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! style="background: rgb(238, 238, 238) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial;" colspan="2" | Length | ! style="background: rgb(238, 238, 238) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial;" colspan="2" | Length | ||
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− | + | |<math> | |
− | | <math>\ | + | \begin{array}{lcl} |
− | + | 1 \mbox{ kilometer (km) } & = & 1000 \mbox{ meter (m) } \\ | |
− | + | 1 \mbox{ meter (m) } & = & 109 \mbox{ centimeter (cm) } \\ | |
− | + | 1 \mbox{ centimeter (cm) } & = & 10^{-2} \mbox{ m } \\ | |
+ | 1 \mbox{ millimeter (mm) } & = & 10^{-3} \mbox{ m } \\ | ||
+ | |||
+ | 1 \mbox{ millimicron} ( m \mu ) & = & 10^{-9} \mbox{ m } \\ | ||
+ | 1 \mbox{ angstrom (A) } & = & 10^{-10} \mbox{ m } \\ | ||
+ | \end{array} | ||
+ | </math> | ||
|- | |- | ||
! style="background: rgb(238, 238, 238) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial;" colspan="2" | Surface | ! style="background: rgb(238, 238, 238) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial;" colspan="2" | Surface |
Revision as of 14:41, 23 November 2010
Conversion Factors | |
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Length | |
$ \begin{array}{lcl} 1 \mbox{ kilometer (km) } & = & 1000 \mbox{ meter (m) } \\ 1 \mbox{ meter (m) } & = & 109 \mbox{ centimeter (cm) } \\ 1 \mbox{ centimeter (cm) } & = & 10^{-2} \mbox{ m } \\ 1 \mbox{ millimeter (mm) } & = & 10^{-3} \mbox{ m } \\ 1 \mbox{ millimicron} ( m \mu ) & = & 10^{-9} \mbox{ m } \\ 1 \mbox{ angstrom (A) } & = & 10^{-10} \mbox{ m } \\ \end{array} $ | |
Surface | |
Binomial random variable with parameters n and p | $ \,E[X] = np,\ \ Var(X) = np(1-p)\, $ |
Poisson random variable with parameter $ \lambda $ | $ \,E[X] = \lambda,\ \ Var(X) = \lambda\, $ |
Exponential random variable with parameter $ \lambda $ | $ \,E[X] = \frac{1}{\lambda},\ \ Var(X) = \frac{1}{\lambda^2}\, $ |