A Least-Squares Approach to Prediction the Future Sales of Pharmacy
Mohammad Abazid1, Duaa Alkoud2
1Mohammad Abazid, Ph.D. Candidate, Department of Civil Engineering, Near East University, Nicosia, Turkey.
2Duaa Alkoud, Master Student, Department of Pharmacy and Medical Sciences, Al-Ahliyya Amman University, Amman, Jordan.
Manuscript received on 05 July 2018 | Revised Manuscript received on 18 July 2018 | Manuscript published on 30 July 2018 | PP: 1-4 | Volume-7 Issue-10, July 2018 | Retrieval Number: J25120771018/18©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The least square methods (LSM) are widely utilized in data fitting, with the best fit minimizing the residual squared sum. LSM can be divided into two categories, the ordinary or linear LSM and the nonlinear LSM, this depends on the residuals. To statistically analyzed regression, the linear LSM most have a solution that is closed form. Conversely, the nonlinear LSM is analyzed using the iterative refinement. In this paper, the best fit of the data that correspond to pharmacy sales for the year 2019 and 2020 is evaluated using the LS method. The results revealed that the trend value for 2014, 2015, 2016, 2017, 2018, 2019, 2020 and 2021 is found to be 10200, 27300, 44400, 61500, 78600, 95700, 112800 and 129900 respectively.
Keyword: The Least Square Methods, LSM, Pharmacy, Sales.
Scope of the Article: Health Monitoring and Life Prediction of Structures