Topics for Test 1
... c. Some basic logical identities that might help you:
P( (AB)’ ) = P( A’ B’ )
P( (AB)’ ) = P( A’B’ )
d. Remember :
P ( “at least one....” ) = 1 - P(“none”)
P(“neither A nor B “ ) means P(A’ B’)
e. Often probabilities are easiest to figure out if
if you make a chart or ...
Applied Probability Lecture 2
... • Before this we talked about “Probabilities” of events and
sets of events where in many cases we hand selected the
set of fine grain events that made up an event whose
probability we were seeking.
p (x)Now we move onto another
more interesting way to group this point: using a function
to ascribe va ...
Moments of a Random Variable, Moment Generating Function
... For a random variable X , the skewness of X is defined to
be E [(X − µ)3 ]/σ 3 . If skewness is positive, we say the
distribution is skewed to the right. If it is negative, we say
the distribution is skewed to the left.
In probability and statistics, a random variable, aleatory variable or stochastic variable is a variable whose value is subject to variations due to chance (i.e. randomness, in a mathematical sense). A random variable can take on a set of possible different values (similarly to other mathematical variables), each with an associated probability, in contrast to other mathematical variables.A random variable's possible values might represent the possible outcomes of a yet-to-be-performed experiment, or the possible outcomes of a past experiment whose already-existing value is uncertain (for example, due to imprecise measurements or quantum uncertainty). They may also conceptually represent either the results of an ""objectively"" random process (such as rolling a die) or the ""subjective"" randomness that results from incomplete knowledge of a quantity. The meaning of the probabilities assigned to the potential values of a random variable is not part of probability theory itself but is instead related to philosophical arguments over the interpretation of probability. The mathematics works the same regardless of the particular interpretation in use.The mathematical function describing the possible values of a random variable and their associated probabilities is known as a probability distribution. Random variables can be discrete, that is, taking any of a specified finite or countable list of values, endowed with a probability mass function, characteristic of a probability distribution; or continuous, taking any numerical value in an interval or collection of intervals, via a probability density function that is characteristic of a probability distribution; or a mixture of both types. The realizations of a random variable, that is, the results of randomly choosing values according to the variable's probability distribution function, are called random variates.The formal mathematical treatment of random variables is a topic in probability theory. In that context, a random variable is understood as a function defined on a sample space whose outputs are numerical values.