Advertisement
| uconn statistics courses: Introduction to Probability Joseph K. Blitzstein, Jessica Hwang, 2014-07-24 Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment. |
| uconn statistics courses: Minitab Manual Vukov, 2002-02-20 Integrates the statistical computing package MINITAB(tm) into an Introductory Statistics course, using Statistics by McClave/Sincich, 9/e. |
| uconn statistics courses: Introductory Statistics 2e Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| uconn statistics courses: Models for Intensive Longitudinal Data Theodore A. Walls, Joseph L. Schafer, 2006-01-19 Rapid technological advances in devices used for data collection have led to the emergence of a new class of longitudinal data: intensive longitudinal data (ILD). Behavioral scientific studies now frequently utilize handheld computers, beepers, web interfaces, and other technological tools for collecting many more data points over time than previously possible. Other protocols, such as those used in fMRI and monitoring of public safety, also produce ILD, hence the statistical models in this volume are applicable to a range of data. The volume features state-of-the-art statistical modeling strategies developed by leading statisticians and methodologists working on ILD in conjunction with behavioral scientists. Chapters present applications from across the behavioral and health sciences, including coverage of substantive topics such as stress, smoking cessation, alcohol use, traffic patterns, educational performance and intimacy. Models for Intensive Longitudinal Data (MILD) is designed for those who want to learn about advanced statistical models for intensive longitudinal data and for those with an interest in selecting and applying a given model. The chapters highlight issues of general concern in modeling these kinds of data, such as a focus on regulatory systems, issues of curve registration, variable frequency and spacing of measurements, complex multivariate patterns of change, and multiple independent series. The extraordinary breadth of coverage makes this an indispensable reference for principal investigators designing new studies that will introduce ILD, applied statisticians working on related models, and methodologists, graduate students, and applied analysts working in a range of fields. A companion Web site at www.oup.com/us/MILD contains program examples and documentation. |
| uconn statistics courses: Introductory Business Statistics 2e Alexander Holmes, Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| uconn statistics courses: Data Mining in Finance Boris Kovalerchuk, Evgenii Vityaev, 2005-12-11 Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics. |
| uconn statistics courses: War of a Thousand Deserts Brian DeLay, 2008-11-01 In the early 1830s, after decades of relative peace, northern Mexicans and the Indians whom they called the barbarians descended into a terrifying cycle of violence. For the next fifteen years, owing in part to changes unleashed by American expansion, Indian warriors launched devastating attacks across ten Mexican states. Raids and counter-raids claimed thousands of lives, ruined much of northern Mexico's economy, depopulated its countryside, and left man-made deserts in place of thriving settlements. Just as important, this vast interethnic war informed and emboldened U.S. arguments in favor of seizing Mexican territory while leaving northern Mexicans too divided, exhausted, and distracted to resist the American invasion and subsequent occupation. Exploring Mexican, American, and Indian sources ranging from diplomatic correspondence and congressional debates to captivity narratives and plains Indians' pictorial calendars, War of a Thousand Deserts recovers the surprising and previously unrecognized ways in which economic, cultural, and political developments within native communities affected nineteenth-century nation-states. In the process this ambitious book offers a rich and often harrowing new narrative of the era when the United States seized half of Mexico's national territory. |
| uconn statistics courses: Mathematical Foundations of Infinite-Dimensional Statistical Models Evarist Giné, Richard Nickl, 2021-03-25 In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics. |
| uconn statistics courses: Symbolic Data Analysis Lynne Billard, Edwin Diday, 2012-05-14 With the advent of computers, very large datasets have become routine. Standard statistical methods don’t have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal structure, which must be taken into account in any analysis. This text presents a unified account of symbolic data, how they arise, and how they are structured. The reader is introduced to symbolic analytic methods described in the consistent statistical framework required to carry out such a summary and subsequent analysis. Presents a detailed overview of the methods and applications of symbolic data analysis. Includes numerous real examples, taken from a variety of application areas, ranging from health and social sciences, to economics and computing. Features exercises at the end of each chapter, enabling the reader to develop their understanding of the theory. Provides a supplementary website featuring links to download the SODAS software developed exclusively for symbolic data analysis, data sets, and further material. Primarily aimed at statisticians and data analysts, Symbolic Data Analysis is also ideal for scientists working on problems involving large volumes of data from a range of disciplines, including computer science, health and the social sciences. There is also much of use to graduate students of statistical data analysis courses. |
| uconn statistics courses: American Jewish Year Book 2020 Arnold Dashefsky, Ira M. Sheskin, 2022-01-01 The American Jewish Year Book, which spans three different centuries, is the annual record of the North American Jewish communities and provides insight into their major trends. Part I of the current volume contains the lead article: Chapter 1, “Pastrami, Verklempt, and Tshoot-spa: Non-Jews’ Use of Jewish Language in the US” by Sarah Bunin Benor. Following this chapter are three on domestic and international events, which analyze the year’s events as they affect American Jewish communal and political affairs. Three chapters analyze the demography and geography of the US, Canada, and world Jewish populations. Part II provides lists of Jewish institutions, including federations, community centers, social service agencies, national organizations, synagogues, Hillels, camps, museums, and Israeli consulates. The final chapters present national and local Jewish periodicals and broadcast media; academic resources, including Jewish Studies programs, books, journals, articles, websites, and research libraries; and lists of major events in the past year, Jewish honorees, and obituaries. While written mostly by academics, this volume conveys an accessible style, making it of interest to public officials, professional and lay leaders in the Jewish community, as well as the general public and academic researchers. The American Jewish Year Book has been a key resource for social scientists exploring comparative and historical data on Jewish population patterns. No less important, the Year Book serves organization leaders and policy makers as the source for valuable data on Jewish communities and as a basis for planning. Serious evidence-based articles regularly appear in the Year Book that focus on analyses and reviews of critical issues facing American Jews and their communities which are indispensable for scholars and community leaders. Calvin Goldscheider, Professor Emeritus of Sociology and Ungerleider Professor Emeritus of Judaic Studies, Brown University They have done it again. The American Jewish Year Book has produced yet another edition to add to its distinguished tradition of providing facts, figures and analyses of contemporary life in North America. Its well-researched and easily accessible essays offer the most up to date scrutiny of topics and challenges of importance to American Jewish life; to the American scene of which it is a part and to world Jewry. Whether one is an academic or professional member of the Jewish community (or just an interested reader of all things Jewish), there is not another more impressive and informative reading than the American Jewish Year Book. Debra Renee Kaufman, Professor Emerita and Matthews Distinguished University Professor, Northeastern University |
| uconn statistics courses: Approval of Law Schools American Bar Association, 1973 |
| uconn statistics courses: Parameter Estimation and Inverse Problems Richard C. Aster, Brian Borchers, Clifford H. Thurber, 2018-10-16 Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of Laplacian smoothing, an expansion of basis function exercises, the addition of stochastic descent, an improved presentation of Fourier methods and exercises, and more. - Features examples that are illustrated with simple, easy to follow problems that illuminate the details of a particular numerical method - Includes an online instructor's guide that helps professors teach and customize exercises and select homework problems - Covers updated information on adjoint methods that are presented in an accessible manner |
| uconn statistics courses: Bird At the Buzzer Jeff Goldberg, 2011-03-01 On March 6, 2001, the top two women’s college basketball teams in the nation, UConn and Notre Dame, played what was arguably the greatest game in the history of the sport. When UConn’s Sue Bird hit a twelve-foot pull-up jumper at the buzzer over national player of the year Ruth Riley in the Big East Tournament championship game, it marked the end of an epic contest that featured five future Olympians and eight first-round WNBA selections. Bird at the Buzzer re-creates this unique season with a detailed account of the games that led up to—and beyond—the tournament finale; profiles of the two coaches, UConn’s Geno Auriemma and Notre Dame’s Muffet McGraw; close-ups of the players who made the year so memorable; and, finally, an in-depth recap of the game worthy of being designated ESPN’s first-ever women’s basketball “Instant Classic.” Author Jeff Goldberg shows us the drama on the court and behind the scenes as the big game pitted Riley and the upstarts from Notre Dame against what many believed was the most talented team in UConn history, under Hall of Fame coach Auriemma. A see-saw affair in which neither team led by more than eight points, the 2001 Big East championship game encapsulates the quintessential inside story of the individual talents and skills, team spirit and smarts, and the moment-by-moment realities of college athletics that made this season a snapshot of sports at its finest. |
| uconn statistics courses: Statistics in the 21st Century Adrian E. Raftery, Martin A. Tanner, Martin T. Wells, 2001-07-09 This volume discusses an important area of statistics and highlights the most important statistical advances. It is divided into four sections: statistics in the life and medical sciences, business and social science, the physical sciences and engineering, and theory and methods of statistics. |
| uconn statistics courses: Bayesian Item Response Modeling Jean-Paul Fox, 2010-05-19 The modeling of item response data is governed by item response theory, also referred to as modern test theory. The eld of inquiry of item response theory has become very large and shows the enormous progress that has been made. The mainstream literature is focused on frequentist statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly for making further - provements in the statistical modeling process. The Bayesian approach has two important features that make it attractive for modeling item response data. First, it enables the possibility of incorpor- ing nondata information beyond the observed responses into the analysis. The Bayesian methodology is also very clear about how additional information can be used. Second, the Bayesian approach comes with powerful simulation-based estimation methods. These methods make it possible to handle all kinds of priors and data-generating models. One of my motives for writing this book is to give an introduction to the Bayesian methodology for modeling and analyzing item response data. A Bayesian counterpart is presented to the many popular item response theory books (e.g., Baker and Kim 2004; De Boeck and Wilson, 2004; Hambleton and Swaminathan, 1985; van der Linden and Hambleton, 1997) that are mainly or completely focused on frequentist methods. The usefulness of the Bayesian methodology is illustrated by discussing and applying a range of Bayesian item response models. |
| uconn statistics courses: Introductory Geographic Information Systems John R. Jensen, Ryan R. Jensen, 2013 Geospatial technologies in general - and Geographic Information Systems (GIS) in particular - are becoming increasingly important in our society. GIS technology is used to identify the optimal routes for emergency vehicles, to determine the best locations for various businesses, schools, and facilities, to monitor the growth and expansion of urban areas as a way to manage natural resources, and much more. Principles of Geographic Information Systems by John Jensen and Ryan Jensen is an ideal introduction for those who know very little about geographic information systems and spatial analysis. Relatively complex GIS principles are introduced in basic terms, often using graphics to communicate principles rather than complex mathematical equations. Content is not geared toward any single commercial GIS software program, and the book's timely, practical examples and extensive visual format appeal to today's students. This text can be used at the undergraduate or graduate level in one or two semester courses in Introductory and Intermediate GIS, yet can also be useful for professionals looking to increase their knowledge in this subject area. Note: If you are purchasing the standalone text or electronic version, mygeoscienceplace does not come automatically packaged with the text. To purchase mygeoscienceplace, please visit www.mygeoscienceplace.com. |
| uconn statistics courses: A First Course in Linear Model Theory Nalini Ravishanker, Zhiyi Chi, Dipak K. Dey, 2021-10-18 Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the authors introduce to students the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models. In addition to adding R functionality, this second edition features three new chapters and several sections on new topics that are extremely relevant to the current research in statistical methodology. Revised or expanded topics include linear fixed, random and mixed effects models, generalized linear models, Bayesian and hierarchical linear models, model selection, multiple comparisons, and regularized and robust regression. New to the Second Edition: Coverage of inference for linear models has been expanded into two chapters. Expanded coverage of multiple comparisons, random and mixed effects models, model selection, and missing data. A new chapter on generalized linear models (Chapter 12). A new section on multivariate linear models in Chapter 13, and expanded coverage of the Bayesian linear models and longitudinal models. A new section on regularized regression in Chapter 14. Detailed data illustrations using R. The authors' fresh approach, methodical presentation, wealth of examples, use of R, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models. |
| uconn statistics courses: Instrument Development in the Affective Domain D. Betsy McCoach, Robert K. Gable, John P. Madura, 2013-05-09 Whether the concept being studied is job satisfaction, self-efficacy, or student motivation, values and attitudes--affective characteristics--provide crucial keys to how individuals think, learn, and behave. And not surprisingly, as measurement of these traits gains importance in the academic and corporate worlds, there is an ongoing need for valid, scientifically sound instruments. For those involved in creating self-report measures, the completely updated Third Edition of Instrument Development in the Affective Domain balances the art and science of instrument development and evaluation, covering both its conceptual and technical aspects. The book is written to be accessible with the minimum of statistical background, and reviews affective constructs from a measurement standpoint. Examples are drawn from academic and business settings for insights into design as well as the relevance of affective measures to educational and corporate testing. This systematic analysis of all phases of the design process includes: Measurement, scaling, and item-writing techniques. Validity issues: collecting evidence based on instrument content. Testing the internal structure of an instrument: exploratory and confirmatory factor analyses. Measurement invariance and other advanced methods for examining internal structure. Strengthening the validity argument: relationships to external variables. Addressing reliability issues. As a graduate course between covers and an invaluable professional tool, the Third Edition of Instrument Design in the Affective Domain will be hailed as a bedrock resource by researchers and students in psychology, education, and the social sciences, as well as human resource professionals in the corporate world. |
| uconn statistics courses: Strength in Numbers: The Rising of Academic Statistics Departments in the U. S. Alan Agresti, Xiao-Li Meng, 2014-12-16 Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history. |
| uconn statistics courses: Teaching Statistics Andrew Gelman, Deborah Nolan, 2002-08-08 Students in the sciences, economics, psychology, social sciences, and medicine take introductory statistics. Statistics is increasingly offered at the high school level as well. However, statistics can be notoriously difficult to teach as it is seen by many students as difficult and boring, if not irrelevant to their subject of choice. To help dispel these misconceptions, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, examples and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and combines chapters such as, 'First week of class', with exercises to break the ice and get students talking; then 'Descriptive statistics' , collecting and displaying data; then follows the traditional topics - linear regression, data collection, probability and inference. Part II gives tips on what does and what doesn't work in class: how to set up effective demonstrations and examples, how to encourage students to participate in class and work effectively in group projects. A sample course plan is provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics and sampling. |
| uconn statistics courses: Biological Collections National Academies of Sciences, Engineering, and Medicine, Division on Earth and Life Studies, Board on Life Sciences, Committee on Biological Collections: Their Past, Present, and Future Contributions and Options for Sustaining Them, 2020-12-29 Biological collections are a critical part of the nation's science and innovation infrastructure and a fundamental resource for understanding the natural world. Biological collections underpin basic science discoveries as well as deepen our understanding of many challenges such as global change, biodiversity loss, sustainable food production, ecosystem conservation, and improving human health and security. They are important resources for education, both in formal training for the science and technology workforce, and in informal learning through schools, citizen science programs, and adult learning. However, the sustainability of biological collections is under threat. Without enhanced strategic leadership and investments in their infrastructure and growth many biological collections could be lost. Biological Collections: Ensuring Critical Research and Education for the 21st Century recommends approaches for biological collections to develop long-term financial sustainability, advance digitization, recruit and support a diverse workforce, and upgrade and maintain a robust physical infrastructure in order to continue serving science and society. The aim of the report is to stimulate a national discussion regarding the goals and strategies needed to ensure that U.S. biological collections not only thrive but continue to grow throughout the 21st century and beyond. |
| uconn statistics courses: An Introduction to Conservation Biology Anna Sher, 2022 An Introduction to Conservation Biology is well suited for a wide range of undergraduate courses, as both a primary text for conservation biology courses and a supplement for ecological and environmental science courses. This new edition focuses on engaging students through videos and activities, and includes new pedagogy to scaffold students' learning. Coverage of recent conservation biology events in the news-such as global climate change and sustainable development-keeps the content fresh and current-- |
| uconn statistics courses: Statistics Made Simple for School Leaders Susan Rovezzi Carroll, David J. Carroll, 2022-02-15 This book is a practical handbook for school leaders who want to use data to help understand their students, their staff and their communities, day to day. |
| uconn statistics courses: Mind on Statistics Jessica M. Utts, R. F. Heckard, 2004 AUTOMATICALLY PACKAGED WITH EVERY NEW COPY OF THE BOOK AND NOT AVAILABLE SEPARATELY. |
| uconn statistics courses: Multilevel Modeling of Educational Data Ann A. O'Connell, D. Betsy McCoach, 2008-04-01 (sponsored by the Educational Statisticians, SIG) Multilevel Modeling of Educational Data, co-edited by Ann A. O’Connell, Ed.D., and D. Betsy McCoach, Ph.D., is the next volume in the series: Quantitative Methods in Education and the Behavioral Sciences: Issues, Research and Teaching (Information Age Publishing), sponsored by the Educational Statisticians' Special Interest Group (Ed-Stat SIG) of the American Educational Research Association. The use of multilevel analyses to examine effects of groups or contexts on individual outcomes has burgeoned over the past few decades. Multilevel modeling techniques allow educational researchers to more appropriately model data that occur within multiple hierarchies (i.e.- the classroom, the school, and/or the district). Examples of multilevel research problems involving schools include establishing trajectories of academic achievement for children within diverse classrooms or schools or studying school-level characteristics on the incidence of bullying. Multilevel models provide an improvement over traditional single-level approaches to working with clustered or hierarchical data; however, multilevel data present complex and interesting methodological challenges for the applied education research community. In keeping with the pedagogical focus for this book series, the papers this volume emphasize applications of multilevel models using educational data, with chapter topics ranging from basic to advanced. This book represents a comprehensive and instructional resource text on multilevel modeling for quantitative researchers who plan to use multilevel techniques in their work, as well as for professors and students of quantitative methods courses focusing on multilevel analysis. Through the contributions of experienced researchers and teachers of multilevel modeling, this volume provides an accessible and practical treatment of methods appropriate for use in a first and/or second course in multilevel analysis. A supporting website links chapter examples to actual data, creating an opportunity for readers to reinforce their knowledge through hands-on data analysis. This book serves as a guide for designing multilevel studies and applying multilevel modeling techniques in educational and behavioral research, thus contributing to a better understanding of and solution for the challenges posed by multilevel systems and data. |
| uconn statistics courses: Introduction to Spatial Econometrics James LeSage, Robert Kelley Pace, 2023-01-09 Introduction to Spatial Econometrics presents a variety of regression methods for analyzing spatial data samples that violate the traditional assumption of independence between observations. It explores a range of alternative topics, including maximum likelihood and Bayesian estimation, various types of spatial regression specif |
| uconn statistics courses: Introduction to Statistics Howard M. Reid, 2013-08-13 Using a truly accessible and reader-friendly approach, Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis, by Howard M. Reid, redefines the way statistics can be taught and learned. Unlike other books that merely focus on procedures, Reid’s approach balances development of critical thinking skills with application of those skills to contemporary statistical analysis. He goes beyond simply presenting techniques by focusing on the key concepts readers need to master in order to ensure their long-term success. Indeed, this exciting new book offers the perfect foundation upon which readers can build as their studies and careers progress to more advanced forms of statistics. Keeping computational challenges to a minimum, Reid shows readers not only how to conduct a variety of commonly used statistical procedures, but also when each procedure should be utilized and how they are related. Following a review of descriptive statistics, he begins his discussion of inferential statistics with a two-chapter examination of the Chi Square test to introduce students to hypothesis testing, the importance of determining effect size, and the need for post hoc tests. When more complex procedures related to interval/ratio data are covered, students already have a solid understanding of the foundational concepts involved. Exploring challenging topics in an engaging and easy-to-follow manner, Reid builds concepts logically and supports learning through robust pedagogical tools, the use of SPSS, numerous examples, historical quotations, insightful questions, and helpful progress checks. |
| uconn statistics courses: Focus on Making Predictions Curriculum Associates Staff, Dale Lyle, Karla Beatty, Curriculum Associates, Inc, 2005-01-01 |
| uconn statistics courses: Nursing Programs - 2010 Peterson's, 2009-04-22 Presents brief profiles of over three thousand undergraduate, graduate, and postdoctoral nursing programs in the U.S. and Canada, listing nursing student resources and activities, degree programs, and full-time, part-time, and distance learning options. |
| uconn statistics courses: Real Analysis for Graduate Students Richard F. Bass, 2013-01-04 This book is a course on real analysis (measure and integration theory plus additional topics) designed for beginning graduate students. Its focus is on helping the student pass a preliminary or qualifying examination for the Ph.D. degree. |
| uconn statistics courses: Genetics and Conservation of Rare Plants Donald A. Falk, 1991 Nearly 700 species of plants may become extinct by the year 2000. Faced with this overwhelming prospect, plant conservationists must take advantage of every technique available. This unique work summarizes our current knowledge of the genetics and population biology of rare plants, and integrates it with practical conservation recommendations. It features discussions on the distribution and significance of genetic variation, management and evaluation of rare plant germplasm, and conservation strategies for genetic diversity. Case studies focusing on specific problems offer important insights for today's challenges in rare plant conservation. |
| uconn statistics courses: Consumer Math: (10 volumes) Alpha Omega Publications, 1983 |
| uconn statistics courses: The New Literacies Elizabeth A. Baker, 2010-04-13 With contributions from leading scholars, this compelling volume offers fresh insights into literacy teaching and learning—and the changing nature of literacy itself—in today's K–12 classrooms. The focus is on varied technologies and literacies such as social networking sites, text messaging, and online communities. Cutting-edge approaches to integrating technology into traditional, print-centered reading and writing instruction are described. Also discussed are ways to teach the new skills and strategies that students need to engage effectively with digital texts. The book is unique in examining new literacies through multiple theoretical lenses, including behavioral, semiotic, cognitive, sociocultural, critical, and feminist perspectives. |
| uconn statistics courses: A Probability Path Sidney I. Resnick, 2013-11-30 |
| uconn statistics courses: Urban Economic Development Richard D. Bingham, John P. Blair, 1984-11 Urban Economic Development, Volume 27 of the Urban Affairs Annual Reviews, discusses the effectiveness of various policies which aim to stimulate private sector activity in urban areas. It examines urban enterprise zones; grants and investments; federal, state and local development programmes; and four case studies of city projects. |
| uconn statistics courses: Bulletin - Institute of Mathematical Statistics Institute of Mathematical Statistics, 1998 |
| uconn statistics courses: Math in Society David Lippman, 2022-07-14 Math in Society is a survey of contemporary mathematical topics, appropriate for a college-level topics course for liberal arts major, or as a general quantitative reasoning course. This book is an open textbook; it can be read free online at http://www.opentextbookstore.com/mathinsociety/. Editable versions of the chapters are available as well. |
| uconn statistics courses: Nursing Programs 2011 Peterson's, 2010-05-18 Nursing Programs 2011 profiles nearly 3,200 undergraduate, graduate, and postdoctoral options at more than 700 institutions in the Unioted States and Canada. A special section, The Nursing School Advisor, includes indepth articles about degree and career options, the admissions process, and specialized programs for professions such as nurse practitioner and clinical specialist. |
| uconn statistics courses: The Business School Buzz Book Carolyn C. Wise, Stephanie Hauser, 2007 In this updated guide, Vault publishes the entire surveys of current students and alumni at more than 100 top business schools. Each 4- to 5-page entry is composed almost entirely of insider comments from students and alumni. Each school profile features surveys of about 10 students or alumni. These narratives provide applicants with detailed and balanced perspectives and insider information on admissions and employment prospects, which is lacking in other business school guides. |
| uconn statistics courses: Psychology Research Methods Elizabeth Brondolo, 2021-04-25 Psychology Research Methods: A Writing Intensive Approach provides instruction in critical concepts and processes in behavioral science research methods and skills in formulating and writing research papers. The book creates an experiential approach to learning, with chapters organized around the task of writing a complete APA-style research paper.The chapters consist of instructional text, excerpts from published research articles, and learning activities. The reading activities help students develop skills in reading scientific research, evaluating and analyzing scientific information, and assembling evidence to make a scientific argument. The writing activities help students to break down the process of writing a research paper into manageable and meaningful components. As students complete the chapter activities, they assemble their research paper.The book teaches research methods in a clinical context, inspired by the National Institute of Health's Science of Behavior Change Program. Students acquire knowledge about research methods as they read research articles about behavioral health disorders, including studies about their prevalence, causes, and treatment. Teaching research methods with a clinical focus helps students appreciate the value of psychological research.Psychology Research Methods: A Writing Intensive Approach provides instruction in critical concepts and processes in behavioral science research methods and skills in formulating and writing research papers. The book creates an experiential approach to learning, with chapters organized around the task of writing a complete APA-style research paper.The chapters consist of instructional text, excerpts from published research articles, and learning activities. The reading activities help students develop skills in reading scientific research, evaluating and analyzing scientific information, and assembling evidence to make a scientific argument. The writing activities help students to break down the process of writing a research paper into manageable and meaningful components. As students complete the chapter activities, they assemble their research paper.The book teaches research methods in a clinical context, inspired by the National Institute of Health's Science of Behavior Change Program. Students acquire knowledge about research methods as they read research articles about behavioral health disorders, including studies about their prevalence, causes, and treatment. Teaching research methods with a clinical focus helps students appreciate the value of psychological research.Psychology Research Methods: A Writing Intensive Approach provides instruction in critical concepts and processes in behavioral science research methods and skills in formulating and writing research papers. The book creates an experiential approach to learning, with chapters organized around the task of writing a complete APA-style research paper.The chapters consist of instructional text, excerpts from published research articles, and learning activities. The reading activities help students develop skills in reading scientific research, evaluating and analyzing scientific information, and assembling evidence to make a scientific argument. The writing activities help students to break down the process of writing a research paper into manageable and meaningful components. As students complete the chapter activities, they assemble their research paper.The book teaches research methods in a clinical context, inspired by the National Institute of Health's Science of Behavior Change Program. Students acquire knowledge about research methods as they read research articles about behavioral health disorders, including studies about their prevalence, causes, and treatment. Teaching research methods with a clinical focus helps students appreciate the value of psychological research. |
University of Connecticut
5 days ago · It's simple enough, UConn is a great university. But it's more than that. A top-ranked research institution, campuses across Connecticut built to inspire, the global community that is …
Undergraduate Admissions | UConn : Undergraduate Admissions
At the University of Connecticut, we each have the potential to be something great. We're tenacious. We're fierce. We're ready. Because here, we're Huskies. What we start has the …
Admissions - University of Connecticut
At the University of Connecticut, we each have the potential to be something great. We're tenacious. We're fierce. We're ready. Because here, we're Huskies. What we start has the …
About Us - University of Connecticut
This is UConn. Learning and academics are about exploring the things that interest you, growing with that knowledge, and finding the path on which you'll be most successful. With 14 schools …
Schools and Colleges - University of Connecticut
UConn Home; Disclaimers, Privacy, & Copyright; Accessibility © 2025 University of Connecticut
The Graduate School | The Graduate School
May 20, 2025 · Contact us via email at gradschool@uconn.edu or contact a specific staff member. We will respond as quickly as possible and may be able to schedule phone and/or video …
Visit Campus - University of Connecticut
Each year, 50,000 people take student-guided tours of the UConn campus in Storrs departing from the Lodewick Visitors Center. Our four regional campuses across the state at Avery …
Explore UConn | Undergraduate Admissions - Visit
Explore UConn There are many ways to explore life as a future member of our community, whether you're interested in our prestigious academic programs or curious about the clubs, …
Home | UConn Health
UConn is Connecticut’s largest single source of new physicians, surgeons, and dentists. 70% of our medical school graduates are Connecticut physicians or resident physicians, and more …
First-Year Applicants | Undergraduate Admissions
Your pathway to UConn Nation starts here. First-year applicants must complete either the Common Application or Apply Coalition with Scoir . Admission notification begins in mid …
University of Connecticut
5 days ago · It's simple enough, UConn is a great university. But it's more than that. A top-ranked research institution, campuses across Connecticut built to inspire, the global community that is …
Undergraduate Admissions | UConn : Undergraduate Admissions
At the University of Connecticut, we each have the potential to be something great. We're tenacious. We're fierce. We're ready. Because here, we're Huskies. What we start has the …
Admissions - University of Connecticut
At the University of Connecticut, we each have the potential to be something great. We're tenacious. We're fierce. We're ready. Because here, we're Huskies. What we start has the …
About Us - University of Connecticut
This is UConn. Learning and academics are about exploring the things that interest you, growing with that knowledge, and finding the path on which you'll be most successful. With 14 schools …
Schools and Colleges - University of Connecticut
UConn Home; Disclaimers, Privacy, & Copyright; Accessibility © 2025 University of Connecticut
The Graduate School | The Graduate School
May 20, 2025 · Contact us via email at gradschool@uconn.edu or contact a specific staff member. We will respond as quickly as possible and may be able to schedule phone and/or video …
Visit Campus - University of Connecticut
Each year, 50,000 people take student-guided tours of the UConn campus in Storrs departing from the Lodewick Visitors Center. Our four regional campuses across the state at Avery …
Explore UConn | Undergraduate Admissions - Visit
Explore UConn There are many ways to explore life as a future member of our community, whether you're interested in our prestigious academic programs or curious about the clubs, …
Home | UConn Health
UConn is Connecticut’s largest single source of new physicians, surgeons, and dentists. 70% of our medical school graduates are Connecticut physicians or resident physicians, and more …
First-Year Applicants | Undergraduate Admissions
Your pathway to UConn Nation starts here. First-year applicants must complete either the Common Application or Apply Coalition with Scoir . Admission notification begins in mid …
