In article <[EMAIL PROTECTED]>, "jackson marshmallow" <[EMAIL PROTECTED]> wrote:
> 1) Two samples of are given and I need to compare their means and variances. > The distribution of the population is unknown. Can I use the F-test and the > t-test? Is it necessary that the sample _means_ have a Gaussian > distribution? Is it sufficient? Maybe I misunderstand something here... Both the t-test and F-test assume samples are from a normal (Gaussian) distribution. The t-test is reasonably robust, i.e., the sample distribution needs to deviate quite a bit from normal before conclusions based on the t-test are likely to be invalid. But, the F-test is more senistive to deviations from normality and probably shouldn't be used if there is reason to suspect the sample distribution isn't normal. > 2) I need to calculate the significance of correlation between two > sequences. I would actually prefer to use randomization, but the sequences > may be too short. Another option is to perform linear regression and > calculate the significance of the slope using a t-test (?). When is it > valid? Linear regression (least squares) assumes a model of the form y_n = m x_n + b + e_n where m and b are the desired regression parameters and e_n is the error associated with observation y_n. Further, it is assumed the e_n are from a normal distribution. It isn't clear to me whether this model is applicable to your problem with sequences. -- To reply via email subtract one hundred nine . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
