| cov |
covariance, = sxy |
| e |
error term in regression |
| E( ) |
expected value, = m
|
| L( ) |
likelihood function |
| MLE |
maximum likelihood estimate |
| n |
sample size |
| N |
population size |
| OLS |
ordinary least squares |
| p(x) |
probability distribution |
| p(x,y) |
joint probability distribution |
| P |
sample proportion |
| Pr(E/F) |
conditional probability |
| r |
simple correlation |
| s |
standard deviation |
| s2 |
variance of sample, or residual variance |
| SE |
standard error |
| SS |
sum of squares(variation) |
| var |
variance, = s2 |
| X |
random variable, or regressor in original form |
| x |
realized value of X, or regressor in deviation form |
|
sample mean |
 |
sample median |
| Y |
response in regression |
 |
fitted value of Y |
| Z |
standard normal variable |
 |
is proportional to |
 |
equals, by definition |
 |
approximately equals |
| > |
greater than |
|
a
|
probability of type I error, or population regression intercept |
|
b
|
probability of type II error, or population regression slope |
|
D
|
population mean difference |
|
q
|
any population parameter |
|
m
|
population mean |
|
p
|
population proportion |
|
r
|
population correlation |
|
s2 |
population variance |
|
sXY |
population covariance |
|
S
|
sum of |
|
d
|
partial derivative |
 |
infinity |
|
P
|
product |
 |
integral |