In simple regression, we analyze the relationship between a predictor (the attribute we think to be the cause) and the criterion (the attribute we think is the consequence). There are two very important parameters (among others) that result from a regression analysis:
The intercept: This is the average value of the criterion when the predictor is 0, which is when the effect of the predictor is partialed out
The slope coefficient: This indicates by how many units, on average, the criterion changes (with reference to the intercept) when the predictor increases by one unit
Regression seeks to obtain the values that explain the relationship the best, but such a model only seldom reflects the relationship entirely. Indeed, measurement error, but also attributes that are not included in the analysis affect also the data. The residuals express the deviation of the observed data points to the model. Its value is the vertical distance from a point to the regression line...