acquisition of the Stata (version 16) software package.
Biostatistics deals with making sense of data. While statistical inference is essential in our application of the research findings to clinical decision-making regarding the care of our patients, statistical inference without clinical relevance or importance can be very misleading and even meaningless. This textbook has attempted to deemphasize p value in the interpretation of clinical and biomedical data by stressing the importance of confidence intervals, which allow for the quantification of evidence. For example, a large study due to a large sample size that minimizes variability may show a statistically significant difference while in reality the difference is too insignificant to warrant any clinical relevance. Covers these relevant topics in biostatistics: Design Process, Sampling & Reality in Statistical Modeling Basics of Biostatistical Reasoning & Inference Central Tendency Theorem & Measures of Dispersion Most commonly used & abused parametric test - t test Most commonly used & abused non-parametric test - chi squared statistic Sample size and power estimations Logistic/Binomial Regression Models - Binary Outcomes Time-to-Event Data - Survival Analysis & Count Data - Poisson Regression ANOVA, ANCOVA - Mixed Effects Model (Fixed and Random), RANOVA, GEE Simple & Multiple Linear Regression Models Correlation Analysis (Pearson & Spearman Rank) Clinical & Statistical Significance - p value as a function of sample size Clinical and biomedical researchers often ignore an important aspect of evidence discovery from their funded or unfunded projects. Since the attempt is to illustrate some sets of relationships from the data set, researchers often do not exercise substantial amount of time in assessing the reliability and validity of the data to be utilized in the analysis. However, the expected inference or the conclusion to be drawn is based on the analysis of the un-assessed data. Reality in statistical modeling of biomedical and clinical research data remains the focus of scientific evidence discovery, and this book. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample and the randomization process when applicable prior to the selection of the analytic tool. In addition, this book stresses the importance of biologic and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is random sample, which had been perpetually addressed in this book. When studies are conducted without a random sample, except when disease registries/databases or consecutive subjects are utilized, as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value.