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Undergraduate students

  • Learn to distinguish between exponential and logistic growth of populations, identify carrying capacity, differentiate density-dependent and density-independent limiting factors, apply population models to data sets and determine carrying capacity from population data. Make predictions on graphs and interpret graphical data to analyze factors that influence population growth.

    This link includes a lesson plan, assessment materials, and access to SmartGraphs, a software that helps students create and interpret graphs.

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  • One of the original (and still best) sources for archived data.

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  • The Research Methods Knowledge Base is a comprehensive web-based textbook that addresses all of the topics in a typical introductory undergraduate or graduate course in social research methods.  It covers the entire research process including: formulating research questions; sampling (probability and nonprobability); measurement (surveys, scaling, qualitative, unobtrusive); research design (experimental and quasi-experimental); data analysis; and, writing the research paper.  It also addresses the major theoretical and philosophical underpinnings of research including: the idea of validity in research; reliability of measures; and ethics.  The Knowledge Base was designed to be different from the many typical commercially-available research methods texts.  It uses an informal, conversational style to engage both the newcomer and the more experienced student of research.  It is a fully hyperlinked text that can be integrated easily into an existing course structure or used as a sourcebook for the experienced researcher who simply wants to browse.

     

    Navigate this source:  http://www.socialresearchmethods.net/kb/contents.php  

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  • Statistics forum for questions/conversations ranging from homework problems in statistics and probability and help using statistical software to statistical research inquiries and career advising.

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  • The Islands is a free, innovative, online virtual human population created by Dr Michael Bulmer from the University of Queensland. The Islands supports the teaching of statistics through data investigations by providing students with a realistic virtual world where they can propose statistical questions, design investigations and collect the necessary data for statistical analysis and interpretation. The wide range of data and tasks available on the Islands caters to many scientific areas and student interests. Must create an account to access this virtual world.

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  • The Probability Web is a collection of probability resources designed to be especially helpful to researchers, teachers, and people in the probability community.  Web page links on this site include probabilty/statistics books and journals, information on mathematics and statistics-based careers, statistical software, teaching resources on probabilty topics, and more.

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  • The Journal of Statistics Education provides a collection of Java applets and excel spreadsheets (and the articles associated with them) from as early as 1998 on this webpage.

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  • StatCrunch is a web-based package that does a complete range of statistical calculations. Formerly known as WebStat, it provides statistical calculation functions that would be done in most introductory statistics courses, including, but not limited to, creating histograms, pie charts, and boxplots; calculating summary statistics and confidence intervals; and performing hypothesis tests. It allows data to be entered in a spreadsheet style data window or opened from a file. StatCrunch does require a subscription for students and professionals ($13 for 6 months and $23 for 12 months).

    StatCrunchThis allows you to pull data sets contained on many web pages in various forms directly into StatCrunch for analysis.

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  • This is a chapter on data wrangling excerpted from a book on data science. The book is “Modern Data Science with R,” and the authors are Benjamin J. Baumer, Daniel T. Kaplan, and Nicholas J. Horton. It contains the R code needed to do basic things with data such as sorting, arranging, and summarizing data.

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  • This is a chapter on ethics excerpted from a book on data science. The book is “Modern Data Science with R,” and the authors are Benjamin J. Baumer, Daniel T. Kaplan, and Nicholas J. Horton. The chapter presents several ethical dilemmas, then a framework to use when evaluating ethical issues. Then it discusses the dilemmas again, now resolving them.

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