multiple regression analysis

The Balochistan province of Pakistan is mostly affected by severe drought events due to little amount of precipitation. “Several Large Scale Climate Drivers (LSCDs) are known for their effects on precipitation worldwide but studies in the region are missing; a wide variety of LSCDs and lagged associative information”.The current study aimed to identify the significant LSCDs in the Balochistan province of Pakistan and improve the prediction skill of monthly precipitation by employing the Principal Component Analysis, Artificial Neural Network (ANN), Bayesian Regularization Neural Network (BRNN), and Multiple Regression (MR) Analysis using the 12 months lagged LSCDs such as Nino-1+2, Nino-3, Nino-3.4, Nino-4, QBO at 30 and 50hpa (QBOI and QBOII), Sea Surface Temperature (SST), 2m air temperature (T2M), 500hpa and 850hpa geopotential heights (H500 and H850), 500hpa zonal velocity (U500), 500hpa and 850hpa meridional velocity (V500 and V850), Latent and Sensible Heat Fluxes Over Land (LHFOL and SHFOL), and Surface Specific Humidity (SSH). To collect the data, Global Land Data Assimilation System, Tropical Rainfall Measuring Mission, MERRA-2, NOAA, Freie University Berlin, and HadISST datasets were used. The results of the study showed that significant LSCDs with a 99% confidence level were SSH, SST, LHFOL, SHFOL, T2M, U500, Nino-3.4, and Nino-4. During the test period, compared with MR models of 0.15 to 0.49 and principal components of -0.16 to 0.43, the ANN and BRNN models had better predictive skills with correlation coefficients of 0.40 to 0.74 and 0.34 to 0.70, respectively. It can be concluded that the ANN and BRNN models enable us to predict monthly precipitation in the Balochistan region with lagged LSCDs.

The main objective of this research is providing an entrepreneurship education model for small and medium-sized businesses. To this end, the dimensions and indicators were identified and the initial model was developed by examining the models and theories related to the factors affecting the creation of entrepreneurship and exploratory interview with some experts. Then, the Delphi panel consisting of 20 experts was formed. The questionnaires were distributed and their opinions were analyzed. The result showed that entrepreneurship education for small and medium enterprises consists of the skill dimensions of IT, technical, entrepreneurial, and management. These dimensions consist of 47 indicators. Reliability of the questionnaire was obtained equal to 0.89 using SPSS software and Cronbach's alpha. Express Choice Software was used to determine the priority of each of the dimensions and indicators of the paired comparison technique. The results showed that entrepreneurial skills play the most important role in creating small and medium-sized businesses. In order to determine the effectiveness of the model, 384 samples were selected from the graduates and the final questionnaire was provided to them. In this case, the difference between the scores before and after training was considered as a dependent variable. Using SPSS software and multiple regression analysis, the positive relationship of entrepreneurship education was confirmed with IT, technical, entrepreneurship, and management skills. The results of modeling with Amos software also showed a positive and significant relationship between skills and entrepreneurship education.