4.3 residuals and outliers

Question 1

importance of avoiding extrapolation

Answer 1

the line can be determine whether to use the least-squares regression line in predicting selling price

Question 2

never try predicting the y-value for an x-value that is outside the range of data

Answer 2

this is because we don’t know if the least-squares regression line is linear outside the given range

Question 3

extrapolation

Answer 3

making predictions for values outside the data; leads to unreliable predictions

Question 4

residual (y-yhat)

Answer 4

the difference between the predicted point on the y-hat line and the y-value of the point

Question 5

residual plot (x, y-yhat)

Answer 5

a plot in which the residuals are plotted against the values of the explanatory variable x

Question 6

when there is a curved or any type of pattern

Answer 6

dont use the least-squares regression line because that means theres no linear relationship

Question 7

influential point

Answer 7

an outlier that causes a big shift in the position of the line when included in the scatterplot

Question 8

when influential points are present

Answer 8

its good practice to show the least-squares regression lines both including and excluding the influential points