Regression

  • Using a parameter it's possible to represent a property of an entire population with a single number instead of millions of individual data points. There are a number of possible parameters to choose from such as the median, mode, or interquartile range. Each is calculated in a different manner and illuminates the data from a different point of view. The mean is one of the most useful and widely used and helps us understand populations. A population is simulated by generating 10,000 floating point random numbers between 0 and 10. Sample means are displayed in histograms and analyzed.
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  • This applet allows a person to add up to 50 points onto its green viewing screen. After each point is added by clicking on the screen with the mouse, a red line will appear. This red line represents a line passing through (Average x, Average y) with a slope that can be altered by clicking the Left or Right buttons. The slope of this line may also be changed by dragging the mouse either right or left. By clicking on Show Best Fit, a blue best fit line will be calculated by the computer.

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  • A mathematical word processor that includes an easy-to-use computer algebra system (MuPad). Products include Scientific Wokplace, Scientific Word, Scientific Notebook, and MuPad Pro. Student version are available.

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  • This program visualizes the effects of outliers to regression lines. The user may pick up a point with the mouse and move it across the chart. The resulting regression line is automatically adjusted after each movement, showing the effect in an immediate and impressive way. The program Leverage allows one to experiment with the leverage effect. You can create a random sample of data noisy points on a line. Dragging one of the points away from the regression line immediately shows the effect, as the regression line is recalculated and moves according to the current data set. Not online: user has to download the program.

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  • This program has been written to explore the relationship between the data points and the error surface of the regression problem. On one hand you can learn how to represent a line in two different spaces ({x,y} and {k,d}), and on the other hand you see that solving the regression problem is nothing else than finding the minimum in the error surface.

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  • Each dataset in this collection includes description of the study, description of the data file, statistical topic covered, and reference. Topics addressed include: correlation, one-way ANOVA, Bonferroni multiple comparison procedure, regression (simple, multiple, and loglinear), chi-square, and the t-test.
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  • A collection of applets addressing data analysis, sampling distribution simulations, and probability and inference. Some can be used individually, though others require context from the textbook.

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  • This module contains discussions on two and three dimensional graphs, histograms, scatterplots, boxplots, and data visualization, and provides links to a variety of relevant activities.
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  • The Journal of Statistics Education has published this collection of datasets and related articles describing their use, submitted by faculty members from numerous institutions. Data is in .dat format.
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  • In this activity, students will generate scatterplots and use regression and logarithms to explore a dataset with time and temperature data for an insulation pack. Questions about the exercise are given at the bottom of the page as well as links to instructions for Excel and the TI-83 calculator. The data exists in TI-83 group, Excel, and text formats.
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