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Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1-5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE O Model 2 Model 3 Model 5 O Model 4 O Model 1 1 <0.000001 0.955978 0.792802 18.277907 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 3 4 2 <0.000001 <0.000001 0.928791 0.928295 0.759583 0.761951 0.652212 17.253086 17.234646 14.639962 Test Data Scoring Models (Polynomial of order 1-5) 3 4 2 1.193874 1.764467 <0.000001 0.855568 1.358224 46.700141 -49.9795 68.2403 49.9795 5 <0.000001 0.832559 0.630168 13.863111 5 -162.8793 234.5554 162.8793 0.111347 0.941985 0.769574 13.310035 69851.1195 825243.8222

Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1-5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE O Model 2 Model 3 Model 5 O Model 4 O Model 1 1 <0.000001 0.955978 0.792802 18.277907 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 3 4 2 <0.000001 <0.000001 0.928791 0.928295 0.759583 0.761951 0.652212 17.253086 17.234646 14.639962 Test Data Scoring Models (Polynomial of order 1-5) 3 4 2 1.193874 1.764467 <0.000001 0.855568 1.358224 46.700141 -49.9795 68.2403 49.9795 5 <0.000001 0.832559 0.630168 13.863111 5 -162.8793 234.5554 162.8793 0.111347 0.941985 0.769574 13.310035 69851.1195 825243.8222

Operations Research : Applications and Algorithms
Operations Research : Applications and Algorithms
4th Edition
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Wayne L. Winston
Chapter20: Queuing Theory
Section20.2: Modeling Arrival And Service Processes
Problem 3P
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Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1 - 5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE Model 2 Model 3 Model 5 Model 4 Model 1 1 <0.000001 0.955978 0.792802 18.277907 1 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 4 2 <0.000001 0.928791 0.759583 17.253086 3 <0.000001 <0.000001 0.928295 0.855568 0.761951 0.652212 17.234646 14.639962 Test Data Scoring Models (Polynomial of order 1-5) 3 4 2 1.193874 1.764467 1.358224 46.700141 0.111347 0.941985 0.769574 13.310035 69851.1195 -49.9795 68.2403 49.9795 5 <0.000001 0.832559 0.630168 13.863111 5 -162.8793 234.5554 162.8793 825243.8222
Transcribed Image Text:Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1 - 5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE Model 2 Model 3 Model 5 Model 4 Model 1 1 <0.000001 0.955978 0.792802 18.277907 1 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 4 2 <0.000001 0.928791 0.759583 17.253086 3 <0.000001 <0.000001 0.928295 0.855568 0.761951 0.652212 17.234646 14.639962 Test Data Scoring Models (Polynomial of order 1-5) 3 4 2 1.193874 1.764467 1.358224 46.700141 0.111347 0.941985 0.769574 13.310035 69851.1195 -49.9795 68.2403 49.9795 5 <0.000001 0.832559 0.630168 13.863111 5 -162.8793 234.5554 162.8793 825243.8222
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