explanatory variable
independent variable; x-axis
response variable
dependent variable; y-axis
correlation coefficient (r)
measures the strength of a linear relationship
r interpretation
There is a (strength), (direction) linear relationship between (variable) and (variable).
lurking variable
“behind the scenes” variable that affects association
LSRL
line that minimizes distance of data points to the line
LSRL equation
y^= a + bx
slope interpretation
For every increase in one (x-context), the model predicts an/a (increase/decrease) of about (slope) in (y-context).
y-int interpretation
When (x=0 context) the predicted value of (y-context) is about (y-int).
residual formula
residual = actual - predicted
residual interpretation
The actual (y-context) was (residual) (above/below) the predicted value for (x-context).
interpolation
predictions made within the range of data
extrapolation
predictions made outside of the data range (unreliable)
s
standard deviation of residuals (typical error)
s interpretation
The actual (y-context) is typically about (S) away from the number predicted by the LSRL.
R²
% improvement in error given an explanatory variable
R² interpretation
About (R²)% of the variability in (y-context) can be explained by the variability in (x-context).
outliers
do not follow linear pattern and have a large residual
high leverage points
substantially larger/smaller x-value than the rest of the data
influential points
any point that will change the LSRL substantially if removed