LearningActivities ColorBlindness

From Socr

(Difference between revisions)
Jump to: navigation, search
(Hands-on Activity)
(Overview)
Line 4: Line 4:
This [[LearningActivities| Distributome Activity]] illustrates an application of probability theory to study Colorblindness.
This [[LearningActivities| Distributome Activity]] illustrates an application of probability theory to study Colorblindness.
-
Colorblindness results from an abnormality on the X chromosome. The condition is thus rarer in women since a woman would need to have the abnormality on both of her X chromosomes in order to be colorblind (whether a woman has the abnormality on one X chromosome is essentially independent of having it on the other).  
+
[http://en.wikipedia.org/wiki/Color_blindness Colorblindness] results from an abnormality on the X chromosome. The condition is thus rarer in women since a woman would need to have the abnormality on both of her X chromosomes in order to be colorblind (whether a woman has the abnormality on one X chromosome is essentially independent of having it on the other).
===Goals===
===Goals===

Revision as of 00:04, 25 October 2011

Contents

Distributome Learning Activities - Distributome Colorblindness Activity

Overview

This Distributome Activity illustrates an application of probability theory to study Colorblindness.

Colorblindness results from an abnormality on the X chromosome. The condition is thus rarer in women since a woman would need to have the abnormality on both of her X chromosomes in order to be colorblind (whether a woman has the abnormality on one X chromosome is essentially independent of having it on the other).

Goals

The goal of this activity is to demonstrate an efficient protocol of estimating the probability that a randomly chosen individual may be colorblind.

Hands-on Activity

Suppose that p is the probability that a randomly selected man is colorblind.

  • 100 men are selected at random. What is the distribution of Xm = the number of these men that are colorblind?
Xm~Binomial(100,p).
  • 100 women are selected at random. What is the distribution of Xf = the number of these women that are colorblind?
Hint: the chance that an individual woman is colorblind is p2, why?
Solution: Xf~Binomial(100,p2)
  • To estimate the probability that a randomly selected woman is colorblind, you might use the proportion of colorblind women in a sample of n women. What is the variance of this estimator?
Xf~Binomial(n,p2). Thus Var(\frac{X_f}{n})=\frac{p^2(1-p^2)}{n}.
  • Alternatively, to estimate the probability that a randomly selected woman is colorblind, you might use the square of the proportion of colorblind men in a sample of n men. Explain why this estimate makes sense. What is the variance of this estimator?
Hint: The moment generating function can be used to find the fourth moment about the origin.
Hint: We want to estimate p2 and \frac{X_m}{n} estimates p so it makes sense to use (\frac{X_m}{n})^2 as the estimator (in fact it will be the maximum likelihood estimate). We have Var[( \frac{X_m}{n} )^2 ] = n^{-4}[E(X_m^4 ) - (E(X_m^2 ))^2 ]. Take q = 1 − p. Then the fourth moment about the origin of a binomial is E(X4) = np(q − 6pq2 + 7npq − 11np2q + 6n2p2q + n3p3) and the second moment is E(X2) = np(q + np). Thus Var[( \frac{X_m}{n} )^2 ] = n^{-3}(pq + 6(n-1)p^2q^2 + 4n(n-1)p^3q).
  1. For large samples, is it better to use a sample of men or a sample of women to estimate the probability that a randomly selected women is colorblind? Explain.
Hint: Show that a normal approximation is valid for both and then compare the variances.
Solution: For large n the ratio of the variances for the estimate in part c to the estimate in part d is \frac{Var(\frac{X_f}{n})}{Var((\frac{X_m}{n})^2 )} \sim \frac{p^2(1-p^2)}{4p^3q} = \frac{1+ p}{4p}. When this ratio is greater than 1, the estimator based on the sample of men will be better. Since this happens for any p < \frac{1}{3}, which is clearly the case for colorblindness, it is better to use a sample of men to estimate the probability that a random woman is colorblind.

Conclusions

You can also use the delta method to find the approximate variance for the estimator above.




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