Difference between revisions of "SOCR EduMaterials Activities JointDistributions"
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== This is an activity to explore the joint distributions of X and Y through two simple examples. == | == This is an activity to explore the joint distributions of X and Y through two simple examples. == | ||
| − | * '''Description''': You can access the applets for the | + | * '''Description''': You can access the applets for the following experiments at [http://www.socr.ucla.edu/htmls/SOCR_Experiments.html SOCR Experiments] |
* '''Exercise 1''': Die coin experiment: | * '''Exercise 1''': Die coin experiment: | ||
| − | A die is rolled and the number observed <math> X </math> is recorded. Then a coin is tossed number of times equal to the value of <math>X</math | + | A die is rolled and the number observed <math> X </math> is recorded. Then a coin is tossed number of times equal to the value of <math>X</math>. For example if <math>X=2</math> then the coin is tossed twice, etc. Let <math>Y</math> be the number of heads observed. Note: Assume that the die and the coin are fair. |
**'''1.''' Construct the joint probability distribution of <math>X</math> and <math>Y</math>. | **'''1.''' Construct the joint probability distribution of <math>X</math> and <math>Y</math>. | ||
**'''2.''' Find the conditional expected value of <math>Y</math> given <math>X=5</math>. | **'''2.''' Find the conditional expected value of <math>Y</math> given <math>X=5</math>. | ||
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Below you can see a snapshot of the theoretical distribution of <math> Y </math>. | Below you can see a snapshot of the theoretical distribution of <math> Y </math>. | ||
| + | <center>[[Image: SOCR_Activities_Christou_christou_diecoin.jpg|600px]]</center> | ||
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Below you can see a snapshot of the theoretical distribution of <math> Y </math>. | Below you can see a snapshot of the theoretical distribution of <math> Y </math>. | ||
| + | <center>[[Image: SOCR_Activities_Christou_christou_coindie.jpg|600px]]</center> | ||
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* SOCR Home page: http://www.socr.ucla.edu | * SOCR Home page: http://www.socr.ucla.edu | ||
| − | {{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title= | + | {{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=SOCR_EduMaterials_Activities_JointDistributions}} |
Latest revision as of 22:12, 28 December 2006
This is an activity to explore the joint distributions of X and Y through two simple examples.
- Description: You can access the applets for the following experiments at SOCR Experiments
- Exercise 1: Die coin experiment:
A die is rolled and the number observed \( X \) is recorded. Then a coin is tossed number of times equal to the value of \(X\). For example if \(X=2\) then the coin is tossed twice, etc. Let \(Y\) be the number of heads observed. Note: Assume that the die and the coin are fair.
- 1. Construct the joint probability distribution of \(X\) and \(Y\).
- 2. Find the conditional expected value of \(Y\) given \(X=5\).
- 3. Find the conditional variance of \(Y\) given \(X=5\).
- 4. Find the expected value of \(Y\).
- 5. Find the standard deviation of \(Y\).
- 6. Graph the probability distribution of \(Y\).
- 7. Use SOCR to graph and print the empirical distribution of \(Y\) when the experiment is performed
- a. \(n=1000\) times.
- b. \(n=10000\) times.
- 8. Compare the theoretical mean and standard deviation of \(Y\) (parts (4) and (5)) with the empirical mean and standard deviation found in part (8).
Below you can see a snapshot of the theoretical distribution of \( Y \).
- Exercise 2: Coin Die experiment:
A coin is tossed and if heads is observed then a red die is rolled. If tails is observed then a green die is rolled. You can choose the distribution of each die as well as the probability of heads. Choose for the red die the 3-4 flat distribution and for the green die the skewed right distribution. Finally using the scroll button choose $p=0.2$ as the probability of heads. Let \(X\) be the score of the coin (1 for heads, 0 for tails), and let \(Y\) be the score of the die (1,2,3,4,5,6).
- 1. Construct the joint probability distribution of \(X, Y\).
- 2. Find the marginal probability distribution of \(Y\) and verify that it is the same with the one given in the applet.
- 3. Compute \(E(Y)\).
- 4. Compute \(E(Y)\) using expectation by conditioning \(E[E(Y|X)]\).
- 5. Run the experiment 1000 times take a snapshot and comment on the results.
Below you can see a snapshot of the theoretical distribution of \( Y \).
- SOCR Home page: http://www.socr.ucla.edu
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