Question: For a big data set with over a thousand data points, here are the results of 3 models: Linear Regression Model: Intercept: 2016.2420482198067 Coefficient: 103.53239780610906

For a big data set with over a thousand data points, here are the results of 3 models:

Linear Regression Model:

Intercept: 2016.2420482198067

Coefficient: 103.53239780610906

R-squared: 0.69

Mean Squared Error (MSE): 27905774319.88

Root-mean-square deviation (RMSE): 27905774319.88

Durbin-Watson: 1.986

Logarithmic Regression Model

Intercept: -279009.73914077145Coefficient: 72124.17336834407R-squared: 0.18Mean Squared Error (MSE): 73977422157.93Root-mean-square deviation (RMSE): 73977422157.93

Durbin-Watson: 1.986

Quadratic Regression Model

Intercept: 50000.009427102494Coefficient: 0.0017529203911621145R-squared: 0.39Mean Squared Error (MSE): 54988109028.83Root-mean-square deviation (RMSE): 54988109028.83

Durbin-Watson: 1.994

Compare each model and interpret each statistic value (R-squared, etc.) thoroughly for each model. Give the best fit according to the above-mentioned data. Discuss the limitations for these statistical measures in BIG DATA.

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