1. Use R to create a histogram, boxplot, and normal quantile-quantile plot for a sample data set, then choose an appropriate transformation and plot a histogram, boxplot, and normal quantile-quantile plot of your transformed data values.
2. Use R’s t.test command to calculate a confidence interval for a population mean, then give a
proper interpretation of the interval.
3. Read parts of two journal articles and interpret two of the values given in the articles.

Part 1

investigated nighttime bat activity for several species of bat. In one part of the study, the authors investigated a possible relationship between moonlight intensity and bat activity. They looked at several species, but here we’ll look only at the results for Pteronotus parnellii. Bat activity was recorded by automatic sensors on a number of nights. The counts for P. parnellii are given in the file s2040_W20_batcounts.

For this part of the assignment:

• Plot a histogram, boxplot, and normal quantile-quantile plot of the bat passes. Properly label the axes. Put all 3 plots on one page. Briefly comment on the shape of the distribution.
• Many statistical procedures require the assumption of a normally distributed population, and often this assumption is clearly violated. But sometimes we can find a transformation of the data that results in data that is approximately normal. Common transformations include the log (log(x)), or various power transformations such as the reciprocal ( 1x), square root (√x), or square (x2). We might consider more exotic options (e.g. x3.17 or log(x + 10)), but we usually stick with the more common ones when they work reasonably well. Sometimes the nature of the data suggests a certain transformation, and sometimes we use mathematical techniques to suggest one, but often we simply wing it a little, and try a few different ones and see if we can find one that results in our assumptions being satisfied.

For this part of the assignment, find a transformation that results in transformed bat counts that are approximately normally distributed. Try a few different ones – I think you can find a good one! Plot a histogram, boxplot, and normal quantile-quantile plot of the transformed values. Properly label your plots. Put all 3 plots on one page.

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