nicholasjhorton
commented
6 years ago I don’t have access to the book but wonder why samples of size 100 are taken here. Is this still a hypothetical sampling distribution or is the goal to bootstrap an interval?
All the best,
Nick
On Jul 3, 2018, at 11:30 AM, Margaret Chien notifications@github.com wrote:
In Chapter 14, there are two instances in which a 95% confidence interval is found for bootstrap means. Figure 14.11 (page 455): library(mosaic) library(readr) library(janitor) Babies <- read_csv("http://nhorton.people.amherst.edu/is5/data/Babysamp_98.csv") %>% clean_names() set.seed(1243536) numsim <- 10000 bootstrapmeans <- do(numsim) * mean(~ weight, data = mosaic::resample(Babies, 100)) gf_histogram(~ mean, data = bootstrapmeans) %>% gf_labs(x = "Bootstrap Means", y = "")
Figure 14.11
bootstraplm <- lm(mean ~ 1, data = bootstrapmeans) confint(bootstraplm)
Example (page 456): Commute <- read_csv("http://nhorton.people.amherst.edu/is5/data/Population_Commute_Times.csv") %>% clean_names() set.seed(134) numsim <- 10000 commutebootstrap <- do(numsim) * mean(~ commute_time, data = mosaic::resample(Commute, 100)) gf_histogram(~ mean, data = commutebootstrap, title = "Time Bootstrap Set Means") %>% gf_labs(x = "", y = "") cbootlm <- lm(mean ~ 1, data = commutebootstrap) confint(cbootlm)
I've tried both confint() and t.test(), but the intervals are a lot narrower than the ones in the book. I tried multiple seeds, but they were all similar.
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mchien20
amywagaman
In Chapter 14, there are two instances in which a 95% confidence interval is found for bootstrap means. Figure 14.11 (page 455): library(mosaic) library(readr) library(janitor) Babies <- read_csv("http://nhorton.people.amherst.edu/is5/data/Babysamp_98.csv") %>% clean_names() set.seed(1243536) numsim <- 10000 bootstrapmeans <- do(numsim) * mean(~ weight, data = mosaic::resample(Babies, 100)) gf_histogram(~ mean, data = bootstrapmeans) %>% gf_labs(x = "Bootstrap Means", y = "")
Figure 14.11
bootstraplm <- lm(mean ~ 1, data = bootstrapmeans) confint(bootstraplm)
Example (page 456): Commute <- read_csv("http://nhorton.people.amherst.edu/is5/data/Population_Commute_Times.csv") %>% clean_names() set.seed(134) numsim <- 10000 commutebootstrap <- do(numsim) * mean(~ commute_time, data = mosaic::resample(Commute, 100)) gf_histogram(~ mean, data = commutebootstrap, title = "Time Bootstrap Set Means") %>% gf_labs(x = "", y = "") cbootlm <- lm(mean ~ 1, data = commutebootstrap) confint(cbootlm)
I've tried both confint() and t.test(), but the intervals are a lot narrower than the ones in the book. I tried multiple seeds, but they were all similar.