An electronics company wanted to determine a fair market price for their phones. They took a random sample of 40 phones currently on the market and fit a multiple regression model to predict the price of the phones based on various characteristics, with the output shown below. Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 4 Analysis of Variance Sum of DFSquares Mean Square 5 JMP regression output 96263.91 283T Source Model Error C. Total Parameter Estimates 5 34 39 616472.38 Term Intercept Weight (oz) Internal Memory (MB) 0.843347 0.820884 53.20988 41735 40 RAM (GB) Rear Camera Resolution (MP) Front Camera Resolution (MP) F Ratio 36.7471 Prob> F <.0001- 0001* Estimate Std Error t Ratio Prob>|t| 266 31355 26.98862 9.87 -0.540333 2.875019 -0.19 0.8520 0.858115 0.686231 1.25 0.2197 36.522719 15.90112 2.30 0.0279* 3.4209006 2.34991 1.46 0.1546 7.028927 2.829839 2.48 0.0181 (a) Identify the correct statement below regarding the quality of the model: O 91.9% o

Answer rating: 100% (QA)

Based on the data given lets analyze each aspect of the output to understand the quality of the model 1 RSquare RSquared Value The RSquared value give... View the full answer