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1. Back transforming square root transformed data

If it's root transformed, squaring should give you the original numbers, right?
2. How many standard deviations to determine outliers

There's no formal definition, it's subjective. But 3 sd seems to be a common cutoff.
3. dummy variables in logistic regression

Yes, the problem is that your model can't be specified uniquely, and the regression coefficients can't be calculated. If you included four dummy variables, then the fourth can be expressed as a function of the other three. That means it's impossible to determine the regression coefficients...
4. Interpreting z-score of an Output

Happy to help!
5. Interpreting z-score of an Output

Oh and I'm not sure if this is correct: "However, the Z score does not tell me whether the excess returns are positiv or negative." It does tell you something like that: if it's positive then there are more positive excess returns than negative ones. However, since it doesn't take into account...
6. Interpreting z-score of an Output

If you wonder why one would use a z test over an exact test, part of it is historical: those are simpler to calculate. However there can be other advantages, such as easily being able to calculate a confidence interval or power calculations.
7. Interpreting z-score of an Output

A z table should be fine. A binomial test can be done using an exact test, which uses combinatrics to calculate the p value. However the binomial distribution is approximately normal for large n, with parameters m = np and s^2 = np(1 - p), where n is the number of trials (the number of bonds)...
8. Interpreting z-score of an Output

Completely correct. I assume the 50 percent is chosen because if there's no effect then you would expect half to be higher than zero and half lower, just by chance. But I don't know the context well enough to know if that makes sense.
9. Interpreting z-score of an Output

From what I understand he's worried that the assumptions of the t-test are not met. He says the distribution is "slightly skewed". However, it's not the distribution of the variable that's assumed to be normal but the sample mean. If the distribution of bond bond excess is slight skewed than...
10. test of non-linearity for linear regression

It looks pretty linear to me. The non-constant variance is a violation of the homoscedasticity assumption (there will come a time when I don't need to look up the spelling for that one). But that's not important for determining the regression coefficient, only for when you want to predict...
11. Prior concentration in Bayesian contingency tables

Another question, am I correct in setting the rows fixed? The number of assignments each teacher was expected to grade was fixed, but the outcome was not. That makes sense, right?
12. Prior concentration in Bayesian contingency tables

Hi all! I'm working on a simple project for the uni where I work. I'm trying to find out if teachers grade students differently. I'm looking at an assignment that can only be passed or failed. I thought this might make a nice project to delve into Bayesian statistics. So I found this very...
13. Power analysis for cross-classified model

Hi there, Any help would be greatly appreciated! We have done a study in which a sample of respondents rated a sample of online profiles on their trustworthiness. There were a total of 189 respondents and 259 profiles. So, each profile was rated on average 7.3 times. We are using a...
14. Large Sample Surveys

I think they determine what they consider "meaningful" not from any statistics approach, but from experience and domain knowledge. You can use an online tool to calculate the margin of error, like this one. With an infinite population, the margin of error will be around 0.4%. But since there...
15. Large Sample Surveys

"Meaningful" is not the the same as "statistically significant". In studies with large sample sizes, statistically significant differences may be rather small in practical terms. In this report, the 7% threshold may simply be selected to filter out results "worth paying attention to". Whether 7%...
16. Null Hypothesis

I'm not sure hypothesis testing is the right approach in your situation. The .7 that you set seems kind of arbitrary. What is your rationale for .7? Why not .65 or .75? Maybe you could just report your results descriptively, with confidence intervals around the survey answers. You can then...
17. Shapiro wilk effect size

You might be interested in this Cross Validated thread. Basically, I think the conclusion in that thread is that there is no uniform definition for the deviance from normality, since it can be defined in many ways. I think most textbooks advise not to use the Shapiro Wilk test anyway and just...
18. Null Hypothesis

Hi there, Before we can answer your question, we would need to know more about your study. What do you mean by "the business models in the hotel industry are suited for the current economy"? That's a bit too vague for statistical testing. What did you measure? What was your research design...
19. What type of Analysis is needed?

I'm assuming you can have multiple loyalty cards, each with different companies. Then it seems to me that a logistic regression model for each company might be the way to go.
20. When to give up on a regression model

I think that perhaps you could enter the operating condition as a covariate in a Cox model?