Question

sd(trees$Height) ## [1] 6.371813 length(trees$Height) ## [1] 31 C. Construct the same confidence intervals that you constructed above using the t.test() function in R. Confirm that you get very similar values. D. Suppose this data came from 31 trees cut down by a single logger. How does that affect the conclusions we can draw? Suppose this data came from 31 trees selected at the saw mill from a variety of logging companies, how does that affect the conclusions we can draw? E. Construct 90% Bootstrap T Confidence intervals for ?G, and ?_H (G: Girt, H:Height) from the sample data in the trees data set. Compare these confidence intervals to those you computed in Part B and part C and brainstorm possible reasons reasons for the relationships you noticed. (Note, you will need to include the bootstrap function code [?12 lines starting with “bootstrap=function(...)” from the notes] within your .Rmd file in order for R Markdown to find it when you Knit.) F. Suppose the 31 trees in the trees data set is a simple random sample from those at a saw mill. The mill would like to use this sample to estimate the proportion of trees that they have at their mill with Volume over 65 cubic ft. Use the following code to determine what count of trees in this sample have Volume over 65 cubic ft. Then, explain why they should not do a large-sample z confidence interval for the proportion of trees at their mill with Volume over 65 cubic feet with this sample of 31 trees. (Hint: Consider what assumption for our large sample Z confidence interval for ? is not well met.) sum(trees$Volume>65) ## [1] 1

          sd(trees$Height)
## [1] 6.371813
length(trees$Height)
## [1] 31
C. Construct the same confidence intervals that you constructed above using the t.test() function in R. Confirm that you get very similar values.
D. Suppose this data came from 31 trees cut down by a single logger. How does that affect the conclusions we can draw? Suppose this data came from 31 trees selected at the saw mill from a variety of logging companies, how does that affect the conclusions we can draw?
E. Construct 90% Bootstrap T Confidence intervals for ?G, and ?_H (G: Girt, H:Height) from the sample data in the trees data set. Compare these confidence intervals to those you computed in Part B and part C and brainstorm possible reasons reasons for the relationships you noticed. (Note, you will need to include the bootstrap function code [?12 lines starting with “bootstrap=function(...)” from the notes] within your .Rmd file in order for R Markdown to find it when you Knit.)
F. Suppose the 31 trees in the trees data set is a simple random sample from those at a saw mill. The mill would like to use this sample to estimate the proportion of trees that they have at their mill with Volume over 65 cubic ft. Use the following code to determine what count of trees in this sample have Volume over 65 cubic ft. Then, explain why they should not do a large-sample z confidence interval for the proportion of trees at their mill with Volume over 65 cubic feet with this sample of 31 trees. (Hint: Consider what assumption for our large sample Z confidence interval for ? is not well met.)
sum(trees$Volume>65)
## [1] 1

sd(treesHeight)
## [1] 6.371813
length(treesHeight)
## [1] 31
C. Construct the same confidence intervals that you constructed above using the t.test() function in R. Confirm that you get very similar values.
D. Suppose this data came from 31 trees cut down by a single logger. How does that affect the conclusions we can draw? Suppose this data came from 31 trees selected at the saw mill from a variety of logging companies, how does that affect the conclusions we can draw?
E. Construct 90% Bootstrap T Confidence intervals for ?G, and ?H (G: Girt, H:Height) from the sample data in the trees data set. Compare these confidence intervals to those you computed in Part B and part C and brainstorm possible reasons reasons for the relationships you noticed. (Note, you will need to include the bootstrap function code [?12 lines starting with “bootstrap=function(...)” from the notes] within your .Rmd file in order for R Markdown to find it when you Knit.)
F. Suppose the 31 trees in the trees data set is a simple random sample from those at a saw mill. The mill would like to use this sample to estimate the proportion of trees that they have at their mill with Volume over 65 cubic ft. Use the following code to determine what count of trees in this sample have Volume over 65 cubic ft. Then, explain why they should not do a large-sample z confidence interval for the proportion of trees at their mill with Volume over 65 cubic feet with this sample of 31 trees. (Hint: Consider what assumption for our large sample Z confidence interval for ? is not well met.)
sum(treesVolume>65)
## [1] 1

Added by Pamela G.

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