Simple random sampling

From AWF-Wiki
(Difference between revisions)
Jump to: navigation, search
m (→‎Notations used)
(→‎Notations)
 
(22 intermediate revisions by 2 users not shown)
Line 1: Line 1:
−
{{Content Tree|HEADER=Forest Inventory lecturenotes|NAME=Forest Inventory lecturenotes}}
+
{{Ficontent}}
−
==General observations==
+
 
Simple random sampling (SRS) is the basic theoretical [[:Category:Sampling design|sampling technique]].  
 
Simple random sampling (SRS) is the basic theoretical [[:Category:Sampling design|sampling technique]].  
−
The sampling elements are selected as an [[independent random sample]] from the population. Each element of the population has the same probability of being selected. And, likewise, each combination of n sampling elements has the same probability of being eventually selected.
+
The sampling elements are selected as an [[independent random sample]] from the [[population]]. Each element of the population has the same probability of being selected. And, likewise, each combination of ''n'' sampling elements has the same probability of being eventually selected.
  
 
Every possible combination of sampling units from the population has an equal and independent chance of being in the sample.  
 
Every possible combination of sampling units from the population has an equal and independent chance of being in the sample.  
 
   
 
   
−
Simple random sampling is introduced and dealt with here and in sampling textbooks mainly because it is a very instructive way to learn about sampling; many of the underlying concepts can excellently be explained with simple random sampling. However, it is hardly applied in [[Forest inventory|forest inventories]] because there are various other sampling techniques which are more efficient, given the same sampling effort.
+
Simple random sampling is introduced and dealt with here and in sampling textbooks mainly because it is a very instructive way to learn about sampling; many of the underlying concepts can excellently be explained with simple random sampling. However, it is hardly applied in [[Forest inventory|forest inventories]] because there are various other sampling techniques which are more efficient, given the same sampling effort<ref>Kleinn, C. 2007. Lecture Notes for the Teaching Module  Forest Inventory. Department of Forest Inventory and Remote Sensing. Faculty of Forest Science and Forest Ecology,  Georg-August-Universität Göttingen. 164 S.</ref>.
  
 
For information about how exactly sampling units are choosen see [[Random selection]].
 
For information about how exactly sampling units are choosen see [[Random selection]].
  
 +
==Notations==
  
−
==Notations used==
 
  
−
::{| class="wikitable"
+
{| class="wikitable"
 
|-
 
|-
−
!''Estimators''
+
!''Statistic''
−
!''Parametric''
+
!''Parametric value''
−
!''Sample''
+
!''Sample based estimator''
 
|-
 
|-
 
|Mean
 
|Mean
Line 25: Line 24:
 
|Variance
 
|Variance
 
|<math>\sigma^2 = \frac{\sum_{i=1}^N (y_i - \mu)^2}{N}</math>
 
|<math>\sigma^2 = \frac{\sum_{i=1}^N (y_i - \mu)^2}{N}</math>
−
|<math>s^2 = \frac{\sum_{i=1}^n (y_i - \bar {y})^2}{n-1}</math>
+
|<math>S_y^2 = \frac{\sum_{i=1}^n (y_i - \bar {y})^2}{n-1}</math>
 
|-
 
|-
 
|Standard deviation
 
|Standard deviation
 
|<math>\sigma = \sqrt{\frac{\sum_{i=1}^N (y_i - \mu)^2}{N}}</math>
 
|<math>\sigma = \sqrt{\frac{\sum_{i=1}^N (y_i - \mu)^2}{N}}</math>
−
|<math>s = \sqrt{\frac{\sum_{i=1}^n (y_i - \bar {y})^2}{n-1}}</math>
+
|<math>S_y = \sqrt{\frac{\sum_{i=1}^n (y_i - \bar {y})^2}{n-1}}</math>
 
|-
 
|-
 
|Standard error
 
|Standard error
Line 43: Line 42:
  
  
−
::'''Where,'''
+
Where,
−
::<math>N =</math> number of sampling elements in the population (= population size);
+
{|
−
::<math>n =</math> number of sampling elements in the sample (= sample size);
+
−
::<math>y_i =</math> observed value of i-th sampling element;
+
−
::<math>\mu =</math> parametric mean of the population;
+
−
::<math>\bar {y} =</math> estimated mean;
+
−
::<math>\sigma =</math> standard deviation in the population;
+
−
::<math>S =</math> estimated standard deviation in the population;
+
−
::<math>\sigma^2 =</math> parametric variance in the population;
+
−
::<math>s^2 =</math> estimated variance in the population;
+
−
::<math>\sigma_{\bar {y}} =</math> parametric standard error of the mean;
+
−
::<math>s_{\bar {y}} =</math> estimated standard error of the mean.
+
−
 
+
−
==Examples==
+
−
 
+
−
 
+
−
====Example 1:====
+
−
 
+
−
<br> 
+
−
In this section, SRS estimators are illustrated with an example. This example will be pursued through the entire Lecture notes and illustrates that different sampling designs perform differently for the same population and with the same sample size.
+
−
The example population has <math>N = 30</math> individual elements; we may imagine 30 strip plots that cover a forest area (Figure 73). This dataset will also be used in the further chapters for comparison among the performance of different sampling techniques.
+
−
Table 11 lists the values of the 30 units. Here, for SRS, we are only interested in the y values. The <math>x</math> values are a measure for the size (area) of the strips; this will later be used in the context of other estimators.
+
−
From this population we get the following parametric values:
+
−
 
+
−
 
+
−
<blockquote><math>\mu = \frac{\sum_{i=1}^N y_i}{N} = 7.0667</math> and <math>\sigma^2 = \frac{\sum_{i=1}^N (y_i - \mu)^2}{N} = 7.1289</math></blockquote>
+
−
 
+
−
 
+
−
If we take samples of size <math>n=10</math>, then the parametric error variance of the estimated mean is:
+
−
 
+
−
 
+
−
<blockquote> <math>var (\bar {y}) = \frac {N-n}{N-1} * \frac {\sigma^2}{n} = 0.491645</math> </blockquote>
+
−
 
+
−
 
+
−
:[[image:SkriptFig_73.jpg]]
+
−
:'''Figure 73:''' Example population
+
−
 
+
−
 
+
−
::'''Table 1:'''Example Population of N = 30 individual elements
+
−
 
+
−
 
+
−
::{| class="wikitable"
+
 
|-
 
|-
−
!Number
+
| <math>N \!</math> || number of sampling elements in the population (= population size);
−
!y
+
−
!x
+
 
|-
 
|-
−
|1 ||2 ||50
+
| <math>n \!</math> || number of sampling elements in the sample (= sample size);
 
|-
 
|-
−
|2 ||3 ||50
+
| <math>y_i \!</math> || observed value of i-th sampling element;
 
|-
 
|-
−
|3 ||6 ||100
+
| <math>\mu \!</math> || parametric mean of the population;
 
|-
 
|-
−
|4 ||5 ||100
+
| <math>\bar {y} </math> || estimated mean;
 
|-
 
|-
−
|5 ||6 ||125
+
| <math>\sigma \!</math> || standard deviation in the population;
 
|-
 
|-
−
|6 ||8 ||130
+
| <math>S \!</math> || estimated standard deviation in the population;
 
|-
 
|-
−
|7 ||6 ||130
+
| <math>\sigma^2 \!</math> || parametric variance in the population;
 
|-
 
|-
−
|8 ||7 ||140
+
| <math>s^2 \!</math> || estimated variance in the population;
 
|-
 
|-
−
|9 ||8 ||140
+
| <math>\sigma_{\bar {y}} </math> || parametric standard error of the mean;
 
|-
 
|-
−
|10 ||6 ||130
+
| <math>s_{\bar {y}} </math> || estimated standard error of the mean.
−
|-
+
−
|11 ||7 ||140
+
−
|-
+
−
|12 ||7 ||150
+
−
|-
+
−
|13 ||9 ||160
+
−
|-
+
−
|14 ||8 ||170
+
−
|-
+
−
|15 ||10 ||180
+
−
|-
+
−
|16 ||9 ||200
+
−
|-
+
−
|17 ||12 ||210
+
−
|-
+
−
|18 ||8 ||210
+
−
|-
+
−
|19 ||14 ||210
+
−
|-
+
−
|20 ||7 ||200
+
−
|-
+
−
|21 ||12 ||200
+
−
|-
+
−
|22 ||9 ||180
+
−
|-
+
−
|23 ||8 ||160
+
−
|-
+
−
|24 ||6 ||140
+
−
|-
+
−
|25 ||7 ||120
+
−
|-
+
−
|26 ||4 ||90
+
−
|-
+
−
|27 ||5 ||90
+
−
|-
+
−
|28 ||6 ||100
+
−
|-
+
−
|29 ||4 ||100
+
−
|-
+
−
|30 ||3 ||80
+
−
|-
+
−
|'''Mean''' ||'''7.0667''' ||'''13950'''
+
−
|-
+
−
|'''Pop. variance''' ||'''7.1289''' ||'''2087.25'''
+
 
|}
 
|}
−
<br>
 
  
−
This value will be compared in the subsequent chapters with the error variances produced by other sampling techniques with the same sample size <math>n=10</math>. The square root of the error variance is the standard error; and this is the true parametric standard error which we strive to estimate from a single sample then. To recap: the parametric standard error is the standard deviation of ''all possible samples'' of size <math>n=10</math>. In a concrete sampling study, we have only one single sample of size <math>n=10</math> and from this sample the standard error can only be ''estimated''.
 
  
  
 +
{{Exercise
 +
|message=Simple random sampling examples
 +
|alttext=test
 +
|text=2 exercises for this topic
 +
}}
  
−
====Example 2:====
+
=References=
−
 
+
<references/>
−
<br> 
+
−
Let´s take one single sample of <math>n=10</math> from the population of <math>N=30</math> given in Figure 73 and Table 1. Assume that the following elements were randomly selected:
+
−
 
+
−
<br>
+
−
{{construction}}
+
−
<br>
+
  
 
[[Category:Sampling design]]
 
[[Category:Sampling design]]

Latest revision as of 14:35, 26 October 2013

Forest inventory
Introduction to forest inventory
Forest mensuration
Introduction to sampling
Plot design
Sampling design
Estimation issues
Planning issues
UNFCCC Implications for Forest Monitoring
Forest Inventory Examples
  Forest Inventory Glossary

Simple random sampling (SRS) is the basic theoretical sampling technique. The sampling elements are selected as an independent random sample from the population. Each element of the population has the same probability of being selected. And, likewise, each combination of n sampling elements has the same probability of being eventually selected.

Every possible combination of sampling units from the population has an equal and independent chance of being in the sample.

Simple random sampling is introduced and dealt with here and in sampling textbooks mainly because it is a very instructive way to learn about sampling; many of the underlying concepts can excellently be explained with simple random sampling. However, it is hardly applied in forest inventories because there are various other sampling techniques which are more efficient, given the same sampling effort[1].

For information about how exactly sampling units are choosen see Random selection.

[edit] Notations

Statistic Parametric value Sample based estimator
Mean \(\mu = \frac{\sum_{i=1}^N y_i}{N}\) \(\bar {y} = \frac{\sum_{i=1}^n y_i}{n}\)
Variance \(\sigma^2 = \frac{\sum_{i=1}^N (y_i - \mu)^2}{N}\) \(S_y^2 = \frac{\sum_{i=1}^n (y_i - \bar {y})^2}{n-1}\)
Standard deviation \(\sigma = \sqrt{\frac{\sum_{i=1}^N (y_i - \mu)^2}{N}}\) \(S_y = \sqrt{\frac{\sum_{i=1}^n (y_i - \bar {y})^2}{n-1}}\)
Standard error

(without replacement or from a finite population)

\(\sigma_{\bar {y}} = \sqrt{\frac{N-n}{N-1}}*\frac {\sigma}{\sqrt{n}}\) \(S_{\bar {y}} = \sqrt{\frac{N-n}{N}}*\frac{S_y}{\sqrt{n}}\)
Standard error

(with replacement or from an infinite population)

\(\sigma_{\bar {y}} = \frac{\sigma}{\sqrt{n}}\) \(S_{\bar {y}} = \frac{S_y}{\sqrt{n}}\)


Where,

\(N \!\) number of sampling elements in the population (= population size);
\(n \!\) number of sampling elements in the sample (= sample size);
\(y_i \!\) observed value of i-th sampling element;
\(\mu \!\) parametric mean of the population;
\(\bar {y} \) estimated mean;
\(\sigma \!\) standard deviation in the population;
\(S \!\) estimated standard deviation in the population;
\(\sigma^2 \!\) parametric variance in the population;
\(s^2 \!\) estimated variance in the population;
\(\sigma_{\bar {y}} \) parametric standard error of the mean;
\(s_{\bar {y}} \) estimated standard error of the mean.



Exercise.png Simple random sampling examples: 2 exercises for this topic

[edit] References

  1. ↑ Kleinn, C. 2007. Lecture Notes for the Teaching Module Forest Inventory. Department of Forest Inventory and Remote Sensing. Faculty of Forest Science and Forest Ecology, Georg-August-Universität Göttingen. 164 S.
Retrieved from "http://wiki.awf.forst.uni-goettingen.de/wiki/index.php?title=Simple_random_sampling&oldid=9489"
Personal tools
Namespaces

Variants
Actions
Navigation
Development
Toolbox
Print/export