SOCR EduMaterials FunctorActivities Bernoulli Distributions

(Difference between revisions)

Revision as of 02:42, 9 January 2008

Description: You can access the applets for the above distributions at http://www.socr.ucla.edu/htmls/SOCR_DistributionFunctors.html .

Exercise 1: Use SOCR to graph the MGF's and print the following distributions and answer the questions below. Also, comment on the shape of each one of these distributions:
- a. $X \sim Bernoulli(0.5)$
- b. $X \sim Binomial(1,0.5)$
- c. $X \sim Geometric(0.5)$
- d. $X \sim NegativeBinomial(1, 0.5)$

Below you can see a snapshot of the MGF of the distribution of $X \sim Bernoulli(0.8)$

Do you notice any similarities between the graphs of these MGF's between any of these distributions?

Exercise 2: Use SOCR to graph and print the MGF of the distribution of a geometric random variable with $p = 0.2, p = 0.7$ . What is the shape of this function? What happens when $p$ is large? What happens when $p$ is small?

Exercise 3: You learned in class about the properties of MGF's If $X 1,... X n$ are iid. and $Y = \sum_{i=1}^n X_i.$ then $M_{y}(t) = {[M_{X_1}(t)]}^n$ .

Revision as of 02:38, 9 January 2008 (view source) Rgidwani (Talk \| contribs) ← Older edit		Revision as of 02:42, 9 January 2008 (view source) Rgidwani (Talk \| contribs) Newer edit →
Line 16:		Line 16:
	* '''Exercise 2:''' Use SOCR to graph and print the MGF of the distribution of a geometric random variable with <math> p=0.2, p=0.7 </math>. What is the shape of this function? What happens when <math> p </math> is large? What happens when <math> p </math> is small?		* '''Exercise 2:''' Use SOCR to graph and print the MGF of the distribution of a geometric random variable with <math> p=0.2, p=0.7 </math>. What is the shape of this function? What happens when <math> p </math> is large? What happens when <math> p </math> is small?

-	* '''Exercise 3:''' You learned in class about the properties of MGF's If <math> X_1, ...X_n are iid. and Y = \sum_{i=1}^n X_i. </math> then	+	* '''Exercise 3:''' You learned in class about the properties of MGF's If <math> X_1, ...X_n</math> are iid. and <math>Y = \sum_{i=1}^n X_i. </math> then <math>M_{y}(t) = {[M_{X_1}(t)]}^n</math>.