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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20964/ http://journalarticle.ukm.my/20964/1/QT%205.pdf |
| Summary: | 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. |
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