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32 Cards in this Set

  • Front
  • Back
varience in percent means
sq rt of P*(1-P)/N
covarience
1/n-1 * sigma of xi-xm*yi-ym
what factors contribute to observed corrilation of x and y
reserve causality
unboserved heterogeneity
selection bias
n=16 use t or z
t!
if the mean is less than the median then?
skewed to left
beta1=
cov(x,)/var (X) or corr xy * sdy/sdx
bayes theorum: all of it
b|a= a|b*b/a
A= A|B* B+A|not b* not b
when can we use beta?
unbais: cov x,e=0
existence= var x does not equal zero
var e =0 efficency
discreate random variable mean
n*p+n*p+n*p etc
discreate random varaible varience
sigma (xi-xm)^2*pi
random binaomial variance
n*p*(1-p)
random binomial mean
n*p
beta= zero means?
no effect of x on y
centeral tendency?
mean median and mode
var of x,y in solving x-y
sq rt (varx/nx+vary/ny)
how to make OLS more persise?
more observations (greater n)
more varaition in explanatory variable
better fit to the data in the first place
estimate SD
1/4th the range of the data
five number summary?
min 25th median 75th max
chebyshevs theorum
1-1/k^2 of the data is within k SD of the mean
good way to use pie charts?
when placed side by side its easy to see diffference
type 1 error
rejecting the correct thing
type 2 error
accepting something you should reject
varience in beta1
sq rt (vare)/((n-2)*(var x))
difference in a prediction or forecast?
prediction gives average values of y
forecast gives actual values of y
slope of beta
cov x,y/sx^2
y intercept
mean of y- beta1*mean of x
corr
covxy/(sx*sy)
e(fx))=F(e(x))
false
e(a+b*x)= a+B*e(x)
true
whats an estimator
a rule, formula, or method for trying to determine the parameter of a population
binomial variance
n*p*(1-p)
uniform distribution variance
1/12*(x1-x2)^2