Estimation of Nonorthogonal Problem Using Time Series Dataset
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200%">Abstract
200%">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.
200%">Keyword. Nonorthogonal, Mean Square, Partial Least Square, R Square.
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200%">Table of Content
200%">1.0 Introduction
200%">2.0 The Ordinary Least Square Model
200%">2.3 Ridge Regression (RR).
200%">3.0 Result and Discussion
200%">3.1 Illustrative Example
200%">4.0 Conclusion
200%">4.1 Recommendation
200%">Reference
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BARTHOLOMEW, D. (2018). Estimation of Nonorthogonal Problem Using Time Series Dataset. Afribary. Retrieved from https://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://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649. Accessed 24 May. 2022.
BARTHOLOMEW, DESMOND . "Estimation of Nonorthogonal Problem Using Time Series Dataset". Afribary, Afribary, 29 Jan. 2018. Web. 24 May. 2022. < https://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 May 24, 2022. https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649