linear regression

This paper aimed to apply a balanced scorecard as an evaluation performance tool to investigate the impact of organizational structure variables on the organizational performance of Esfahan steel industries. The standardized Robbins’s (1998) questionnaire was used for data collection. Hersey and Goldsmith’s standard questionnaire was used to assess aspects of organizational performance. The sample consisted of 100 employees working in the Esfahan steel industries. The results demonstrated that there is a meaningful relationship between organizational performance and organizational structure and its components including complexity, formality, and concentration. The findings also indicated that concentrations had the most significant impact on organizational performance. The results of the Bartlett and Kaiser-Meyer tests indicated a high reliability of the research (0.89 and a confidence level of less than 0.05). Also, using correlation coefficient, regression test, and structural equations, the assumptions of the research were confirmed. The results of regression show that centralizations (With a coefficient of 0.59) had the most significant impact on organizational performance.

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president’s approval rate, and others are considered in a stepwise regression to identify significant variables. The president’s approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method’s calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast.