**HHMI Quantitative Biology Conference**

**HHMI Quantitative Biology Conference**

**June 11 – 14, 2013 **

**Emory University **

**June 11 – 14, 2013**

**Emory University**

This annotated list of the Quantitative Biology Conference resources will continue to be added to throughout the conference.

Comments, including those that suggest additional resources, are welcome.

**Projects and Conferences**

**Prior HHMI Quantitative Biology Workshops **

http://wikifuse.pbworks.com/w/page/14383803/FrontPage and http://wikifuse.pbworks.com/w/page/14383857/Resources

**Making Biomath Happen **University of Arizona (2012)

The National Academy of Sciences call in BIO2010 has led to the creative energy behind many new innovations in curriculum in mathematics courses, life sciences courses and interdisciplinary courses. The

University of Arizonahosted a conference in June, 2012 “” which includes online resources.

*Using Data in Undergraduate Science Classrooms **Carleton College* Science Education Resource Center (2003)

The project provides information and discussion for educators and resource developers interested in effective teaching methods and pedagogical approaches for using data in the classroom. Great starting point for exploring what works.

* MathBench Biology Modules *(

*University of Maryland*)

Introduces students to the mathematical underpinnings of what they learn in introductory biology courses. Their goal is to integrate quantitative approaches and mathematics more deeply into the undergraduate curriculum in a way that (1) reinforces biological concepts, (2) increases math literacy, and (3) prepares students for more complicated mathematical approaches in upper-level courses.

* Numbers Count *(

*University of Minnesota – Rochester)*

The focus is on biological problem solving with data. Faculty workshop and classroom resources include biological data (

Excelfiles, images, and FASTA format text files), introductory mathematical modules for biology and chemistry, concepts in statistics mapped to individual modules, our workshop resources (materials, tools, data, powerpoints, and participant products), course materials forCalculusandFreshman Statistics, andlinks to sites focusing on data,modeling tools,and visualization.

**Publications**

**Special Issue of CBE—Life Sciences Education**

http://www.lifescied.org/content/9/3.toc

Welcome to this special issue of

CBE—Life Sciences Education! The national scientific and academic community has issued repeated clarion calls for revising college biology curricula and the mathematical and computational preparation for future life scientists to reflect the tools and practices of science. This issue celebrates progress on incorporating quantitative reasoning into biology courses and integrating biological exemplars into mathematics courses. Within the 17 articles, seven essays, and seven features, readers find examples of innovative undergraduate research programs that emerged from research collaborations between biologists and mathematicians as well as collaborations initiated by either biologists or mathematicians. Other articles and essays illustrate collaborations between biologists and quantitative scientists that have resulted in new courses, new majors, textbooks, and modules that highlight and celebrate progress toward theBIO2010vision.

**USING DATA IN UNDERGRADUATE SCIENCE CLASSROOMS: USING DATA IN OUR CLASSES AND LABS**

http://serc.carleton.edu/files/usingdata/UsingData.pdf

Focuses learning on the process of science while capitalizing on new opportunities created by data-sharing and web technology. This report summarizes discussions of 26 faculty from science, technology, engineering, and mathematics (STEM) disciplines and National Science Digital Library (NSDL) project Principal Investigators to better define why, how, and to what effect faculty use datain the classroom. The workshop goals were to advance the effective use of data in the classroom, to support inquiry- and discovery-based learning, to identify common practices in the use of data among the science disciplines as well as their special needs, and to inform the design of online data-delivery tools.

**Achieving Quantitative Literacy: An Urgent Challenge … – Amazon.com**** **

**http://www.amazon.com/Achieving-Quantitative-Literacy-Challenge-Education/dp/0883858169 **

A century after the United States crossed the threshold into universal secondary education we are crossing a quite different threshold into universal postsecondary education. Consequently, society now expects of higher education what in the early twentieth century it asked of secondary schools, namely, to prepare students for civic and economic life. In contrast to that earlier time, however, our age is dominated by computers and data, not factory assembly lines. These changes in society have created an urgent demand for multifaceted literacy far more sophisticated than what previously served as the foundation of today’s curriculum.This greater demand for higher order competencies is nowhere more apparent than in the area of quantitative literacy (QL). Although no less important for all citizens than fluency in reading and writing, quantitative literacy too often continues to be the province of the few. Indeed, for too long, our educational system has produced a scientific and mathematical elite while failing to nurture the literate citizenry required for robust democracy. As a result, the gap between expert and citizen has widened dangerously, most notably when numbers and data are brought to bear in deciding public and private issues – and one can scarcely think of an issue in contemporary life where this is not the case.

**Liberal Education | Spring 2012 | Achieving a Quantitatively Literate … Achieving a Quantitatively Literate Citizenry: Resources and Community to Support National Change**

Reports and Case Statements

Beyond Crossroads, AMATYC, 2005

Mathematics and Democracy: The Case for Quantitative Literacy, National Council on Education and the Disciplines, Lynn Arthur Steen, ed., 2001

Why Numbers Count: Qualitative Literacy for Tomorrow’s America, The College Board, 1997

Crossroads in Mathematics, American Mathematical Association for Two-Year Colleges (AMATYC), 1995

Quantitative Reasoning for College Graduates: A Complement to the Standards, Mathematical Association of America, 1995

CUPM Quantitative Literacy Committee

Moving Beyond Myths: Revitalizing Undergraduate Mathematics, National Academy Press, 1991

What Work Requires of Schools: A SCANS Report for 2000, Secretary’s Commission on Achieving Necessary Skills (SCANS),U.S. Department of Labor, Washington, DC, 1991

A Challenge of Numbers: People in the Mathematical Sciences,National Academy Press, 1990

Everybody Counts: A Report to the Nation on the Future of Mathematics Education, National Academy Press, 1989

**Scientific Thinking and Integrative Reasoning Skills (STIRS) **http://www.aacu.org/stirs/

OverviewTo become engaged and productive citizens prepared to address the critical challenges of the 21

^{st}century, college graduates in all fields of study need to be able to:

- Use scientific reasoning to gather and evaluate evidence
- Understand how scientific and social science studies are designed and executed and recognize the implications of design choices
- Use statistical reasoning to evaluate data and use data to communicate effectively
- Base decisions on analysis of evidence, logic, and ethics

**Pat Marsteller**

**Introduction to Special Issue ****Beyond BIO2010: Integrating Biology and Mathematics: Collaborations, Challenges, and Opportunities**

CBE Life Sci Educ

20109:141–142; doi:10.1187/cbe.10-06-0084

**From the National Academies:**

**John R. Jungck, Holly D. Gaff, Adam P. Fagen, and Jay B. Labov**

**“Beyond ***BIO2010*: Celebration and Opportunities” at the Intersection of Mathematics and Biology

*BIO2010*: Celebration and Opportunities” at the Intersection of Mathematics and Biology

CBE Life Sci Educ

20109:143–147; doi:10.1187/cbe.10-06-0079

**Erin Dolan**

**Recent Research in Science Teaching and Learning**

CBE Life Sci Educ

20109:148–149; doi:10.1187/cbe.10-06-0081

**WWW.Life Sciences Education**

**Dennis Liu**

**Math and Data Exploration**

CBE Life Sci Educ

20109:150–153; doi:10.1187/cbe.10-05-0073

### Book Review

**Raquell M. Holmes**

**Physics in a Beautiful Context**

CBE Life Sci Educ

20109:154–156; doi:10.1187/cbe.10-06-0080

**Laura L. Mays Hoopes E. Virginia Armbrust**

**Educator Highlight**

CBE Life Sci Educ

20109:157–158; doi:10.1187/cbe.10-06-0074

### Approaches to Biology Teaching and Learning

**Kimberly D. Tanner**

#### Order Matters: Using the 5E Model to Align Teaching with How People Learn

CBE Life Sci Educ

20109:159–164; doi:10.1187/cbe.10-06-0082

**Pat Marsteller, Lisette de Pillis, Ann Findley, Karl Joplin, John Pelesko, Karen Nelson, Katerina Thompson, David Usher, and Joseph Watkins**

**Toward Integration: From Quantitative Biology to Mathbio-Biomath?**

CBE Life Sci Educ

20109:165–171; doi:10.1187/cbe.10-03-0053

**Mark Maloney, Jeffrey Parker, Mark LeBlanc, Craig T. Woodard, Mary Glackin, and Michael Hanrahan**

#### Bioinformatics and the Undergraduate Curriculum

CBE Life Sci Educ

20109:172–174; doi:10.1187/cbe.10-03-0038

**Maeve L. McCarthy and K. Renee Fister**

**BioMaPS: A Roadmap for Success**

CBE Life Sci Educ

20109:175–180; doi:10.1187/cbe.10-03-0023

**David C. Usher, Tobin A. Driscoll, Prasad Dhurjati, John A. Pelesko, Louis F. Rossi, Gilberto Schleiniger, Kathleen Pusecker, and Harold B. White**

#### A Transformative Model for Undergraduate Quantitative Biology Education

CBE Life Sci Educ

20109:181–188; doi:10.1187/cbe.10-03-0029

**Srebrenka Robic**

#### Mathematics, Thermodynamics, and Modeling to Address Ten Common Misconceptions about Protein Structure, Folding, and Stability

CBE Life Sci Educ

20109:189–195; doi:10.1187/cbe.10-03-0018

**Andrej Šorgo**

**Connecting Biology and Mathematics: First Prepare the Teachers**

CBE Life Sci Educ

20109:196–200; doi:10.1187/cbe.10-03-0014

**John R. Jungck, Holly Gaff, and Anton E. Weisstein**

#### Mathematical Manipulative Models: In Defense of “Beanbag Biology”

CBE Life Sci Educ

20109:201–211; doi:10.1187/cbe.10-03-0040

**Lester Caudill, April Hill, Kathy Hoke, and Ovidiu Lipan**

#### Impact of Interdisciplinary Undergraduate Research in Mathematics and Biology on the Development of a New Course Integrating Five STEM Disciplines

CBE Life Sci Educ

20109:212–216; doi:10.1187/cbe.10-03-0020

**Yolande V. Tra and Irene M. Evans**

#### Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

CBE Life Sci Educ

20109:217–226; doi:10.1187/cbe.09-09-0067

**Raina Robeva, Robin Davies, Terrell Hodge, and Alexander Enyedi**

#### Mathematical Biology Modules Based on Modern Molecular Biology and Modern Discrete Mathematics

CBE Life Sci Educ

20109:227–240; doi:10.1187/cbe.10-03-0019

**Gregory Goins, Mingxiang Chen, Catherine White, Dominic Clemence, Thomas Redd, and Vinaya Kelkar**

#### An Initiative to Broaden Diversity in Undergraduate Biomathematics Training

CBE Life Sci Educ

20109:241–247; doi:10.1187/cbe.10-03-0043

**Hillel J. Chiel, Jeffrey M. McManus, and Kendrick M. Shaw**

#### From Biology to Mathematical Models and Back: Teaching Modeling to Biology Students, and Biology to Math and Engineering Students

CBE Life Sci Educ

20109:248–265; doi:10.1187/cbe.10-03-0022

**Julia Svoboda and Cynthia Passmore**

#### Evaluating a Modeling Curriculum by Using Heuristics for Productive Disciplinary Engagement

CBE Life Sci Educ *2010 **9**:**266**–**276**; doi:**10.1187/cbe.10-03-0037 *

**Katerina V. Thompson, Kären C. Nelson, Gili Marbach-Ad, Michael Keller, and William F. Fagan**

#### Online Interactive Teaching Modules Enhance Quantitative Proficiency of Introductory Biology Students

CBE Life Sci Educ

20109:277–283; doi:10.1187/cbe.10-03-0028

**Jason E. Miller and Timothy Walston**

#### Interdisciplinary Training in Mathematical Biology through Team-based Undergraduate Research and Courses

CBE Life Sci Educ

20109:284–289; doi:10.1187/cbe.10-03-0046

**Kelly E. Matthews, Peter Adams, and Merrilyn Goos**

#### Using the Principles of *BIO2010* to Develop an Introductory, Interdisciplinary Course for Biology Students

CBE Life Sci Educ

20109:290–297; doi:10.1187/cbe.10-03-0034

**Joseph C. Watkins**

#### On a Calculus-based Statistics Course for Life Science Students

CBE Life Sci Educ

20109:298–310; doi:10.1187/cbe.10-03-0035

**Sarah I. Duncan, Pamela Bishop, and Suzanne Lenhart**

#### Preparing the “New” Biologist of the Future: Student Research at the Interface of Mathematics and Biology

CBE Life Sci Educ

20109:311–315; doi:10.1187/cbe.10-03-0025

**John G. Milton, Ami E. Radunskaya, Arthur H. Lee, Lisette G. de Pillis, and Diana F. Bartlett**

#### Team Research at the Biology–Mathematics Interface: Project Management Perspectives

CBE Life Sci Educ

20109:316–322; doi:10.1187/cbe.10-03-0021

**Elena Bray Speth, Jennifer L. Momsen, Gregory A. Moyerbrailean, Diane Ebert-May, Tammy M. Long, Sara Wyse, and Debra Linton**

#### 1, 2, 3, 4: Infusing Quantitative Literacy into Introductory Biology

CBE Life Sci Educ

20109:323–332; doi:10.1187/cbe.10-03-0033

**Anne-Marie Hoskinson**

#### How to Build a Course in Mathematical–Biological Modeling: Content and Processes for Knowledge and Skill

CBE Life Sci Educ

20109:333–341; doi:10.1187/cbe.10-03-0041

**Audrey M. Depelteau, Karl H. Joplin, Aimee Govett, Hugh A. Miller III, and Edith Seier**

#### SYMBIOSIS: Development, Implementation, and Assessment of a Model Curriculum across Biology and Mathematics at the Introductory Level

CBE Life Sci Educ

20109:342–347; doi:10.1187/cbe.10-05-0071

**Roni Ellington, James Wachira, and Asamoah Nkwanta**

#### RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

CBE Life Sci Educ

20109:348–356; doi:10.1187/cbe.10-03-0036

**Tomasz G. Smolinski**

#### Computer Literacy for Life Sciences: Helping the Digital-Era Biology Undergraduates Face Today’s Research

CBE Life Sci Educ

20109:357–363; doi:10.1187/cbe.10-03-0050

**Jeff Knisley and Esfandiar Behravesh**

#### Developing Student Collaborations across Disciplines, Distances, and Institutions

CBE Life Sci Educ

20109:364–369; doi:10.1187/cbe.10-03-0031

**Dwight Duffus and Andrei Olifer**

#### Introductory Life Science Mathematics and Quantitative Neuroscience Courses

CBE Life Sci Educ

20109:370–377; doi:10.1187/cbe.10-03-0026

**Andreas Madlung, Martina Bremer, Edward Himelblau, and Alexa Tullis**

**A Study Assessing the Potential of Negative Effects in Interdisciplinary Math–Biology Instruction **

CBE Life Sci Educ

201110:43–54; doi:10.1187/cbe.10-08-0102Strategies for reducing math anxiety in post-secondary…for collaboration. In: Math and Bio 2010: Linking Undergraduate…Research Council (2003). BIO 2010: Transforming Undergraduate…attitudes towards statistics, math self-concept, test anxiety…

**Jessica Watkins and Andrew Elby**

**Context Dependence of Students’ Views about the Role of Equations in Understanding Biology **

CBE Life Sci Educ

June 3, 201312:274–286; doi:10.1187/cbe.12-11-0185…discipline-specific epistemologies in math and science, more systematic work is…epistemologies and expectations. The CLASS-Bio measures whether students report making…postsurvey questions about the importance of math in biology.

** Eric Brewe, Nancy J. Pelaez, and Todd J. Cooke**

#### From Vision to Change: Educational Initiatives and Research at the Intersection of Physics and Biology

CBE Life Sci Educ

June 3, 201312:117–119; doi:10.1187/cbe.13-03-0069In this editorial we link the articles published in this Special Issue with the framework from Vision and Change and summarize findings from the editorial process of assembling the Special Issue.

### From the National Science Foundation

**Terry Woodin, Helen Vasaly, Duncan McBride, and Gary White**

#### Integration of Physics and Biology: Synergistic Undergraduate Education for the 21st Century

CBE Life Sci Educ

June 3, 201312:120–123; doi:10.1187/cbe.13-03-0053Three aspects of the interactions of physics and biology are covered as seen from the viewpoint of four members of the Division of Undergraduate Education of the National Science Foundation.

** Jason Feser, Helen Vasaly, and Jose Herrera**

#### On the Edge of Mathematics and Biology Integration: Improving Quantitative Skills in Undergraduate Biology Education

CBE Life Sci Educ

June 3, 201312:124–128; doi:10.1187/cbe.13-03-0057The feature describes two major efforts to integrate mathematics and biology. A call is made to biologists to consider the need to address biology undergraduate education changes and to use the resources described.

**Elisa M. Stone**

#### Editorial Preface

CBE Life Sci Educ

June 3, 201312:139; doi:10.1187/cbe.13-03-0065

**David G. L. Van Wylen, Beth R. J. Abdella, Shelly D. Dickinson, Jason J. Engbrecht, and Rebecca Vandiver**

#### Interdisciplinarity: The Right *People*, a Supportive *Place*, and a *Program* Emerges

CBE Life Sci Educ

June 3, 201312:140–143; doi:10.1187/cbe.13-01-0001This paper describes the St. Olaf College experience moving to a more interdisciplinary approach to student learning. The authors place this within the context of the three “P”s of higher education—

people,place, andprogram. The key for transformation resided in focusing on the people and the place. In so doing, an interdisciplinary program emerged.

**Barbara Nagle**

#### Preparing High School Students for the Interdisciplinary Nature of Modern Biology

CBE Life Sci Educ

June 3, 201312:144–147; doi:10.1187/cbe.13-03-0047Preparing students for the interdisciplinary nature of modern biology will require changes in curriculum, instruction, assessments, and teacher professional development in order to support teaching for conceptual understanding and for making cross-disciplinary connections.

**Katerina V. Thompson, Jean Chmielewski, Michael S. Gaines, Christine A. Hrycyna, and William R. LaCourse**

#### Competency-Based Reforms of the Undergraduate Biology Curriculum: Integrating the Physical and Biological Sciences

CBE Life Sci Educ

June 3, 201312:162–169; doi:10.1187/cbe.12-09-0143In response to the Association of American Medical Colleges–Howard Hughes Medical Institute report

Scientific Foundations for Future Physicians, a collaborative effort by four institutions has produced an introductory physics for life sciences course that stresses competency building and helps students apply strategies from the physical sciences to solve authentic biological problems.

**Robert C. Hilborn and Michael J. Friedlander**

#### Biology and Physics Competencies for Pre-Health and Other Life Sciences Students

CBE Life Sci Educ

June 3, 201312:170–174; doi:10.1187/cbe.12-10-0184We describe how the competencies articulated in the

Scientific Foundations for Future Physiciansreport influenced the structure of the revised MCAT.

**Edward F. Redish and Todd J. Cooke**

#### Learning Each Other’s Ropes: Negotiating Interdisciplinary Authenticity

CBE Life Sci Educ

June 3, 201312:175–186; doi:10.1187/cbe.12-09-0147This article considers a multiyear conversation between a physicist interested in adapting a physics course for biologists and a biologist interested in including more physics in a biology course. Examples are given, along with insights developed about the different approaches biologists and physicists tend to take toward the same phenomena.

**Jessica Watkins and Andrew Elby**

#### Context Dependence of Students’ Views about the Role of Equations in Understanding Biology

CBE Life Sci Educ

June 3, 201312:274–286; doi:10.1187/cbe.12-11-0185The authors show how students’ views about what counts as learning in biology can be context-dependent. They examine an interview in which a student expressed two different views about the use of equations in biology. The results highlight how a given student can have diverse ways of thinking about the value of bringing physics and math into biology.

Quantitative Reasoning: Mathematics Across the Curriculum, Klement Teixeira, Borough of Manhattan Community College

What Mathematics Should All College Student Know?Bill Briggs, in Current Practices in Quantitative Literacy, Rick Gillman, ed. Mathematical Association of America, 2004.

Developing QR at a Two-Year College, Klement Teixeira, Borough of Manhattan Community College

Student Engagement in a Quantitative Literacy Course, Bill Briggs, Mitch Handelsman, Nora Sullivan, AMATYC Review, Fall 2004

, Bill Briggs, SIAM News, April 2002.Quantitative Literacy and SIAM, Bernard Madison, Notice of the AMS, February 2002Educating for Numeracy: A Challenging Responsibility

, Lynn Steen, Liberal Education, Summer 2001Reading, Writing, and Numeracy

, Lynn Steen, Educational Leadership, October 1999Numeracy: The New Literacy for a Data-Drenched Society

General Education Mathematics: New Approaches for a New Millennium, Jeff Bennett and Bill Briggs, AMATYC Review, Fall 1999

Reflections on QL,Lynn Steen and Bernard Madison, 1999

Numeracy,Lynn Steen, Daedalus, Spring 1990

I’d like to recommend the Biological ESTEEM collection of Excel modules as a resource. Each module includes a downloadable Excel (.xls) file, references to textbooks where the relevant biology and mathematics are introduced, the original sources of such models, current research articles that employ the models explicitly or derivatives of these models, and online related resources. In some instances, additional documentation, other software (particularly Java Applets and remotely run Web Mathematica applications), classroom-lab-field activities, science and mathematics education research references, and historical material are also provided.

There are a lot of resources we may all want to revisit.

Bio 2010 http://www.nap.edu/openbook.php?isbn=0309085357 suggests:

“Quantitative analysis, modeling, and prediction play increasingly significant day-to-day roles in today’s biomedical research. To prepare for this sea change in activities, biology majors headed for research careers need to be educated in a more quantitative manner, and such quantitative education may require the development of new types of courses. The committee recommends that all biology majors master the concepts listed below. In addition, the committee recommends that life science majors become sufficiently familiar with the elements of programming to carry out simulations of physiological, ecological, and evolutionary processes. They should be adept at using computers to acquire and process data, carry out statistical characterization of the data and perform statistical tests, and graphically display data in a variety of representations. Furthermore, students should also become skilled at using the Internet to carry out literature searches, locate published articles, and access major databases.

The elucidation of the sequence of the human genome has opened new vistas and has highlighted the increasing importance of mathematics and computer science in biology. The intense interest in genetic, metabolic, and neural networks reflects the need of biologists to view and understand the coordinated activities of large numbers of components of the complex systems underlying life. Biology students should be prepared to carry out in silico (computer) experiments to complement in vitro and in vivo experiments. It is essential that biology undergraduates become quantitatively literate. The concepts of rate of change, modeling, equilibria and stability, structure of a system, interactions among components, data and measurement, visualizing, and algorithms are among those most important to the curriculum. Every student should acquire the ability to analyze issues arising in these contexts in some depth, using analytical methods (e.g., pencil and paper), appropriate computational tools, or both. The course of study would include aspects of probability, statistics, discrete models, linear algebra, calculus and differential equations, modeling, and programming.”

Math & Bio 2010Linking Undergraduate disciplines

http://www.maa.org/mtc/projectreport.html

This series of essays gives a broad perspective on approaches to a more quantitative, computational and mathematical appraoch to undergraduate biology education.

Indeed for Intro courses, we could look at the AP Bio Quantitative Skills Guide. http://apcentral.collegeboard.com/apc/public/repository/AP_Bio_Quantitative_Skills_Guide-2012.pdf

It covers graphing, data analysis, hypothesis testing and mathematical modeling.

Malcom Campbell’s and Laurie Heyer’s book

Discovering Genomics, Proteomics and Bioinformaticshttp://wps.aw.com/bc_campbell_genomics_2/0,11571,2875502-,00.html

They have incorporated math minutes into the text. Perhaps this could be a model for others.They provide mathematical underpinnings and give practice in applying the math.

Math Minutesincluded:clustering

correl_explore.xls

correlation guide

diatom_he.xls

diatom_sim.xls

dot plot

dotplot.xls

drugmodel.xls

Endo16 Perl

Exploring Dot Plots

genetictests.xls

modeling activity

pwm.xls

Reading BLASTp

software

Here’s new link from John R Jungck: Mapping with Excel Data: Mapping with Excel Data: http://m.wikihow.com/Create-a-Google-Map-With-Excel-Data-and-Fusion-Tables (Thanks, John!)

From Pat:

Cluster analysis – Wikipedia, the free encyclopedia

en.wikipedia.org/wiki/Cluster_analysis

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called cluster) are more similar (in some sense …

Clusters and clusterings – Clustering algorithms – Evaluation of clustering results

Cluster analysis (in marketing) – Wikipedia, the free encyclopedia

en.wikipedia.org/wiki/Cluster_analysis_(in_marketing)

Cluster analysis is a class of statistical techniques that can be applied to data that exhibit “natural” groupings. Cluster analysis sorts through the raw data and …

Cluster analysis and display of genome-wide expression patterns.

http://www.ncbi.nlm.nih.gov/pubmed/9843981

by MB Eisen – 1998 – Cited by 13170 – Related articles

Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8. Cluster analysis and display of genome-wide expression patterns. Eisen MB, Spellman PT, Brown PO, …

[PDF]

Cluster Analysis

http://www.norusis.com/pdf/SPC_v13.pdf

Tip: Although both cluster analysis and discriminant analysis classify objects (or cases) into … In cluster analysis, you don’t know who or what belongs in.

How To Group Objects Into Similar Categories, Cluster Analysis

http://www.statsoft.com/textbook/cluster-analysis/

The term cluster analysis (first used by Tryon, 1939) encompasses a number of different algorithms and methods for grouping objects of similar kind into …

[PDF]

Cluster Analysis: Basic Concepts and Algorithms – CSE User Home …

www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf

for understanding or utility, cluster analysis has long played an important …There have been many applications of cluster analysis to practical prob- lems.

Cluster Analysis: Basic Concepts and Algorithms – CSE User Home …

www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf

for understanding or utility, cluster analysis has long played an important …There have been many applications of cluster analysis to practical prob- lems.

A Simple Tutorial on Conjoint and Cluster Analysis – SlideShare

http://www.slideshare.net/…/a-simple-tutorial-on-conjoint-and-cluster-analysis

A simple tutorial to show conjoint analysis and cluster analysis. please send your feedback, this version is still rough and I would like to iteratively improve.

clustering – Why don’t dummy variables have the continuous …

stats.stackexchange.com/…/why-dont-dummy-variables-have-the-contin…

Oct 17, 2012 – I know that if we use categorical variables in cluster analysiswe would assume … But what is the difference when you use dummyvariables?

[PDF]

Download Cluster Analysis – Daniel Wiechmann

http://www.daniel-wiechmann.eu/downloads/cluster_%20analysis.pdf

Cluster Analysis … Cluster Analysis: A practical introduction with R. [materials from workshop at the ….. Recode VAR as dummies and proceed as just described.

Cluster Analysis For Dummies – Free PPT downloads

freepdfdb.com/ppt/cluster-analysis-for-dummies

40+ items – Download free ppt files, ebooks and documents about Cluster …

From Pat:

An International Look at Women’s Cancers via GapMinder | SeventyK

http://www.seventyk.org › Blog

Aug 24, 2011 – GapMinder is a nonprofit based out of Sweden and was acquired by … death rate in developing countries (here an HPV vaccine would be most …

HPV – Get Satisfaction

getsatisfaction.com/gapminder/topics/hpv

A description for this result is not available because of this site’s robots.txt –learn more.

Gapminder: Hans Rosling illustrates the relationship between wealth…

saveone.net/Gapminder-Hans-Rosling-illustrates-the-relationship-betwee…

Factsheet: Human Papillomavirus (HPV) ….. Gapminder: Hans Rosling illustrates the relationship between wealth and life expectancy Take Action.

Review: Gapminder Makes Statistics Fun | BlogHer

http://www.blogher.com › Home › Web site

Oct 21, 2010 – HPV & Our Sons: Why I’m Vaccinating My Boys ….Gapminder describes itself as “unveiling the beauty of statistics for a fact based world view.

WinCollGeog: Ageing populations – features Gapminderfounder …

wincollgeog.blogspot.com/…/ageing-populations-features-gapminder.ht…

Mar 2, 2013 – Ageing populations – features Gapminder founder Hans Rosling! Forum: The Challenge of … HPV vaccine for schoolboys? Powered by Blogger.

Breast Cancer Statistics

Breast Cancer increases with income, but so does the chances of saving women with cancer.