Determine the parameters for photoelectric effect data using correlation and simple linear regression
Pearson's correlation coefficient, otherwise known as the product-moment correlation coefficient, a non-parametric process, is a very important concept in statistics, data science, and even in machine learning. It has gained tremendous acceptance in almost all fields and industries where data a...
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| Format: | Article |
| Language: | en |
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Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20964/1/QT%205.pdf http://journalarticle.ukm.my/20964/ https://www.ukm.my/jqma/current/ |
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| author | Saratha Sathasivam, Salaudeen Abdulwaheed Adebayo, Muraly Velavan, Kng, Jason Wei Liang |
| author_facet | Saratha Sathasivam, Salaudeen Abdulwaheed Adebayo, Muraly Velavan, Kng, Jason Wei Liang |
| author_sort | Saratha Sathasivam, |
| building | Tun Sri Lanang Library |
| collection | Institutional Repository |
| content_provider | Universiti Kebangsaan Malaysia |
| content_source | UKM Journal Article Repository |
| continent | Asia |
| country | Malaysia |
| description | Pearson's correlation coefficient, otherwise known as the product-moment correlation coefficient, a non-parametric process, is a very important concept in statistics, data science, and even in machine learning. It has gained tremendous acceptance in almost all fields and industries where data analysis is the business of the day. It helps to highlight the affinity between two variables whose behaviour might be entirely different, correlation coefficient is an indicator that shows whether such affinity is positive, negative, or none, when no linear relationship can be established between the variables. It is characterized by a numerical value that ranges between -1 and 1. These values serve as the indicators that determine the status of the relationship. In this research, we utilized the idea of correlation coefficient and simple linear regression on experimental data of photoelectric effects to determine the Planck constant, work function, and threshold frequency using MATLAB code. |
| format | Article |
| id | my-ukm.journal.20964 |
| institution | Universiti Kebangsaan Malaysia |
| language | en |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | eprints |
| spelling | my-ukm.journal.209642023-01-17T08:04:38Z http://journalarticle.ukm.my/20964/ Determine the parameters for photoelectric effect data using correlation and simple linear regression Saratha Sathasivam, Salaudeen Abdulwaheed Adebayo, Muraly Velavan, Kng, Jason Wei Liang Pearson's correlation coefficient, otherwise known as the product-moment correlation coefficient, a non-parametric process, is a very important concept in statistics, data science, and even in machine learning. It has gained tremendous acceptance in almost all fields and industries where data analysis is the business of the day. It helps to highlight the affinity between two variables whose behaviour might be entirely different, correlation coefficient is an indicator that shows whether such affinity is positive, negative, or none, when no linear relationship can be established between the variables. It is characterized by a numerical value that ranges between -1 and 1. These values serve as the indicators that determine the status of the relationship. In this research, we utilized the idea of correlation coefficient and simple linear regression on experimental data of photoelectric effects to determine the Planck constant, work function, and threshold frequency using MATLAB code. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/20964/1/QT%205.pdf Saratha Sathasivam, and Salaudeen Abdulwaheed Adebayo, and Muraly Velavan, and Kng, Jason Wei Liang (2022) Determine the parameters for photoelectric effect data using correlation and simple linear regression. Journal of Quality Measurement and Analysis, 18 (3). pp. 61-70. ISSN 2600-8602 https://www.ukm.my/jqma/current/ |
| spellingShingle | Saratha Sathasivam, Salaudeen Abdulwaheed Adebayo, Muraly Velavan, Kng, Jason Wei Liang Determine the parameters for photoelectric effect data using correlation and simple linear regression |
| title | Determine the parameters for photoelectric effect data using correlation and simple linear regression |
| title_full | Determine the parameters for photoelectric effect data using correlation and simple linear regression |
| title_fullStr | Determine the parameters for photoelectric effect data using correlation and simple linear regression |
| title_full_unstemmed | Determine the parameters for photoelectric effect data using correlation and simple linear regression |
| title_short | Determine the parameters for photoelectric effect data using correlation and simple linear regression |
| title_sort | determine the parameters for photoelectric effect data using correlation and simple linear regression |
| url | http://journalarticle.ukm.my/20964/1/QT%205.pdf http://journalarticle.ukm.my/20964/ https://www.ukm.my/jqma/current/ |
| url_provider | http://journalarticle.ukm.my/ |