When conducting an ANOVA, it's important to realize that the test in and of itself will not tell which of the means in the null hypothesis are different from each other. The only thing we will know is whether or not all the means are equal or if one or more of the means are different. If you want to know which mean or means are different from the others you must perform one of the post hoc tests. You can find more information about these with a Google search on the topic.

Suppose you need to take a log transformation on set of data that is non-linear in nature.  You can tell this from a scatterplot.  Take ln (natural logarithm) of each x value and y value to complete the transformation. In doing so, you will get some errors in x for ln(0) and negative values for ln of values between 0 and 1. So to compensate for that add 1 to each x value before taking natural log. Then you will eliminate such problems and can get the regression equation and scatterplot accordingly

If we are now testing to see if two variances are equal, as compared to where we wanted to see if the variance equaled a certain value, we use an F test instead of a Chi-square test.  So the test statistic is an F statistic  equal to s1^2/s2^2 and we compare to an F critical value found in any F chart. The decision rule is reject if F falls in the rejection region and do not reject if F is not in the rejection region.  The hypotheses for this test would be Ho: variance1 = variance2,  Ha: variance1 does not equal variance2