You can use the correlation analysis tool to examine each pair of measurement variables to determine whether the two measurement variables tend to move together - that is, whether large values of one variable tend to be associated with large values of the other (positive correlation), whether small values of one variable tend to be associated with large values of the other (negative correlation), or whether values of both variables tend to be unrelated (correlation near 0 (zero)). (For example, if the two measurement variables are weight and height, the value of the correlation coefficient is unchanged if weight is converted from pounds to kilograms.) The value of any correlation coefficient must be between -1 and +1 inclusive. The correlation coefficient, like the covariance, is a measure of the extent to which two measurement variables 'vary together.' Unlike the covariance, the correlation coefficient is scaled so that its value is independent of the units in which the two measurement variables are expressed. It provides an output table, a correlation matrix, that shows the value of CORREL (or PEARSON) applied to each possible pair of measurement variables.

(Any missing observation for any subject causes that subject to be ignored in the analysis.) The Correlation analysis tool is particularly useful when there are more than two measurement variables for each of N subjects. The CORREL and PEARSON worksheet functions both calculate the correlation coefficient between two measurement variables when measurements on each variable are observed for each of N subjects. For each of the six possible pairs of pair in the preceding example). For example, in an experiment to measure the height of plants, the plants may be given different brands of fertilizer (for example, A, B, C) and might also be kept at different temperatures (for example, low, high). This analysis tool is useful when data can be classified along two different dimensions. TEST, and the Single Factor Anova model can be called upon instead. With more than two samples, there is no convenient generalization of T. If there are only two samples, you can use the worksheet function T. The analysis provides a test of the hypothesis that each sample is drawn from the same underlying probability distribution against the alternative hypothesis that underlying probability distributions are not the same for all samples.

This tool performs a simple analysis of variance on data for two or more samples. The tool that you should use depends on the number of factors and the number of samples that you have from the populations that you want to test. The Anova analysis tools provide different types of variance analysis.

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