Exercise 3 Use the power.t.test() function to estimate the sample sizes for a two-sample t test...

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Exercise 3 Use the power.t.test() function to estimate the sample sizes for a two-sample t test comparing mean CO₂ levels. Use a delta of 5ppm, sig. level (alpha) of 0.05, and power of 0.95. Make two estimates one with the point estimate of the sample variance, and another with the upper confidence limit. In each case, how large should each sample interval be? (remember the "round up" rule) power. t.test(n-NULL, delta-5, sd-sqrt(var (pilot.data)), sig.level-0.05, power-0.95, alternative "two.sided") #why 2-sided? #don't forget the second estimate Write tho Roport YOUR TURN The Problem Is atmosphenc CO₂ concentration increasing? Short term trends can be misleading, so it might be more credible to compare the means of widely separated intervals, using a two-sample t test. How large should those intervals be to get a significant result? Estimate the sample sizes in two steps • use "pilot study data to calculate an interval estimate of variance • use the upper confidence limit of the sample variance to estimate sample sizes Get a Sample Exercise 1 Use the first two years of monthly observations from the coz data frame as your pilot study sample. What is the point estimate of the sample variance? pilot.data <- co2[1:24] var (pilot.data) Bootstrap an Interval Estimate Exercise 2 Exercise 3 Calculate a 95% bootstrap confidence interval type-bca for the pilot.data. Save the upper confidence limit to working variable var.upper. How large is the upper confidence limit in comparison to the point estimate? Bco2var <boot (pilot.data, Boot.Link, R-1000) (varci <boot.ci(Bcozvar, type="bca")); var.upper <varCISbca[5] Use the power.t.test() function to estimate the sample sizes for a two-sample t test comparing mean CO₂ levels. Use a delta of 5ppm, sig.level (alpha) of 0.05, and power of 0.95. Make two estimates one with the point estimate of the sample variance, and another with the upper confidence limit. In each case, how large should each sample interval be? (remember the "round up" rule) power.t.test(n-NULL, delta-5, sd-sqrt(var (pilot.data)), sig.level-0.05, power-0.95, alternative "two-sided") # why 2-sided/ # don't forget the second estimate Exercise 3 Use the power.t.test() function to estimate the sample sizes for a two-sample t test comparing mean CO₂ levels. Use a delta of 5ppm, sig. level (alpha) of 0.05, and power of 0.95. Make two estimates one with the point estimate of the sample variance, and another with the upper confidence limit. In each case, how large should each sample interval be? (remember the "round up" rule) power. t.test(n-NULL, delta-5, sd-sqrt(var (pilot.data)), sig.level-0.05, power-0.95, alternative "two.sided") #why 2-sided? #don't forget the second estimate Write tho Roport YOUR TURN The Problem Is atmosphenc CO₂ concentration increasing? Short term trends can be misleading, so it might be more credible to compare the means of widely separated intervals, using a two-sample t test. How large should those intervals be to get a significant result? Estimate the sample sizes in two steps • use "pilot study data to calculate an interval estimate of variance • use the upper confidence limit of the sample variance to estimate sample sizes Get a Sample Exercise 1 Use the first two years of monthly observations from the coz data frame as your pilot study sample. What is the point estimate of the sample variance? pilot.data <- co2[1:24] var (pilot.data) Bootstrap an Interval Estimate Exercise 2 Exercise 3 Calculate a 95% bootstrap confidence interval type-bca for the pilot.data. Save the upper confidence limit to working variable var.upper. How large is the upper confidence limit in comparison to the point estimate? Bco2var <boot (pilot.data, Boot.Link, R-1000) (varci <boot.ci(Bcozvar, type="bca")); var.upper <varCISbca[5] Use the power.t.test() function to estimate the sample sizes for a two-sample t test comparing mean CO₂ levels. Use a delta of 5ppm, sig.level (alpha) of 0.05, and power of 0.95. Make two estimates one with the point estimate of the sample variance, and another with the upper confidence limit. In each case, how large should each sample interval be? (remember the "round up" rule) power.t.test(n-NULL, delta-5, sd-sqrt(var (pilot.data)), sig.level-0.05, power-0.95, alternative "two-sided") # why 2-sided/ # don't forget the second estimate

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Given that the delta5 the level of significance is5005 the power is095 the alternative is ... View the full answer