Final answer:
Residuals or errors in a regression equation are the differences between the observed values of y and the predicted values from the regression line. The error for a given value is found by subtracting the predicted value from the observed value for that specific data point.
Step-by-step explanation:
When we discuss the residuals or errors in the context of a regression equation, we are talking about the differences between the actual observed values of the dependent variable (y) and the values predicted by the regression line (ŷ). This can be represented as e<sub>i</sub> = y<sub>i</sub> - ŷ<sub>i</sub>, where e<sub>i</sub> denotes the error for each value of x corresponding to each y. The regression equation is a formula used to predict the dependent variable based on the independent variable x.
In the given equation y = 7 2x (assuming the intended equation is y = 72x), errors are obtained by subtracting the prediction made by this equation from the actual observed value for a particular x. Assuming we have the actual y values, we would proceed by plugging in each value of x into the equation to get the predicted y (ŷ), and then find the residual by subtracting ŷ from the actual y.