Exam 1 Cumulative Review

Question 1

cross-sectional

Answer 1

data across different entities from a single period of time

Question 2

time series

Answer 2

data on one thing across different time periods

Question 3

panel data

Answer 3

combination of cross-sectional and time-series

Question 4

probability

Answer 4

the proportion of an outcome is the proportion of time that outcome occurs…

getting heads on a slot machine

Question 5

discrete random variable

Answer 5

can only take on a discrete limited number of values

Question 6

continuous random variable

Answer 6

can take on a continuum of possible values

Question 7

probability distribution

Answer 7

list of all possible values a random variable can take on and the probability that each occurs

Question 8

cumulative probability distribution

Answer 8

probability that your variable is less than or equal to a certain value

Question 9

expected value

Answer 9

the weighted average of all possible outcomes for this variable where the weights are the probabilities of each outcome

Question 10

why do we care about the spread of the data?

Answer 10

when the variance is small, tight, the expected value is more representative of the values in the distribution

Question 11

joint probability distribution

Answer 11

the probability that the random variables simultaneously take on certain values

Question 12

example of 2 random variables

Answer 12

rolling a dice, flipping a coin

Question 13

marginal probability distribution

Answer 13

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

Question 14

conditional distribution

Answer 14

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

Question 15

if the conditional distribution is NO different than the marginal distribution, then the two variables are ___________

Answer 15

independent

Question 16

if the conditional distribution IS different than the marginal distribution, then the two variables are ___________

Answer 16

not independent

Question 17

conditional expectation

Answer 17

the mean of the conditional distribution of Y given X

Question 18

covariance of two random variables

Answer 18

the measure of how much two random variables “move together” or change together

Question 19

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 ______________

Answer 19

large and positive number

Question 20

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 ________

Answer 20

large and positive number

Question 21

if two random variables are independent, their covariances will be…..

Answer 21

zero

Question 22

correlation

Answer 22

measures a relationship between two variables

• always falls between -1 and 1
• if correlation =0, variables are independent

Question 23

what if you add two random variables together?

Answer 23

the expected value of their sums is equal to the sum of their expected values

Question 24

what if you add two random variables variances together?

Answer 24

the variance of the sum is equal to the sum of the variances PLUS two times the covariance

Question 25

if you have a linear function, say y=12,000 + .8x, then the expected value is…

Answer 25

12,000 + .8x

Question 26

z value

Answer 26

a z-value is how many SD’s above or below the mean a score is

Question 27

what do the probabilities in the z-tables show?

Answer 27

what percentage of values fall below (to the left) of a certain point

Question 28

is the sample mean a random variable?

Answer 28

yes, because it can take on lots of values

Question 29

because a sample mean is a random variable, it ….

Answer 29

1. has a probability distribution
2. has an expected value
3. has a variance and a standard deviation

Question 30

variability in a sample average is measured in terms…

Answer 30

of standard error

Question 31

what is meant by error in the term “standard error”?

Answer 31

error doesn’t mean mistake, it means the gap between the sample and the population

Question 32

as the number in your sample gets bigger, the variability of your sample average gets…

Answer 32

smaller, dummy

Question 33

sampling distribution

Answer 33

the distribution of the sample average

Question 34

what are the two ways to look at a sampling distribution?

Answer 34

1. when the population (from which the sample and sample average came) is normally distributed
2. when the population isn’t normally distributed, or when it’s unknown

Question 35

if the population has a normal distribution, then…

Answer 35

then Ῡ does too

Question 36

if the population isn’t normally distributed, the sampling distrubtion ….

Answer 36

will be approximately normally distributed if n is over 30 (which means we can use the z table)