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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Essentials of Business Analytics
Authors: Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson
2nd edition
1305627733, 978-1305861817, 1305861817, 978-0357688960, 978-1305627734
