Multiple linear regression

Management is an effective and goal-oriented process that guides an organization. This guidance involves five sequential functions: planning, organizing, leading, coordinating, and controlling and evaluating. The fundamental knowledge of management, or the management process, has been present even in ancient civilizations such as those of the Iranians, Egyptians, Sumerians, and others. In Islamic civilization, we have had grand and civilizational management systems. Today, the successful implementation of an HSE (Health, Safety, and Environment) management system depends on the participation of all employees. Given that job satisfaction can influence employee performance, this study aims to model the relationship between job satisfaction and the HSE performance of Pars Oil and Gas Company employees. The statistical population of this research consists of all employees of Pars Oil and Gas Company (N = 5300). Using Cochran's formula and simple random sampling, 360 employees were selected as the sample. The data collection tool was a questionnaire, and to ensure its validity, the opinions of several university faculty members were used. Cronbach's alpha coefficient was applied to confirm the reliability, yielding values of 0.79 for job satisfaction and 0.83 for HSE performance. The results indicate a positive and significant relationship between job satisfaction and HSE performance (P-value < 0.01). The regression results show that, in two steps, the indices of job natureand security and safety, which had the highest impact on HSE performance, were included in the analysis. In the first step, the job nature index explains 59% of thevariance in the response variable (HSE performance), and in the second step, with the inclusion of the security and safety index, this figure increases to 63%

Forecasting financial variables, especially the returns of debt instruments, plays a vital role in economic decision-making and risk management. Although the forecasting literature in financial markets is extensive, few studies have focused on predicting the returns of Islamic Treasury Bonds with unconventional structures. Moreover, despite the importance of these bonds, very limited work has been done using machine learning in the debt market. This study aims to predict the returns of Islamic Treasury Bonds using three models: Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLP), and Radial Basis Function Neural Network (RBF). Monthly data from 2018 to 2023 were collected using Excel and Python. The training and evaluation of the models were carried out in MATLAB. Eleven influential variables were selected based on previous studies and expert opinions. The models' performance was evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The findings indicate that the Multilayer Perceptron Neural Network model has higher accuracy in predicting the returns of Islamic Treasury Bonds compared to Multiple Linear Regression and Radial Basis Function models. These results suggest that neural network models can serve as more effective tools in financial and economic analyses, significantly enhancing forecasting accuracy.