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The relationship between algebra and statistics may not be immediately apparent. In algebra, you learn how to change an expression from y equals a function of x to x equals a function of y. This ability to transform equations by the rules of algebra is very important in statistics. The standard textbooks in statistics provide equations identifying how to calculate the mean and standard deviation. Generally, from this point, the ability of these statistics based on a limited sample size, to infer (or suggest) properties of the population is introduced. The rules of algebra are used to transform the equation which provides confidence intervals given a sample size to one that provides the sample size given a confidence interval. Similarly, in hypothesis testing, algebra is used again. I can be given a certain level of significance, and decide whether to accept (fail to reject) or reject the null hypothesis. Or, the same equations can be transformed to identify what level of significance is needed to accept the null hypothesis. Algebra is required to understand the relationships between equations. You can think of statistic equations of a series of building blocks, and with algebra you can understand how one equation is derived from another. Not only algebra, but many other areas of mathematics (geometry, trigonometry and calculus) are used in statistics.

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