Midterm Flashcards
A measure of the central tendency of a set of random variables
Mean
Measures how widely the values of a variable in a set vary
Variance
Measures how much a set of two variables vary together
Covariance
It is the expectation of the squared deviation of a variable from its mean
Variance
It is their average or expected value
Mean
It is the expectation of the product of the deviations of two variables from their respective means
Covariance
Measures the degree and direction of the relationship between two variables
Correlation
Measures the expected change in one variable per unit change in another variable
Regression
Expected value of a constant =
Constant
Expected value of a random variable =
Sum of all values of random variable/ n
What can you do so that the sum of (xi - mean of x) does not equal zero
Square the deviation
Variance equation
= sum (X^2 - 2X(mean X) + (meanX)^2)
Sample variance equation V(X)
(1/(n-1)) * sum(Xi^2 - 2Xi(meanX) + (meanX)^2)
Sample variance V(aX)
= a^2V(X)
Correlation equation
Cov(X,Y)/ (sqrt V(X)*V(Y))
Doesn’t matter order
Not causative
Unitless
0 = unrelated
Correlation
Order matters
Y on X = X is causing Y
Denominator is always variance of the variable that’s causing the other variable (the second variable)
Regression
Regression equation
= Cov(X,Y) / V(X)
ANOVA means
Analysis of variance
Sums of squares equation
SST=SSR+SSE
SST is the
Total sums of squares
SSR is the
Regression sums of squares
SSE is the
Error sum of squares
Mean Yi is the
Mean of observations for the ith individual