# Search results

1. ### Ratio of sizes data

This was my thought after reading your first post. I Think that would be reasonable to use ANCOVA to model Golgi size (volume, area, or however you intended) as a function of genotype after accounting for the covariate cell area/volume (again whichever size measurement you planned to use). Is...
2. ### Logistic Regression Models Without Main Effects?

Another note is that you will have to work with your data to determine which method of relieving collinearity is best. Centering may work in some cases and note in others, depending on the variable, whereas a ridge regression may be worth while in other cases.
3. ### Logistic Regression Models Without Main Effects?

Also, there are ways to handle collinearity if you need to make inferences on the beta estimates (ridge regression, possible centering, partialling out a variable you don't care about, dimension reduction). It is also not advisable, for estimation purposes, to exclude variables that are...
4. ### Logistic Regression Models Without Main Effects?

I don't think it's very reasonable to exclude main effects. By definition, if the interaction is important, you've specified that the variables are important for illustrating the relationship accurately. As a general principle, it's not good to test main effects or lower order terms after a...
5. ### Immortality & Bayesian Statistics

This is different than improbable. Now, you're saying H=>~X (if Hypothesis is true, then we won't see X). The contrapositive is true: X => ~H (If we see X, then H is not true). However, the converse isn't necessarily true. That is, you can't say ~X => H (if we don't see X, H is true)...
6. ### Immortality & Bayesian Statistics

For someone who says he isn't much of a typist, you've done an awful lot of typing... Also, I believe I saw a prior argument: "17. For dummies: a. The likelihood of a "red state" to elect Candidate X is 10%. b. State A elects Candidate X. c. State A is probably not a "red...
7. ### Dependent t-test with extra information provided

It's pretty odd that many of them don't! I have noticed that it is more commonly overlooked when the book is not written by a statistician, but even when it is, it isn't necessarily spelled out for the reader in words. Try running the test with the different hypothesis set up and let's see...
8. ### Dependent t-test with extra information provided

The problem is that this is a common misunderstanding, including in biomedical research. A few things: 1. Failing to reject Ho does not allow someone to conclude Ho. The methodology doesn't allow for that. This kind of hypothesis testing cannot provide evidence FOR a null hypothesis, only...
9. ### Interaction term with categorical variables with more than 2 levels

Why are you artificially creating categorical variables?
10. ### Dependent t-test with extra information provided

If you fail to reject Ho, will you conclude the measurements are normal, on average?
11. ### Dependent t-test with extra information provided

I’m not sure why you conducted the test that way. Additionally, it seems you need review in these areas. I would recommend this before giving instructions on this topic. Edited due to autocorrect.
12. ### Dependent t-test with extra information provided

It is a two tailed p value that SPSS spit out, but a p value doesn’t represent the probability of a result < 20 in your example. A p value is the probability of receiving a summary measure at least as extreme as the observed one, assuming the null hypothesis is true. Your null hypothesis, as...
13. ### Dependent t-test with extra information provided

I agree with Dason, based on what you've written in both posts.
14. ### Seek proper test

I don't think there's anything wrong with including the median in that case, necessarily. You're illustrating a measure of central tendency. I think including a histogram of the observed values may be best since the median, range, and shape can be seen, perhaps with an inset feature. Generalized...
15. ### Dependent t-test with extra information provided

Ho wouldn't be < 20 in this case. It would need to be greater than or equal to 20 if you wanted a rejection of Ho to tell you the difference is normal (<20). It looks like this set your null hypothesis to Ho mu(diff)=20 with a two-tail test. Depending whether you subtracted before from after or...
16. ### Dependent t-test with extra information provided

The only thing you need to do with the paired test is figure about normal and abnormal differences and use that to set the null and alternative in a similar manner we discussed earlier.
17. ### Dependent t-test with extra information provided

Right. You just said you don't want to look at mean differences (can be for dependent samples t test), only an after measurement. Maybe we're not on the same page.
18. ### Dependent t-test with extra information provided

Sorry, I thought you were describing the difference of "up to 20". You also mentioned "comparing before and after" which also sent me down that road. Sure just test mean of the after measurement instead of mean difference. Ho: Mu(after) less than or equal to 30 Ha: Mu(after) greater than 30...
19. ### Dependent t-test with extra information provided

Why not set the null hypothesis to Ho: Mu(diff after minus before) less than or equal to 30 Ha: Mu (diff after minus before) greater than 30 If you reject Ho you conclude the mean measurement after is abnormal at the selected alpha level. You could also set it to Ho: Mu(diff after minus...

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