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  • Gives some examples of two sample proportions. Shows how to answer using formulas and minitab.
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  • Gives four practice problems on the t-test. Gives both the data sets and the mean and standard deviations if you did not want to compute them. Requires students to interpret and reason through some of their answers.
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  • Gives a basic explanation with diagrams of the one and two-tailed t-tests.
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  • Gives very detailed explanation of t-tests (confidence intervals, one-sample, two sample independent, two sample paired, pooled and unpooled variances). Discusses the assumptions that are made for each type of t-test. This topic is part of an online textbook.
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  • Textbook-like example showing the independent t-test. Gives a nice way for students to think through the problem and interpret results.
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  • Gives textbook-like explanation with some real-life data to compute a t-test and determine if two population means are equal. Also has some links for case studies and a web-based program called Dataplot. There is a printer-friendly version on the main homepage (see source).
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  • This page gives a short background on Student's t-test and provides three t-test calculators. Two perform t-tests for independent groups and one performs t-tests for matched pairs. Users type in individual data points or copy and paste the entire data set. Some examples are given for demonstration.
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  • This particular textbook lesson addresses the independent t-test. It presents to the user how to compute the t statistic and then how to interpret the results.
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  • A slideshow presentation with two good examples on using the Z-test for the difference between two means. Gives some good "plain language" interepretations of what "statistically significant difference" means.
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  • This chapter of the HyperStat Online Textbook presents six sections on probability including conditional probability, unions, intersections, and the Binomial distribution. A link to exercises for the chapter is available, as well as a link to an applet that demonstrates the approximation of the binomial with a normal distribution (in section 5).
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