The correlation analysis was performed using MATLAB. The linear regression analysis was performed using SPSS software. Correlation is a relationship between variables and its strength ranges from -1 to +1. The correlation between two variables is positive when the value of one variable is an increase in one variable will result in the value of the other variable will increase.
When we apply the method of least squares, we incorporate the appropriate measure of spread of the data (error). This is preferable to a linear regression model. The least squares method was applied in this instance, and the coefficient of determination (r squared) was found to be 0.9375.
The data was included in a regression model that explained the variation in the dependent variable as a function of the independent variables, i.e., the average amount of the dependent variable that occurred under the various conditions of the independent variables. The analysis declared the significance of the independent variables and yielded their regression coefficients and the coefficient of determination (R squared).
Data analysis was performed using SPSS Statistical Software (IBM Software, Armonk, New York) in terms of one-way analysis of variance. The average values were compared and classified at the 95% confidence level using the Duncan multiple range (DMRT) test. 7211a4ac4a