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
BARTHOLOMEW, D. (2018). Estimation of Nonorthogonal Problem Using Time Series Dataset. Afribary. Retrieved from https://tracking.afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649
BARTHOLOMEW, DESMOND "Estimation of Nonorthogonal Problem Using Time Series Dataset" Afribary. Afribary, 29 Jan. 2018, https://tracking.afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649. Accessed 22 Dec. 2024.
BARTHOLOMEW, DESMOND . "Estimation of Nonorthogonal Problem Using Time Series Dataset". Afribary, Afribary, 29 Jan. 2018. Web. 22 Dec. 2024. < https://tracking.afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649 >.
BARTHOLOMEW, DESMOND . "Estimation of Nonorthogonal Problem Using Time Series Dataset" Afribary (2018). Accessed December 22, 2024. https://tracking.afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649