Statistics is the art of stating in precise terms that which one does not know. A quote by American Statistician William Henry Kruskal (1919 - 2005) in his article "Statistics, Moliere, and Henry Adams," in "American Scientist Magazine" (1967; vol. 55, page 417).The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.
If you can't measure it, I'm not interested. A quote by Canadian educator and management theorist Laurence Johnston Peter (1919 - 1990) from "Peter's People" in "Human Behavior" (August, 1976; page 9). The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.
Those who ignore Statistics are condemned to reinvent it. A quote attributed to Stanford University professor of Statistics Bradley Efron (1938 - ) by his colleague Jerome H. Friedman in a talk to the 29th Symposium on the Interface (May 1997, Houston) and in a paper "The role of Statistics in the Data Revolution" later published in "International Statistical Review" (2001; vol. 69, pages 5 - 10).
This activity enables students to learn about confidence intervals and hypothesis tests for a population mean. It focuses on the t-distribution, the assumptions for using it, and graphical displays. The activity also focuses on how to interpretations a confidence interval, a p-value, and a hypothesis test.
This activity explains the important features of a distribution: shape, center, spread, and unusual features. It also covers how to determine the difference between mean and median, and their respective measures of spread, as well as when to apply them to a particular distribution. Graphical displays such as: histograms and boxplots are also introduced in this activity. The corresponding data set for this activity is found at the following web address: http://www.causeweb.org/repository/ACT/food.txt
This activity focuses on basic ideas of linear regression. It covers creating scatterplots from data, describing the association between two variables, and correlation as a measure of linear association. After this activity students will have the knowledge to create output that yields R-square, the slope and intercept, as well as their interpretations. This activity also covers some of the basics about residual analysis and the fit of the linear regression model in certain settings. The corresponding data set for this activity, 'BAC data', can be found at the following web address: http://www.causeweb.org/repository/ACT/BAC.txt