AP Statistics Curriculum 2007 Beta

From Socr

(Difference between revisions)
Jump to: navigation, search
(Beta Distribution)
(Beta Distribution)
Line 18: Line 18:
*<math>\Beta_x(\alpha,\beta)=\int_0^x t^{\alpha-1}(1-t)^{\beta-1}dt</math>
*<math>\textstyle\Beta_x(\alpha,\beta)=\int_0^x t^{\alpha-1}(1-t)^{\beta-1}dt</math>
*<math>\Beta(\alpha,\beta)=\int_0^1 t^{\alpha-1}(1-t)^{\beta-1}dt</math>
*<math>\textstyle\Beta(\alpha,\beta)=\int_0^1 t^{\alpha-1}(1-t)^{\beta-1}dt</math>
<br />'''Moment generating function''': The Beta moment-generating function is
<br />'''Moment generating function''': The Beta moment-generating function is

Revision as of 20:55, 11 July 2011

Beta Distribution

Definition: Beta distribution is a distribution that models events which are constrained to take place within an interval defined by a minimum and maximum value.

Probability density function: For X\sim Beta(\alpha,\beta)\!, the Beta probability density function is given by



  • α is a positive shape parameter
  • β is a positive shape parameter
  • \textstyle\Beta(\alpha,\beta)=\int_0^1 t^{\alpha-1}(1-t)^{\beta-1}dt or
\textstyle\Beta(\alpha,\beta)=\frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}, where \Gamma(k)!=(k-1)!=1 \times 2 \times\ 3 \times\cdots \times (k-1)
  • x is a random variable

Cumulative density function: Beta cumulative distribution function is given by



  • \textstyle\Beta_x(\alpha,\beta)=\int_0^x t^{\alpha-1}(1-t)^{\beta-1}dt
  • \textstyle\Beta(\alpha,\beta)=\int_0^1 t^{\alpha-1}(1-t)^{\beta-1}dt

Moment generating function: The Beta moment-generating function is

M(t)=1+\sum_{k=1}^\infty (\prod_{r=0}^{k-1}\frac{\alpha+r}{\alpha+\beta+r})\frac{t^k}{k!}

Expectation: The expected value of a Beta distributed random variable x is


Variance: The Beta variance is



The Beta distribution is used in a range of disciplines including rule of succession, Bayesian statistics, and task duration modeling. Examples of events that may be modeled by Beta distribution include:

  • The time it takes to complete a task
  • The proportion of defective items in a shipment


Suppose that DVDs in a certain shipment are defective with a Beta distribution with α=2 and β=5. Compute the probability that the shipment has 20% to 30% defective DVDs.

We can compute this as follows:

P(0.2\le X\le 0.3)=\sum_{x=0.2}^{0.3}\frac{x^{2-1}(1-x)^{5-1}}{\Beta(2,5)}=0.235185

The figure below shows this result using SOCR distributions

Personal tools