Posted on Friday, 10th February 2012

Please read sections 10.2-10.3.5 and post a comment.

Posted in Class | Comments (10)

  1. Scott Kennedy Says:
    February 11th, 2012 at 1:53 pm

    In section 10.3.2, what does it mean that T_n is an asymptotically normal estimator?

  2. Eric VanEpps Says:
    February 12th, 2012 at 12:19 pm

    In explaining how p-values are calculated, you mention the use of pseudo-data generated by simulating data from the null hypothesis’s distribution. In practice, how do we simulate this data? Can you provide an example other than the binomial case in class? Thanks.

  3. Rob Rasmussen Says:
    February 12th, 2012 at 4:36 pm

    I am still having trouble after reading the sections with knowing when/how to choose between the different statistical metrics presented.

  4. Yijuan Says:
    February 13th, 2012 at 2:30 pm

    When we use t test for the null hypothesis H0 : μ1 = μ2, we assume σ1 = σ2. But in actual data, the two deviations may not be equal. Do we need to transform data to make them equal? Is there a test to compare σ1 and σ2?

  5. Shubham Debnath Says:
    February 13th, 2012 at 4:12 pm

    If p-values are calculated after sampling randomly under certain conditions, what kind of variability is there in the p-values? Do they follow a particular distribution?

  6. Rich Truncellito Says:
    February 14th, 2012 at 8:38 am

    What happens to the pooled variance when the standard deviations of the two samples are not assumed to be equal?

  7. David Zhou Says:
    February 14th, 2012 at 8:50 am

    Can you go over some non-paradigmatic cases for null hypothesis formulation? What types of samples are they to be used for – all samples where mean is of little concern?

  8. Amanda Markey Says:
    February 14th, 2012 at 8:55 am

    If you can create null hypotheses of distributions, why do we so often test whether means are different (e.g. ANOVA) instead of testing whether two distributions are different?

  9. Jay Scott Says:
    February 14th, 2012 at 8:59 am

    Terminology clarifications on resampling (jackknifing, bootstrapping) when it concerns pseudo-data:

    1. Are bootstrap values considered a kind of pseudo-data?

    2. Is sampling / resampling of pseudo-data sets such as those discussed on pp. 295-297 a kind of bootstrapping? or does it have its own nomenclature?

  10. Matt Bauman Says:
    February 14th, 2012 at 9:00 am

    In the definition of S_pooled, I was curious where the -2 term came from. When pooling n data sets, does this therm become -n?

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