This online software allows you to load data and make professional-looking graphs with it. Graph types are basic (scatterplot, line plot, bar charts, etc.), statistical (histograms, box plots), scientific (error bars, heat map, contour), 3D charts, and financial (e.g. time series). Other graphs are available with the paid pro version. Log in is required, which allows you to upload data and save it for next use.
A game to aid in the active learning of linear regression. TigerSTAT is a three dimensional on-line game where students use the game to collect data and explore models for estimating the age of a Siberian tiger. In this game, students act as researchers on a national preserve where they are expected to catch tigers, collect data, analyze their data (using simple linear regression on transformed data), and draw appropriate conclusions. Instructors also have the option of asking students to read a scientific publication discussing current methods in estimating ages of tigers. The TigetSTAT labs handouts were created by Rod Sturdivant (Ohio State University), Kevin Cummiskey (West Point) and John Jackson (West Point). Tietronix Software developed the game. This resource is part of the Stat2Labs collection.
A pun to familiarize students with Anscombe's Quartet - the group of 4 data sets with the same means, standard deviations, correlations, and regression lines for X and Y that were produced by British statistician Frank Anscombe in a 1973 paper in the American Statistician. The joke was written in 2016 by Larry Lesser from The University of Texas at El Paso. This joke should be used in a written form since students will not "get" the joke if they have never heard of Anscombe's Quartet - the value for teaching coming from having them look it up. Alternatively, it can be used in an oral presentation following an activity on this topic.
A pun to be used in discussing the concept of regression to the mean. The joke was co-authored in 2017 by Larry Lesser (The University of Texas at El Paso) and Dennis Pearl (Penn State University).
This case study starts by the simple comparison of the prices of houses with and without fireplaces and extends the analysis to examine other characteristics of the houses with fireplace that may affect the price as well. The intent is to show the danger of using simple group comparisons to answer a question that involves many variables. The lesson shows the R code for doing this analysis; however, the data and the model could be used with another statistical software.