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) yes, the residuals would tend to be one - sided. no, the residuals would tend to be one - sided. yes, the residuals would be randomly distributed. no, the residuals would be randomly distributed.

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) yes, the residuals would tend to be one - sided. no, the residuals would tend to be one - sided. yes, the residuals would be randomly distributed. no, the residuals would be randomly distributed.

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 decreasing trend. If we draw a line through (0, 250) and (4, 25), all the data points will be below the line. So the residuals (observed - predicted) will all be negative (or mostly negative), which means they are one - sided.

Answer:

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