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Scatterplot

  • In this activity, students will calculate the correlation between college enrollment at University of New Mexico and unemployment data in the state of New Mexico. They will also generate a scatterplot of the data. 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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  • In this activity, students will calculate the correlation and generate a scatterplot of the number of passengers flying and the number of performed departures from 135 airports in the US. 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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  • 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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  • This tutorial explains in detail how to perform an ANOVA in Excel.
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  • This tutorial explains in detail how to make a scatterplot in Excel.
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  • The applet in this section allows you to see how the T distribution is related to the Standard Normal distribution by calculating probabilities. The T distribution is primarily used to make inferences on a Normal mean when the variance is unknown. If the variance is known inference on the mean can be done using the Standard Normal. The user has a choice of three different probability expressions, then can change the degrees of freedom and the limits of probability. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/TNormal.html
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  • 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.
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  • 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).
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  • This case study addresses the question: "Does the mere presence of a weapon increase the accessibility of aggressive thoughts?" It concerns the following concepts: quantile and box plots, stem and leaf displays, one-sample t test, confidence interval, within-subjects ANOVA, and consequences of violation of normality assumption.
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  • 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. "
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