This issue contains articles about Karl Pearson (150 years after his birth); finding more ways to make learning statistics fun; simulating capture-recapture sampling in Excel and by hand; common misconceptions in statistics; a correlation-based puzzler and a STAT.DOKU puzzle.
This site is a collection of resources related to experiments. The site includes references, resources, and articles related to the scientific method, experimental research, ethics in research, and research design. It also includes tips on writing scientific papers, and there are several statistics tutorials on the site. Another interesting feature of the site is a collection of case studies that include descriptions of famous research studies in fields like social psychology, sociology, physics, biology, and medicine.
This simulation illustrates least squares regression and how the least squares solution minimizes the sum of the squared residuals. The applet demonstrates, in a visual manner, various concepts related to least squares regression. These include residuals, sum of squares, the mean line, how the line of best fit is determined, and how the line of least squares solution minimizes the sum of the squared residuals.
Statistics and probability concepts are included in K–12 curriculum standards—particularly the Common Core State Standards—and on state and national exams. STEW provides free peer-reviewed teaching materials in a standard format for K–12 math and science teachers who teach statistics concepts in their classrooms.
STEW lesson plans identify both the statistical concepts being developed and the age range appropriate for their use. The statistical concepts follow the recommendations of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: A Pre-K-12 Curriculum Framework, Common Core State Standards for Mathematics, and NCTM Principles and Standards for School Mathematics. The lessons are organized around the statistical problemsolving process in the GAISE guidelines: formulate a statistical question, design and implement a plan to collect data, analyze the data by measures and graphs, and interpret the data in the context of the original question. Teachers can navigate the STEW lessons by grade level and statistical topic.
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.
CODAP provides an easy-to-use web-based data analysis platform, geared toward middle and high school students, and aimed at teachers and curriculum developers. CODAP can be incorporated across the curriculum to help students summarize, visualize and interpret data, advancing their skills to use data as evidence to support a claim.
May 8, 2007 webinar resented by Bill Notz, The Ohio State University, and hosed by Jackie Miller, The Ohio State University. In this webinar Bill Notz, the Editor of the Journal of Statistics Education (JSE), discusses all aspects of the journal. He outlines the mission and history of the JSE, describes the various departments of the journal, explains what you can find at the journal's web site, indicates the types of manuscripts the journal seeks to publish, and mentions possible future directions.
This resource gives 3 questions readers should ask when presented with data and why to ask them: Where did the data come from? Have the data been peer-reviewed? How were the data collected? This page also describes why readers should: be skeptical when dealing with comparisons, and be aware of numbers taken out of context.
March 11, 2008 Teaching and Learning webinar presented by Deborah Nolan, University of California at Berkeley and hosted by Jackie Miller, The Ohio State University. Computing is an increasingly important element of statistical practice and research. It is an essential tool in our daily work, it shapes the way we think about statistics, and broadens our concept of statistical science. Although many agree that there should be more computing in the statistics curriculum and that statistics students need to be more computationally capable and literate, it can be difficult to determine how the curriculum should change because computing has many dimensions. In this webinar Dr. Nolan explores alternatives to teaching statistics that include innovations in data technologies, modern statistical methods, and a variety of computing skills that will enable our students to become active and engaged participants in scientific discovery.
An important idea in statistics is that the amount of data matters. We often teach this with formulas --- the standard error of the mean, the t-statistic, etc. --- in which the sample size appears in a denominator as √n. This is fine, so far as it goes, but it often fails to connect with a student's intuition. In this presentation, I'll describe a kinesthetic learning activity --- literally a random walk --- that helps drive home to students why more data is better and why the square-root arises naturally and can be understood by simple geometry. Students remember this activity and its lesson long after they have forgotten the formulas from their statistics class.