bellingcat/RS4OSINT
1
0
Fork 0
mirror of https://github.com/bellingcat/RS4OSINT.git synced 2026-06-12 21:48:37 +03:00
Code Issues Packages Projects Releases Wiki Activity

Add link to chapters/C3_Blast.html

Browse Source
This commit is contained in:
Galen Reich
2024-06-11 15:09:00 +01:00
committed by GitHub
parent 51b06c5188
commit c4f4098210
1 changed files with 2 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/chapters/C3_Blast.html
View File

@@ -371,7 +371,7 @@ gtag('config', 'G-RK9ZLZQ6GL', { 'anonymize_ip': true});
<li><span class="math inline">\(s^2\)</span>: Sample Standard Deviation</li> <li><span class="math inline">\(s^2\)</span>: Sample Standard Deviation</li>
<li><span class="math inline">\(n\)</span>: Number of observations</li> <li><span class="math inline">\(n\)</span>: Number of observations</li>
</ul> </ul>
<p>This procedure gives us a number called a t-value, which is a measure of how many standard deviations the difference between the two means is. We’re not going to get into the details here, but a rule of thumb is that if the t-value is greater than 2, then the difference between the two means is significant. If the t-value is less than 2, then the difference is not significant. We’re going to calculate the t-value for each pixel in the image to determine whether that pixel has changed significantly following the event in question. You don’t need to know the details of the t-test to understand the results (but hopefully you’ve got an intuition for what it’s doing). If you’re interested in learning more about statistical tests of this sort, I teach a course on Data Science at the University College London, and have made all of the lectures and courseware open-source. The T-test lecture is here.</p> <p>This procedure gives us a number called a t-value, which is a measure of how many standard deviations the difference between the two means is. We’re not going to get into the details here, but a rule of thumb is that if the t-value is greater than 2, then the difference between the two means is significant. If the t-value is less than 2, then the difference is not significant. We’re going to calculate the t-value for each pixel in the image to determine whether that pixel has changed significantly following the event in question. You don’t need to know the details of the t-test to understand the results (but hopefully you’ve got an intuition for what it’s doing). If you’re interested in learning more about statistical tests of this sort, I teach a course on Data Science at the University College London, and have made all of the lectures and courseware open-source. <a href="https://oballinger.github.io/QM2/notebooks/W07.%20Hypothesis%20Testing.html">The T-test lecture is here.</a></p>
</section> </section>
<section id="implementing-a-t-test-in-earth-engine" class="level2"> <section id="implementing-a-t-test-in-earth-engine" class="level2">
<h2 class="anchored" data-anchor-id="implementing-a-t-test-in-earth-engine">Implementing a t-test in Earth Engine</h2> <h2 class="anchored" data-anchor-id="implementing-a-t-test-in-earth-engine">Implementing a t-test in Earth Engine</h2>