Term
|
Definition
|
|
Term
|
Definition
|
|
Term
| Deterministic relationships |
|
Definition
-every x has a y value
-linear and non-linear |
|
|
Term
|
Definition
y = a + b( x )
a = intercept
b = slope |
|
|
Term
| Probabilistic relationship |
|
Definition
-when there are (possibly) many of y for each x
-linear or non-linear
ex. there are many weights for the height 5'7"
height = x
weight = y |
|
|
Term
| Conditional distributions |
|
Definition
the distribution of y values
ex. weight for "height and weight" because weight is conditional to height |
|
|
Term
|
Definition
| as x increases, y increases |
|
|
Term
|
Definition
| as x increases, y decreases |
|
|
Term
|
Definition
| a measure of the strength of linear association between two variables; strength is represented by how tightly clustered the points are to an imaginary like that runs through the scatterplot; represented by r |
|
|
Term
| Finding correlation coefficient |
|
Definition
1. turn all data points into z-scores
2. multiply Zx by Zy for each pair
3. sum the products of all
4. divide by n - 1 |
|
|
Term
|
Definition
| all of the conditional distributions have the standard deviation as one another |
|
|
Term
| Bivariate normal distribution |
|
Definition
-both x and y distribution are normal distributions (marginal distributions)
-for each value of x, there is a distribution of y values
-as x increases linearly, y values change linearly
-each conditional distribution is a normal distribution
-all conditional distributions have the standard deviation |
|
|
Term
|
Definition
| distribution of x and y variables in a bivariate normal distribution; x-scores and y-scores (according to mean and standard deviation) are normal distributions |
|
|
Term
|
Definition
| depends on p and sample size (n) |
|
|
Term
|
Definition
|
|
Term
|
Definition
| meaning that the correlation between two variables is high because of a third-party variable. |
|
|
