Estimation Of Nonorthogonal Problem Using Time Series Dataset

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Abstract

Based on presentation of the principles of nonorthogonal problem, we discuss the difference of some approaches. A simple procedure to include the R-squared and Root Mean Square Error (R.M.S.E) is proposed and tested. The results showed that the Partial Least Square Regression provides better predictions due to a small R.M.S.E value.

Keyword. Nonorthogonal, Mean Square, Partial Least Square, R Square.


Table of Content

1.0 Introduction

2.0 The Ordinary Least Square Model

2.3 Ridge Regression (RR).

3.0 Result and Discussion

3.1 Illustrative Example

4.0 Conclusion

4.1 Recommendation

Reference


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(2016, 03). Estimation Of Nonorthogonal Problem Using Time Series Dataset.. ProjectStoc.com. Retrieved 03, 2016, from https://projectstoc.com/read/7457/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649
"Estimation Of Nonorthogonal Problem Using Time Series Dataset." ProjectStoc.com. 03 2016. 2016. 03 2016 <https://projectstoc.com/read/7457/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649>.
"Estimation Of Nonorthogonal Problem Using Time Series Dataset.." ProjectStoc.com. ProjectStoc.com, 03 2016. Web. 03 2016. <https://projectstoc.com/read/7457/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649>.
"Estimation Of Nonorthogonal Problem Using Time Series Dataset.." ProjectStoc.com. 03, 2016. Accessed 03, 2016. https://projectstoc.com/read/7457/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649.

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