Scatterplot

  • Through this activity students will gain an understanding of the effect of the sample size on experiments and simulations. A Microsoft Excel program file and handouts are provided.
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  • 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.
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  • This article presents a dataset containing actual monthly data on computer usage in Best Buy stores from August 1996 to July 2000. This dataset can be used to illustrate time-series forecasting, causal forecasting, simple linear regression, unequal error variances, and variable transformation. Key Words: Model-building; Seasonal Variation.
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  • This article presents data from 1997 Big Ten Conference men's basketball games involving the University of Iowa Hawkeyes. The data can be used to demonstrate bivariate statistical inference techniques such as confidence regions, paired comparisons, and simultaneous confidence intervals. Key Word: Bivariate data; Scatterplot.
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  • This dataset contains a number of variables like birth rate, death rate, life expectancy, and Gross National Product for 97 countries. Suggested activities are geared toward non-mathematicians and include exploratory graphical analyses to answer several central questions. Key words: boxplot, scatterplot, population growth
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  • 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.
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  • A "12 page" tutorial that explores the liner models via excel spreadsheets. The learning module leads the user through various aspects of linear modeling. This tutorial includes a worksheet that allows students to vary the scatter (or noise) level, by adjusting the scroll bar or by clicking on the arrows, to see how the slope and intercept of line respond to the addition of scatter to the data, while monitoring the value of r^2.

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  • Residual plots and other diagnostics are important to deciding whether or not linear regression is appropriate for a set of data. Many students might believe that if the correlation coefficient is strong enough, these diagnostic checks are not important. The data set included in this activity was created to lure students into a situation that looks on the surface to be appropriate for the use of linear regression but is instead based (loosely) on a quadratic function. Key words: regression, residuals
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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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  • In this activity, students will perform a t-test to see if fecal coliform counts collected from Blackwater Creek in Lynchburg, Virginia differ before and after rain showers. Questions about the exercise and links to Excel and TI-83 instructions are given. The data exist in Excel, TI-83, and text formats.
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