Reading Quiz 7 Flashcards
Random variable
A variable whose value is a numerical outcome of a random phenomenon
Discrete random variable
Random variable which takes on only a finite or countable number of possible values
Probability distribution of a random variable X
Tells the possible values of X and tells how probabilities are assigned to those values
Also called a probability function
Continuous random variable
Takes all values in an interval of numbers
Probability distribution of X
Described by a density curve
Probability of any event is the area under the density curve and above the values of X that make up the event
Probability density curve
Aka density curve
Continuous random variable’s probability distribution is described by this graph
Two important characteristics of density curves
- The area under the graph of a density curve equals 1, corresponding to total probability 1
- The graph of a density curve lies on or above the horizontal axis
Continuous probability for individual outcomes
0 because there is no area above a point
Thus can ignore distinction between > and _> when finding probabilities for continuous (but NOT DISCRETE) random variables
How to picture a probability distribution
Drawing a probability histogram in the discrete case
Graphing the density curve in the continuous case
How to identify random variable of interest
X = number of ——— for discrete random variables X = amount of —–—— for continuous random variables
mean of any discrete random variable
aka expected value
weighted average of the possible outcomes
each outcome weighted by probability
multiply value by probability than add
variance of a discrete random variable
value minus mean quantity squared then times probability, add all
how to find mean and variance on calc
stats calc 1 (L1, L2)
standard deviation
o with line on top
square root of variance
law of large numbers
difference between a sample proportion or mean and a population proportion or mean gets smaller as sample size gets larger
average of values of X observed in many trials must approach μ
rules for means
- if X is random variable and a and b are fixed numbers then μ(a+bX) = a + bμX
- if X and Y are random variables then μ(X+Y) = μX + μY
and μ(X-Y) = μX - μY
rules for variances
- if X is a random variable and a and b are fixed numbers then δsquared(a+bX) = (b^2) (δ^2(X))
- if X and Y are independent (IMPORTANT) random variables then δ^2(X+Y) = δ^2(X) + δ^2(Y) and δ^2(X-Y) = δ^2(X) + δ^2(Y)
called addition rule for variances of independent random variables
addition rule for variances
implies that standard deviations don’t generally add
standard deviations most easily combined by using rules for variances
extra
any linear combination of independent normal random variables is also normally distributed
A random variable is a variable whose value is a ________ of a random phenomenon.
numerical outcome
A random variable with a countable number of possible values is a _____ random variable.
discrete
What is a probability distribution of a discrete random variable?
A. A list of the values the variable can take on, and the probability for each value.
For the probability distribution of a discrete random variable, every probability is between ___ and ___, and the sum of all the probabilities is equal to ___.
0 and 1, 1
In a probability histogram, what quantity do the horizontal and vertical axes represent?
A. The horizontal axis represents the possible values the random variable can take on, and the vertical axis represents the probability of that value.