Resource assessment exercises: basic statistics additional exercises
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Take a sample of size <math>n=100</math> from <code>trees</code> and name this sample <code>t.sample</code>. This sample will be used for the exercises. | Take a sample of size <math>n=100</math> from <code>trees</code> and name this sample <code>t.sample</code>. This sample will be used for the exercises. | ||
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# Estimate the required sample size for a relative standard error of 1%. | # Estimate the required sample size for a relative standard error of 1%. | ||
# Estimate the proportion of oak trees. Provide confidence intervals for your estimate. | # Estimate the proportion of oak trees. Provide confidence intervals for your estimate. | ||
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+ | ==Related articles== | ||
+ | * Previous article: [[Resource assessment exercises: estimating a proportion|Estimating a proportion]] | ||
+ | * Next article: [[Resource assessment exercises: Introduction to response designs|Introduction to response designs]] | ||
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+ | [[category:Resource assessment basics in R (2014)|Additional exercises]] |
Latest revision as of 15:38, 23 June 2014
- This article is part of the Resource assessment exercises. See the category page for a (chronological) table of contents.
Take a sample of size \(n=100\) from trees
and name this sample t.sample
. This sample will be used for the exercises.
- What is the maximum and minimum value for
dbh
in you sample? - Estimate the mean, variance, standard deviation, and coefficient of variation for the variable
height
. - Estimate the standard error of the mean for the variable
dbh
(without applying the fpc). How wide is the confidence interval when we set \(\alpha=0.01\)? - Test whether there is a difference in the estimate of the standard error when you apply the fpc.
- Estimate the required sample size for a relative standard error of 1%.
- Estimate the proportion of oak trees. Provide confidence intervals for your estimate.
[edit] Related articles
- Previous article: Estimating a proportion
- Next article: Introduction to response designs