Resource Library

The data presented in this article refer to the reliability of ball bearings in manufacturing. Rather than exploring the data to obtain a multiple linear regression solution, a theoretically derived equation is given and the data is used to test it. Key Words: Failure times; Percentiles; Weighted least squares.

This article describes a dataset containing information on 308 diamond stones, which is useful when studying concepts in multiple linear regression analysis. Key Words: Categorical variables; Data transformation; Standardized residuals.

This article provides a data collection and analysis activity for illustrating simple linear regression and outlier analysis. The activity was designed to involve students in the process of data collection and to motivate studying the relationship between two quantitative variables. Students collect data on occurrences of letters in English text. These data are used to study the relationships between how often a letter occurs in English text, and: (1) the letter's Morse Code units and (2) the relative frequency of Scrabbleä‹¢ game tiles for the letter. Worksheets and answers to the activities are provided.

Survival analysis is concerned with studying the time between entry to a study and a subsequent event. This site looks at the Kaplan-Meier survival curve, its method and how to calculate it. It provides exercises as well as answers.

This section of an online textbook discusses the correlation coefficient and illustrated it visually through graphs. It explains calculations as well as how scatter plots can describe data. It covers significance tests for relationships, the Spearman rank correlation and the regression equation. Exercises and answers are included.

This site discusses types of data, stem and leaf plots, mean and median, histograms, and barcharts. Exercises are also provided, as well as their corresponding answers.

This site provides numerous datasets for graphical display topics including linear, exponential, logistic, power rule, periodic, and other bivariate scatterplots, histograms, and other univariate data. Each data set is accompanied with a description, file format options, and a sample graph.

This is a virtual spinner applet, which allows you to change spinner regions and the number of spins. It records the results and displays the data in a histogram.

This Flash applet provides an introduction to simple linear regression for introductory statistics students. It combines a brief narrated animation with an interactive scatterplot function. Students are able to place points on the scatterplot by clicking with a mouse or typing X-Y coordinates. Students use these points to learn about the best fit line by placing a guess on the plot and comparing it with the least squares line. Students also learn about the value of the correlation coefficent and points that would be considered outliers. Students may also specify a value of x (within the range of the data) and obtain the resulting predicted value.

This excerpt from Engineering Statistics Handbook gives a definition for and examples of outliers. A sub-page also discusses Grubbs' Test for Outliers