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\begin{tabular}{|lll|l|} \hline \multicolumn{1}{|c}{ A } & \multicolumn{1}{c}{ B } & \multicolumn{1}{c|}{ D } \\ \hline House & Actual Price & Predicted Price 1 & Predicted Price 2 \\ \hline 1 & 230500 & 254000 & 256000 \\ \hline 2 & 209900 & 215500 & 223400 \\ \hline 3 & 258900 & 240000 & 228000 \\ \hline 4 & 185500 & 204000 & 219400 \\ \hline 5 & 169000 & 157500 & 159400 \\ \hline 6 & 350500 & 325800 & 339800 \\ \hline 7 & 399900 & 423600 & 452500 \\ \hline 8 & 310000 & 324500 & 305500 \\ \hline 9 & 195500 & 180750 & 193750 \\ \hline 10 & 328900 & 340000 & 324500 \\ \hline \end{tabular}
A real estate company has built two predictive models for estimating the selling price of a house. Using a small test data set of 10 observations, it tries to assess how the prediction models would perform on a new data set. The accompanying data file lists the actual prices and predicted prices generated by the two predictive models. Ciick here for the Excel Data File: a. Compute the ME, RMSE, MAD, MPE, and MAPE for the two predictive models. Note: Round intermediate calculations to ot least 4 decimal ploces and your finol answers to 2 decimal places. Negative volues should be indicoted by a minus sign.
b. Are the predictive models over-or underestimating the actual selling price on average? c-1. Compare the predictive models to a base model where every house is predicted to be sold at the average price o in the training data set, which is $$260,500$. Compute RMSE for the base model Note: Round intermediate calculations to at least 4 decimal places and your final answer to 2 decimal ploces.
c-2. Do the predictive models built by the real estate company outperform the base model in terms of RMSE? d. Which predictive model is the better-performing model?

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