A song for use in helping students to recognize how a change in units affects the variance (since variance is expressed in squared units). Music & Lyrics by Tom Toce, ©2015 Retrograde Music. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students to interpret positive, negative, and zero correlation and transfer knowledge about associations to new context/situations. Lyrics by Lawrence M. Lesser and may be sung to the tune of “Twinkle, Twinkle, Little Star.” This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students to recognize and construct examples to illustrate how correlation does not imply causation. Music & Lyrics © 2016 by Monty Harper. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students to recognize and construct examples to illustrate how correlation does not imply causation. Music & Lyrics © 2016 by Monty Harper. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
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.
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.
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.
"There are a lot of small data problems that occur in big data. They don't disappear because you've got lots of stuff. They get worse." is a quote by British biostatistician David J. Spiegelhalter (1953 - ). The quote may be found in a March 28, 2014 article in the Financial Times written by Tim Hartford entitled "Big data: are we making a big mistake?"
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.