Introduction to Statistics - Revision history

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	==See also==		==See also==
	* [[~~Topic:~~Statistics]]		* [[Statistics]]
	* [[Introduction to research]]		* [[Introduction to research]]
	* [[Statistical Economics]]		* [[Statistical Economics]]
	* [[~~Topic:~~Actuarial mathematics]]		* [[Actuarial mathematics]]
	* [[Introduction to Likelihood Theory]]		* [[Introduction to Likelihood Theory]]
	* [[Introduction to Classical Statistics]]		* [[Introduction to Classical Statistics]]

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	* [[Introduction to Likelihood Theory]]		* [[Introduction to Likelihood Theory]]
	* [[Introduction to Classical Statistics]]		* [[Introduction to Classical Statistics]]
	* [[~~Wikiversity~~:Statistics 202]]		* [[Wikademia:Statistics 202]]
	* [[Introduction to probability and statistics]]		* [[Introduction to probability and statistics]]
	* [[Bayesian Statistics]]		* [[Bayesian Statistics]]
	* [[Statistics for Business Decisions]]		* [[Statistics for Business Decisions]]
	* [[~~Wikiversity~~:Statistics]]		* [[Wikademia:Statistics]]

	[[Category:Mathematics]]		[[Category:Mathematics]]

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	==Examples==		==Examples==
	*46% of people polled enjoy vanilla, while 54% prefer chocolate (+/-4% margin of error).		*46% of people polled enjoy vanilla, while 54% prefer chocolate (+/-4% margin of error).

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	==Examples==		==Examples==
	*46% of people polled enjoy vanilla, while 54% prefer chocolate (+/-4% margin of error).		*46% of people polled enjoy vanilla, while 54% prefer chocolate (+/-4% margin of error).

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	~~{{nav\|School:Mathematics/Undergraduate/Probability and Statistics}}~~

	==Examples==		==Examples==
	*46% of people polled enjoy vanilla, while 54% prefer chocolate (+/-4% margin of error).		*46% of people polled enjoy vanilla, while 54% prefer chocolate (+/-4% margin of error).

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130.15.147.159: /* Discrete and Continuous Random Variables */

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Discrete and Continuous Random Variables

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	Discrete random variables take only countably many values. It means that we can list the set of all possible values that a discrete random variable can take, or in other words, the number of possible values in the set that the variable can take is finite. If the possible values that a DRV X can take are a0,a1,a2,...an, the probability that X takes each is p0=P(X=a0), p1=P(X=a1), p2=P(X=a2),...pn=P(X=an). All these probabilites are greater than or equal zero.		Discrete random variables take only countably many values. It means that we can list the set of all possible values that a discrete random variable can take, or in other words, the number of possible values in the set that the variable can take is finite. If the possible values that a DRV X can take are a0,a1,a2,...an, the probability that X takes each is p0=P(X=a0), p1=P(X=a1), p2=P(X=a2),...pn=P(X=an). All these probabilites are greater than or equal zero.

	For continuous random variables, we cannot list all possible values that a continuous variable can take because the number of values it can take is extremely large. It means that there is no use ~~to calculate~~ the probability of each value separately because the probability that the variable takes a particular value is extremely small and can be considered zero P(X=x)=0).		For continuous random variables, we cannot list all possible values that a continuous variable can take because the number of values it can take is extremely large. It means that there is no use in calculating the probability of each value separately because the probability that the variable takes a particular value is extremely small and can be considered zero: P(X=x)=0.

	== Distribution Functions ==		== Distribution Functions ==

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76.116.138.238: /* Bayes' Law */

2008-07-06T03:01:42Z

Bayes' Law

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	Since <math> P(A\cap B) = P(B\cap A) </math>,<br>		Since <math> P(A\cap B) = P(B\cap A) </math>,<br>
	<math>\frac{P(A\|B)}{P(B)} = \frac{P(B\|A)}{P(A)}</math>.<br>		<math>\frac{P(A\|B)}{P(B)} = \frac{P(B\|A)}{P(A)}</math>.<br>
	A Simple rearrangement of above line gives us '''Bayes' Law'''.		A Simple rearrangement of above line gives us '''Bayes' Law'''.<br><br>
	For a more detailed explanation, see http://en.wikipedia.org/wiki/Bayes'_theorem		For a more detailed explanation, see http://en.wikipedia.org/wiki/Bayes'_theorem