This applet lets you explore the effect of violations of the assumptions of normality and homogeneity of variance on the type I error rate and power of t tests (and two-group analysis of variance).
In this demonstration a scatterplot is displayed and you draw in a regression line by hand. You can then compare your line to the best least squares fit. You can also try to guess the value of Pearson's correlation coefficient.
As described on the page itself: "The simulation shows a scatterplot of data from a bivariate distribution in which the relationship between the two variables is linear. You can change the "input" values of slope, standard error of the estimate, or standard deviation of X for this data sample, and see the effects of your change. "
This site gives an explanation, a definition and an example of probability models. Topics include components of probability models and the basic rules of probability.
This applet demonstrates probability as the area under the normal and the standard normal curves. Students can manipulate mean, standard deviation, and lower and upper bounds to find probabilities.
An explanation of scatter plots, their use, purpose and interpretation. It provides examples of the various relationships described by scatter plots as well as case studies and related techniques.
