A different modeling philosophy attaches more importance to the equal variance assumption when performing t-tests and suggests that the two series in, for example, the logging case have the same...

A different modeling philosophy attaches more importance to the equal variance assumption when performing t-tests and suggests that the two series in, for example, the logging case have the same underlying AR(1) model so that a better estimate, , is (0.573+0.744)/2 = 0.6585. Filter the logging data with this common value for and peform a t.test() using the nondefault equal variance assumption (enlist help(t.test) if needed). Is the result different from the book analysis?