Please show the correct answer with some useful explanation, thanks.

You've just calculated an ordinary least squares fit for a dataset of values (@;, yi ). You use the resulting fit to calculate the residuals e; of all the data points. Which (if any) of the following statements are true? Choose all that apply. If the sum of the residuals is large, the least squares fit is not a good model for the data If the sum of the residuals is close to zero, the least squares fit is a good model for the data. A linear trend in a graph of e; vs Ci+1 is evidence that the least squares fit is a good model for the data. The residuals do not give any information about whether the least squares fit is a good model for the data. A random scatter of points in a graph of Ci vs Ci+ 1 is evidence that the least squares fit is a good model for the data