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  • This presentation was given by Aneta Siemiginowska at the 4th International X-ray Astronomy School (2005), held at the Harvard-Smithsonian Center for Astrophysics in Cambridge, MA.  

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  • This page will perform the procedure for up to k=12 sample values of r, with a minimum of k=2. It will also perform a chi-square test for the homogeneity of the k values of r, with df=k-1. The several values of r can be regarded as coming from the same population only if the observed chi-square value proves the be non-significant.

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  • This page performs a Kolmogorov-Smirnov "Goodness of Fit" test for categorical data. Users enter observed frequencies and expected frequencies for up to 8 mutually exclusive categories. The applet returns the critical values for the .05 and .01 levels of significance.

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  • This page will calculate the 0.95 and 0.99 confidence intervals for rho, based on the Fisher r-to-z transformation. To perform the calculations, enter the values of r and n in the designated places, then click the "Calculate" button. Note that the confidence interval of rho is symmetrical around the observed r only with large values of n.

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  • Using the Fisher r-to-z transformation, this page will calculate a value of z that can be applied to assess the significance of the difference between r, the correlation observed within a sample of size n and rho, the correlation hypothesized to exist within the population of bivariate values from which the sample is randomly drawn. If r is greater than rho, the resulting value of z will have a positive sign; if r is smaller than rho, the sign of z will be negative.

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  • This page will calculate the value of chi-square for a one- dimensional "goodness of fit" test, for up to 8 mutually exclusive categories labeled A through H. To enter an observed cell frequency, click the cursor into the appropriate cell, then type in the value. Expected values can be entered as either frequencies or proportions. Toward the bottom of the page is an option for estimating the relevant probability via Monte Carlo simulation of the multinomial sampling distribution.

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  • In the first simulation, random samples of size n are drawn from the population one sample at a time. With df=3, the critical value of chi-square for significance at or beyond the 0.05 level is 7.815; hence, any calculated value of chi-square equal to or greater than 7.815 is recorded as "significant," while any value smaller than that is noted as "non-significant." The second simulation does the same thing, except that it draws random samples 100 at a time. The Power of the Chi-Square "Goodness of Fit" Test pertains to the questionable common practice of accepting the null hypothesis upon failing to find a significant result in a one- dimensional chi-square test.

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  • As the page opens, you will be prompted to enter the sizes of your several samples. If you are starting out with raw (unranked) data, the necessary rank- ordering will be performed automatically.

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  • A statistics scramble that might be used in teaching goodness-of-fit significance tests. A set of five anagrams must be solved to reveal the letters that provide the answer to the clue in the cartoon. The cartoon was drawn by British cartoonist John Landers based on an idea by Dennis Pearl. Free for use on course websites, or as an in-class, or out-of class exercise.

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  • This is my take on the ubiquitous M&Ms counting activity. Each student records the color proportions in a fun-size bag of M&Ms. We pool the class data and run a Chi-Square goodness-of-fit test to determine whether or not the color proportions match those claimed on the manufacturer's website. We consistently find that the proportions do not match. The blue M&Ms, in particular, are underrepresented. This activity also includes a review of the 1-proportion z confidence interval.

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