the value of a bicycle was recorded over a period of five years. based on how the plot of the residuals…

the value of a bicycle was recorded over a period of five years. based on how the plot of the residuals might look, would a linear function that passes through (0,250) and (4,25) be a good fit for the data shown? bicycle prices value of bicycle (in dollars) 300 250 200 150 100 50 0 1 2 3 4 5 x age of bicycle (in years) no, the residuals would tend to be one - sided. yes, the residuals would be randomly distributed. no, the residuals would be randomly distributed. yes, the residuals would tend to be one - sided.

the value of a bicycle was recorded over a period of five years. based on how the plot of the residuals might look, would a linear function that passes through (0,250) and (4,25) be a good fit for the data shown? bicycle prices value of bicycle (in dollars) 300 250 200 150 100 50 0 1 2 3 4 5 x age of bicycle (in years) no, the residuals would tend to be one - sided. yes, the residuals would be randomly distributed. no, the residuals would be randomly distributed. yes, the residuals would tend to be one - sided.

Answer

Explanation:

Step1: Recall residual - concept

Residuals are the differences between the observed and predicted values. For a good - fitting linear model, residuals should be randomly distributed around the x - axis.

Step2: Analyze the data points

The points seem to follow a non - linear pattern. If we fit a line through (0,250) and (4,25), the line will not capture the curvature of the data. The residuals will tend to be one - sided (either mostly positive or mostly negative on one side of the line and mostly negative or mostly positive on the other side).

Answer:

No, the residuals would tend to be one - sided.