Biological Data Analysis: Exam 2 answers

Here are the answers to exam 2. For some of the questions, I have provided explanatory material in regular type, and the answer in bold; all you need to write down is the answer. If you don't understand why your answer was wrong, you may e-mail me, talk to me before or after class, or set up a time to talk to me in my office. The exam is worth 25% of your grade for the class.

1. One measurement variable, penis length; one nominal variables, food type: one-way anova

2. One measurement variable, flying time; two nominal variables, plugged vs. unplugged vs. Vapo-Rub, and the identity of the individual pigeons (since you have three observations per pigeon): two-way anova, no replication

3. Partitioning the variance helps you decide how to allocate your effort in the big experiment, for example it will help you decide whether to have more rabbits and fewer liver samples per rabbit.

4. One measurement variable, number of food pellets eaten; two nominal variables, joint vs. brownie vs. control, and the pigeon cage: nested anova. Note that because you have several cages per treatment and several pigeons per cage, the identity of the cage is a nominal variable. If there was just one cage per treatment, or one pigeon per cage, then cage would not be a nominal variable.

5. One measurement variable, grip strength; two nominal variables, right vs. left and person (because you have multiple observations per person); each person is found in combination with right and with left: paired t-test or two-way anova without replication.

6. One measurement variable, jumping height; two nominal variables, dog breed and castrated vs. uncastrated; each dog breed is found in combination with castrated and with uncastrated; multiple dogs for each breed/castration combination: two-way anova with replication.

7. You should try transformations and try to find one that makes the data look more normal. Note that if you said you would only use one specific transformation, such as the log transformation, you got a little off; you would need to try different transformations.

8. One measurement variable, melatonin level; one nominal variable, light type; data are heteroscedastic and experiment is unbalanced: Welch's anova.

9. One measurement variable, balance time; two nominal variables, point stared at, and person (since you have multiple observations per person); each person is found in combination with each point stared at; multiple observations per person/point combination: two-way anova with replication.

10. The effect of caffeine on crawling speed was different on different days. Note that the significant interaction does not tell you that caffeine affects speed, or that day affects speed, or that caffeine affects day. Also note that saying that a significant interaction term tells you that there's an interaction was not enough, you had to explain what "interaction" means.

11. Three nominal variables, professor, asleep vs. awake, day: Cochran-Mantel-Haenszel test.

12. One nominal variable, food type; one ranked variable, finishing order: Kruskal-Wallis test.

13. The larger standard error for A. vulgare means you have less confidence in the mean for A. vulgare than for A. nasatum, which means that the estimated mean for A. vulgare is likely to be farther from the true mean than the estimated mean for A. nasatum. The larger standard error could result from a larger standard deviation, a smaller sample size, or both; so if you said the larger standard error resulted just from a larger standard deviation, or just from a smaller sample size, you got points off. And if you said the estimated mean for A. vulgare was definitely further from the true mean than the mean for A. nasatum, you got points off; the larger standard error means that it's must more likely to be further from the true mean.

14. One measurement variable, time rolled up; one nominal variable, humidity (nominal because just two values, 50% and 100%): two-sample t-test or one-way anova.

15. One measurement variable, arsenic content; two nominal variables, region (Newark or Lewes) and random location within each region; multiple individuals from each location: nested anova.

16. Two nominal variables, lure type and fish species; total sample size less than 1000 (about 60 fish for each of 4 lures): Fisher's exact test.

17. One measurement variable, moisture content; two nominal variables, before vs. after the first dose vs. after the second dose, the identity of each person; each person found in combination with each time; one measurement for each combination of time and person: two-way anova without replication.

18. One measurement variable, reflectance of urine; one nominal variable, food; only one measurement per person: one-way anova.

19. With 10 breeds of chicken, there are a lot of pairwise comparisons, so comparing the highest and lowest means has a strong chance of giving a false positive if the null is true. You should have done a one-way anova, followed by the Tukey-Kramer test..

20. One measurement variable, weight; two nominal variables, person and before vs. after; each person is weighed once on each date: paired t-test or two-way anova without replication.

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