SOCR ResamplingSimulation Activity

From Socr

(Difference between revisions)
Jump to: navigation, search
(Videos)
 
(5 intermediate revisions not shown)
Line 2: Line 2:
This activity illustrates the processes of sampling, resampling, similation and randomization using the SOCR [http://socr.ucla.edu/htmls/HTML5/SOCR_Resampling_Webapp/ Resampling, Randomization and Simulation Webapp]. It is implemented in HTML5/JavaScript and should be portable on any computer, operating system and web-browser.
This activity illustrates the processes of sampling, resampling, similation and randomization using the SOCR [http://socr.ucla.edu/htmls/HTML5/SOCR_Resampling_Webapp/ Resampling, Randomization and Simulation Webapp]. It is implemented in HTML5/JavaScript and should be portable on any computer, operating system and web-browser.
 +
[[Image:SOCR_ResamplingSimulation_Activity_Fig1.png|150px|thumbnail|right| [http://socr.ucla.edu/htmls/HTML5/SOCR_Resampling_Webapp/ SOCR Resampling Webapp] ]]
==Goals==
==Goals==
Line 11: Line 12:
==Background==
==Background==
-
TBD ...
+
Random (re)sampling applies stochasticity or randomness in the sampling scheme and reflects what is sampled and what the distribution we sample from is. In [[EBook#Chapter_VIII:_Hypothesis_Testing|parametric-based statistical inference]], the random sampling reflects the stochastic nature of selecting observations from the sample space. In contrast, in randomization-based inference (e.g., bootstrapping), the random sampling reflects the resampling and stochastic assignment of units to treatments or groups.
==Requirements & usability==
==Requirements & usability==
A modern web-browser with enabled HTML and JavaScript support is required (mobile devices, tablets and phones should work fine).  
A modern web-browser with enabled HTML and JavaScript support is required (mobile devices, tablets and phones should work fine).  
-
# Go to the [http://socr.ucla.edu/htmls/HTML5/SOCR_Resampling_Webapp/ SOCR Sesampling/Simulation Webapp].
+
# Go to the [http://socr.ucla.edu/htmls/HTML5/SOCR_Resampling_Webapp/ SOCR Resampling/Simulation Webapp].
# Test the webapp
# Test the webapp
# [https://github.com/SOCRedu/Resampling-Randomization-WebApp Report any constructive and critical feedback]
# [https://github.com/SOCRedu/Resampling-Randomization-WebApp Report any constructive and critical feedback]
 +
 +
<center>[[Image:SOCR_ResamplingSimulation_Activity_Fig2.png|400px]]
 +
</center>
==Learning Activity==
==Learning Activity==
Line 24: Line 28:
You can perform single sample or multiple sample based statistical inference using this resource. Let's take a 2-sample case as a specific example where we are looking for group differences. Follow this protocol to get some simulations/results (both for teaching/learning randomization-based inference, or do do real data analysis):
You can perform single sample or multiple sample based statistical inference using this resource. Let's take a 2-sample case as a specific example where we are looking for group differences. Follow this protocol to get some simulations/results (both for teaching/learning randomization-based inference, or do do real data analysis):
-
# You can either generate random data or copy-paste in your own data. For instance you can generate data using coins/cards, etc., or use one of the [[SOCR_Data|SOCR datasets]] (e.g., [[SOCR_Data_Dinov_020108_HeightsWeights| Human Heights/Weights]])  
+
* You can either generate random data or copy-paste in your own data. For instance you can generate data using coins/cards, etc., or use one of the [[SOCR_Data|SOCR datasets]] (e.g., [[SOCR_Data_Dinov_020108_HeightsWeights| Human Heights/Weights]])  
-
# Simulation-Driven Randomization Inference:
+
* Simulation-Driven Randomization Inference:
-
## To use the Coin-Toss experiment to generate data, click “''Binomial Coin Toss''”
+
# To use the Coin-Toss experiment to generate data, click “''Binomial Coin Toss''”
-
## Choose the parameters -- number of coins, probability of Heads, and number of samples (e.g., k=2)
+
# Choose the parameters -- number of coins, probability of Heads, and number of samples (e.g., k=2)
-
## Click “''Generate Dataset''“ (you can click this button multiple times, notice how the data samples change)
+
# Click “''Generate Dataset''“ (you can click this button multiple times, notice how the data samples change)
-
## Click “''Generate Ransom Samples''”
+
# Click “''Generate Ransom Samples''”
-
## Select sample sizes (e.g., 10) and number of repeated samples (e.g., 10,000)
+
# Select sample sizes (e.g., 10) and number of repeated samples (e.g., 10,000)
-
## Click the “RUN” button
+
# Click the “RUN” button
-
## You can inspect all samples (for the k=2 groups) in the right panel of the webapp (use “Show” button and inspect all the glyphs on the top)
+
# You can inspect all samples (for the k=2 groups) in the right panel of the webapp (use “Show” button and inspect all the glyphs on the top)
-
## Then select “Test Statistics”, e.g., p-value, and Click “Infer” button
+
# Then select “Test Statistics”, e.g., p-value, and Click “Infer” button
-
## This will automatically open you the “Inference Plot” tab where the randomization distribution (of p-values) is shown and the initial p_o value is drawn on top to show the relation to the resampling-based distribution.
+
# This will automatically open you the “Inference Plot” tab where the randomization distribution (of p-values) is shown and the initial p_o value is drawn on top to show the relation to the resampling-based distribution.
-
## You can always make modifications of your prior choices in the “Control” tab.
+
# You can always make modifications of your prior choices in the “Control” tab.
-
# Data-Driven Randomization Inference:
+
* Data-Driven Randomization Inference:
-
## Back at the Webapp startup screen select the “Use Excel Datasheet” Option
+
# Back at the Webapp startup screen select the “Use Excel Datasheet” Option
-
## Click the “Reset” button to remove any previous data from the webapp buffer.
+
# Click the “Reset” button to remove any previous data from the webapp buffer.
-
## Copy-paste data from any data-table, For instance from this Heights/Weights dataset: http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_Dinov_020108_HeightsWeights
+
# Copy-paste data from any data-table, For instance from this [[SOCR_Data_Dinov_020108_HeightsWeights|Heights/Weights dataset]].
-
## Let’s select a set of say 20 Weights and click “Use Selected” (this would represent sample 1). Repeat this selection with another set of 20 Weights.
+
# Let’s select a set of say 20 Weights and click “Use Selected” (this would represent sample 1). Repeat this selection with another set of 20 Weights.
-
## Click “Proceed”. You should see a summary indicating the sample-sizes of the 2 groups of data you selected
+
# Click “Proceed”. You should see a summary indicating the sample-sizes of the 2 groups of data you selected
-
## Click “Done” – this will open the “Control” panel
+
# Click “Done” – this will open the “Control” panel
-
## Select sample sizes (e.g., 10) and number of repeated samples (e.g., 10,000)
+
# Select sample sizes (e.g., 10) and number of repeated samples (e.g., 10,000)
-
## Click the “RUN” button
+
# Click the “RUN” button
-
## You can inspect all samples (for the k=2 groups) in the right panel of the webapp (use “Show” button and inspect all the glyphs on the top)
+
# You can inspect all samples (for the k=2 groups) in the right panel of the webapp (use “Show” button and inspect all the glyphs on the top)
-
## Then select “Test Statistics”, e.g., p-value, and Click “Infer” button
+
# Then select “Test Statistics”, e.g., p-value, and Click “Infer” button
-
## This will automatically open you the “Inference Plot” tab where the randomization distribution (of p-values) is shown and the initial p_o value is drawn on top to show the relation to the resampling-based distribution.
+
# This will automatically open you the “Inference Plot” tab where the randomization distribution (of p-values) is shown and the initial p_o value is drawn on top to show the relation to the resampling-based distribution.
-
## You can always make modifications of your prior choices in the “Control” tab.
+
# You can always make modifications of your prior choices in the “Control” tab.
-
# Some new features (e.g., data import from WorldBank and other URLs) will be added it the next 2 weeks
+
* Some new features (e.g., data import from WorldBank and other URLs) will be added it the next 2 weeks
==Practice experiments==
==Practice experiments==
Line 55: Line 59:
==Videos==
==Videos==
-
* [http://socr.ucla.edu/docs/videos/SOCR_RandomizationResamplingVideo_01.mp4 Preliminary screencast (video 01)].
+
* [http://socr.ucla.edu/docs/videos/SOCR_RandomizationResamplingVideo_01.mp4 Early preliminary screencast (Video 1)].
 +
* [http://youtu.be/QVr8EPQakJo screencast (Video 2)].
 +
 
 +
==Documentation==
 +
A detailed [[SOCR_ResamplingSimulation_Docs|documentation of the SOCR Resampling and Simulation Framework is available here]].
==See also==
==See also==

Current revision as of 18:23, 18 May 2017

Contents

SOCR Educational Materials - Activities - SOCR Resampling, Randomization and Simulation Activity

This activity illustrates the processes of sampling, resampling, similation and randomization using the SOCR Resampling, Randomization and Simulation Webapp. It is implemented in HTML5/JavaScript and should be portable on any computer, operating system and web-browser.

Goals

The aims of this activity are to:

  • Demonstrate the concepts of simulation and data generation
  • Illustrate data resampling on a massive scale
  • Reinforce the concept of resampling and randomization based statistical inference
  • Demonstrate the similarities and differences between parametric-based and resampling-based statistical inference

Background

Random (re)sampling applies stochasticity or randomness in the sampling scheme and reflects what is sampled and what the distribution we sample from is. In parametric-based statistical inference, the random sampling reflects the stochastic nature of selecting observations from the sample space. In contrast, in randomization-based inference (e.g., bootstrapping), the random sampling reflects the resampling and stochastic assignment of units to treatments or groups.

Requirements & usability

A modern web-browser with enabled HTML and JavaScript support is required (mobile devices, tablets and phones should work fine).

  1. Go to the SOCR Resampling/Simulation Webapp.
  2. Test the webapp
  3. Report any constructive and critical feedback

Learning Activity

Load the SOCR resampling and randomization webapp in your browser.

You can perform single sample or multiple sample based statistical inference using this resource. Let's take a 2-sample case as a specific example where we are looking for group differences. Follow this protocol to get some simulations/results (both for teaching/learning randomization-based inference, or do do real data analysis):

  • You can either generate random data or copy-paste in your own data. For instance you can generate data using coins/cards, etc., or use one of the SOCR datasets (e.g., Human Heights/Weights)
  • Simulation-Driven Randomization Inference:
  1. To use the Coin-Toss experiment to generate data, click “Binomial Coin Toss
  2. Choose the parameters -- number of coins, probability of Heads, and number of samples (e.g., k=2)
  3. Click “Generate Dataset“ (you can click this button multiple times, notice how the data samples change)
  4. Click “Generate Ransom Samples
  5. Select sample sizes (e.g., 10) and number of repeated samples (e.g., 10,000)
  6. Click the “RUN” button
  7. You can inspect all samples (for the k=2 groups) in the right panel of the webapp (use “Show” button and inspect all the glyphs on the top)
  8. Then select “Test Statistics”, e.g., p-value, and Click “Infer” button
  9. This will automatically open you the “Inference Plot” tab where the randomization distribution (of p-values) is shown and the initial p_o value is drawn on top to show the relation to the resampling-based distribution.
  10. You can always make modifications of your prior choices in the “Control” tab.
  • Data-Driven Randomization Inference:
  1. Back at the Webapp startup screen select the “Use Excel Datasheet” Option
  2. Click the “Reset” button to remove any previous data from the webapp buffer.
  3. Copy-paste data from any data-table, For instance from this Heights/Weights dataset.
  4. Let’s select a set of say 20 Weights and click “Use Selected” (this would represent sample 1). Repeat this selection with another set of 20 Weights.
  5. Click “Proceed”. You should see a summary indicating the sample-sizes of the 2 groups of data you selected
  6. Click “Done” – this will open the “Control” panel
  7. Select sample sizes (e.g., 10) and number of repeated samples (e.g., 10,000)
  8. Click the “RUN” button
  9. You can inspect all samples (for the k=2 groups) in the right panel of the webapp (use “Show” button and inspect all the glyphs on the top)
  10. Then select “Test Statistics”, e.g., p-value, and Click “Infer” button
  11. This will automatically open you the “Inference Plot” tab where the randomization distribution (of p-values) is shown and the initial p_o value is drawn on top to show the relation to the resampling-based distribution.
  12. You can always make modifications of your prior choices in the “Control” tab.
  • Some new features (e.g., data import from WorldBank and other URLs) will be added it the next 2 weeks

Practice experiments

Repeat the protocol above with different (observed or simulated) data, different study designs (e.g., single sample, vs. multiple samples, etc.)

Videos

Documentation

A detailed documentation of the SOCR Resampling and Simulation Framework is available here.

See also

References




Translate this page:

(default)

Deutsch

Español

Français

Italiano

Português

日本語

България

الامارات العربية المتحدة

Suomi

इस भाषा में

Norge

한국어

中文

繁体中文

Русский

Nederlands

Ελληνικά

Hrvatska

Česká republika

Danmark

Polska

România

Sverige

Personal tools