AP Statistics Curriculum 2007 Limits Norm2Poisson

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<center>[[Image: SOCR_Activities_ExploreDistributions_Christou_figure11.jpg|600px]]</center>
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* '''Note''': We observe that this distribution is bell-shaped.  We can use the normal distribution to approximate this probability.  Using <math> N(\mu=50, \sigma=\sqrt{50}=7.071) </math>, together with the continuity correction for better approximation we obtain <math> P(54 \le X \le 62)=0.2718 </math>, which is close to the exact that was found earlier.  The figure below shows this probability using hte [http://socr.ucla.edu/htmls/SOCR_Distributions.html SOCR Normal Distribution Applet].
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* '''Note''': We observe that this distribution is bell-shaped.  We can use the normal distribution to approximate this probability.  Using <math> N(\mu=50, \sigma=\sqrt{50}=7.071) </math>, together with the continuity correction for better approximation we obtain <math> P(54 \le X \le 62)=0.2718 </math>, which is close to the exact that was found earlier.  The figure below shows this probability using the [http://socr.ucla.edu/htmls/SOCR_Distributions.html SOCR Normal Distribution Applet].
<center>[[Image: SOCR_Activities_ExploreDistributions_Christou_figure12.jpg|600px]]</center>
<center>[[Image: SOCR_Activities_ExploreDistributions_Christou_figure12.jpg|600px]]</center>

Revision as of 20:15, 28 June 2010

Contents

General Advance-Placement (AP) Statistics Curriculum - Normal Approximation to Poisson Distribution

Normal Approximation to Poisson Distribution

The Poisson(λ) Distribution can be approximated with Normal when λ is large.

For sufficiently large values of λ, (say λ>1,000), the Normal(μ = λ,σ2 = λ) Distribution is an excellent approximation to the Poisson(λ) Distribution. If λ is greater than about 10, then the Normal Distribution is a good approximation if an appropriate continuity correction is performed.

If xo is a non-negative integer, X\sim Poisson(\lambda) and U\sim Normal(\mu=\lambda, \sigma^2=\lambda), then PX(X < xo) = PU(U < xo + 0.5).

Examples

Suppose cars arrive at a parking lot at a rate of 50 per hour. Let’s assume that the process is a Poisson random variable with λ = 50. Compute the probability that in the next hour the number of cars that arrive at this parking lot will be between 54 and 62. We can compute this as follows:  P(54 \le X \le 62) = \sum_{x=54}^{62} \frac{50^x e^{-50}}{x!}=0.2617. The figure below from the SOCR Poisson Distribution shows this probability.

  • Note: We observe that this distribution is bell-shaped. We can use the normal distribution to approximate this probability. Using  N(\mu=50, \sigma=\sqrt{50}=7.071) , together with the continuity correction for better approximation we obtain  P(54 \le X \le 62)=0.2718 , which is close to the exact that was found earlier. The figure below shows this probability using the SOCR Normal Distribution Applet.

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