Numbers Final

Confounding Factor

third variable that leads to distorted results/ association

Spurious Relationship

you think theres a relationship between two variables but there isn’t in reality

Multiple regression

when you run regression with multiple variables

Omitted variable bias

bias that comes out of results when you fail to include confounding factors

Bivariate Regression

analyzes relationship between two variables

Multiple Regression

analyzes relationship between multiple independent variables

Predicted values

make prediction about the value of the outcome

Estimated effects

how big are effects of different predictors?

Components of causation

association, elimination of alternatives, and prior occurence

Observational studies

data gathered by observing the real world

Experimental studies

identify group of subjects, randomly assign treatment, control group,

internal validity

effect on independent variable on dependent variable from confounding factors

external validity

results in the “lab” reflect in the real world