# Search results

1. ### Curving qq plot: What does this mean?

The bottom line is more a matter of whether your model predicts well enough to be useful. If it does, minor violations don't really matter. If it does not, you should investigate the sub-populations and include them in your model. To illustrate, we use a virtual catapult to teach design of...
2. ### Curving qq plot: What does this mean?

Reliability analysis is just analyzing the time to an event such as a change in status, so it would be appropriate in your situation. The "failure mode" means that there are distinct subgroups where the distribution of time to a status change is different. In survival analysis, this would...
3. ### Curving qq plot: What does this mean?

If I were to see a plot such as this in a reliability analysis, I would conclude that I had a mixture of 4-5 different failure modes. Dog legs (straight segments with bends) indicate a mixture and each line segment a different failure mode.
4. ### ANOVA analysis of two site management types

2-sided would be used if the OP was looking for any difference, but in this case the OP was looking for a greater than difference, which is 1-sided.
5. ### t.test and organizing data

I am not an R user, but those who are usually like to see your code posted, so they can see if it is a coding issue.
6. ### ANOVA analysis of two site management types

You appear to have only two levels (control and treatment), so you can use a 2-sample t-test. A 1-way ANOVA is typically used for 3 or more levels. Since your alternate hypothesis is that a treated field has more protein, this would be a 1-sided test.
7. ### paired vs independent, which one

Paired (dependent) is where you are evaluating the same subjects before and after (repeated measures). Independent is where you are measuring different groups of subjects.
8. ### Sample Size n=5

This is pure speculation, but the following was very commonplace before calculators. A sample size of 5 was used because A) it was an odd number and made it easy to determine the median, and B) it made it easy to calculate the mean by hand (add the numbers, multiply by two and divide by 10)...
9. ### Skewness and Kurtosis of a t-distribution

Actually, that would be correct for the first one. My company uses a lot of electronic components. Component suppliers will sell different levels of precision for components, but can't actually produce different levels of precision, so they sort them. If you order a high precision component...
10. ### Skewness and Kurtosis of a t-distribution

@spunky Do you recognize these "normal" distributions? No, not cauchy.
11. ### What statistic method should I use for this situation?

Are you referring to a surge test to detect insulation defects (such as in armature windings)? When you say the results are not reproducible, do you mean that there is a measurement error problem, or that the surge problem occurs infrequently in the product?
12. ### Calculating minimum sample size

It depends on both the approximate drop rate and the acceptable margin of error. Since there are 100 possibilities, I used 0.01 as a planning value for multiple margin of error options.
13. ### Which is the correct test to use?

You didn't say whether you also have a treatment, but this could be analyzed as a single factor (patient) repeated measures ANOVA.
14. ### Which is the correct test to use?

Does each row represent one patient? And the value in each cell is the standard deviation for one day?
15. ### Statistically significant disagreement between beekeepers

You could go with an ordinal logistic regression. Temperature as a continuous predictor, pesticide ban as a categorical predictor and mite concentration as the ordinal response.
16. ### Statistically significant disagreement between beekeepers

Regarding correlation of ordinal data, you are correct. Pearson's should not be used for ordinal data, but Spearman's may be used. The real question is whether correlation is the correct analysis. Can you provide more information on your hypothesis? What is the other variable with which you...
17. ### randomization and nested designs

This looks good to me.