Exam 1 Cumulative Review Flashcards
cross-sectional
data across different entities from a single period of time
time series
data on one thing across different time periods
panel data
combination of cross-sectional and time-series
probability
the proportion of an outcome is the proportion of time that outcome occurs…
getting heads on a slot machine
discrete random variable
can only take on a discrete limited number of values
continuous random variable
can take on a continuum of possible values
probability distribution
list of all possible values a random variable can take on and the probability that each occurs
cumulative probability distribution
probability that your variable is less than or equal to a certain value
expected value
the weighted average of all possible outcomes for this variable where the weights are the probabilities of each outcome
why do we care about the spread of the data?
when the variance is small, tight, the expected value is more representative of the values in the distribution
joint probability distribution
the probability that the random variables simultaneously take on certain values
example of 2 random variables
rolling a dice, flipping a coin
marginal probability distribution
This term is used to distinguish the distribution of Y alone from the joint distribution of Y and another random variable with regards to two random variables
conditional distribution
the distribution of a random variable Y conditional on another random variable X taking on a specific value
ex: given that you are batting .320, what’s the probability your salary is xxxxx
if the conditional distribution is NO different than the marginal distribution, then the two variables are ___________
independent
if the conditional distribution IS different than the marginal distribution, then the two variables are ___________
not independent
conditional expectation
the mean of the conditional distribution of Y given X
covariance of two random variables
the measure of how much two random variables “move together” or change together
if X is much bigger than it’s expected value, and Y is much bigger than it’s expected value…then the covariance will be a ______________
large and positive number
If X is much smaller than it’s expected value,
and Y is much smaller than it’s expected value,
then the covariance will be a ________
large and positive number
if two random variables are independent, their covariances will be…..
zero
correlation
measures a relationship between two variables
- always falls between -1 and 1
- if correlation =0, variables are independent
what if you add two random variables together?
the expected value of their sums is equal to the sum of their expected values
what if you add two random variables variances together?
the variance of the sum is equal to the sum of the variances PLUS two times the covariance
