Recall that a simple linear regression model describes the relationship between one independent (or explanatory) variable, and one dependent (or response) variable. In the example:
Car Value = 21375 − 1215 × Age (in years)
We can interpret the relationship between the variables as:
“For every 1 year increase in a car's age, the car's value decreases by .”
Generally, a simple linear regression that fits the data well is helpful in predicting the relationship between two variables. However, a simple linear regression often does not tell the whole story. Further, there are many reasons that a correlation between two variables cannot be used to infer causation.