multiple regression

Background & Aims of the Study: In a competitive world, knowledge management is a major task for the organization. This study was carrying out to recognize the essential factors for deployment of knowledge management in teaching hospitals affiliated to Qom University of Medical Sciences (QUMS), Iran. This descriptive-correlational study was operated on 570 employees of teaching hospitals of QUMS in 2017. A researcher-made questionnaire was expanded. Its validity was confirmed by specialist and also reliability was confirmed by Cronbach's alpha ratio. Data were analyzed by Kolmogorov-Smirnov test, Spearman correlation ratio and multiplex regression. The outcome showed that the average score of knowledge management in teaching hospitals was moderate (3.35). Spearman correlation coefficient showed there is affirmative and significant relation between strategy and policies, staff motivation, learning culture, top management support& decentralization with the deployments of knowledge management. Multiple regressions showed there is a lineal relation between independent variables and dependent variable. Multiple regression ratio (R=0.799) and the coefficient of determination (R2 =0.0607) and adjusted coefficient (R2adj=0.602) in the level of 0,05 are significant. The outcome of this survey, demonstrate that knowledge management has a relatively favorable status. Therefore, top manager's support of knowledge-based strategies and increasing member participation in resolve making and the presence of staff in workshops can help to deployments of knowledge management.

Data mining (DM) is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP) in Birjand, a city in Eastern Iran.
Multiple regression (MR) and neural network (NN) models were examined using influent characteristics (pH, Biochemical oxygen demand [BOD5], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity) as the regression input vectors. Models were adjusted to input attributes, effluent BOD5 (BODout) and COD (CODout). The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic (REC) plot and other statistical measures such as relative absolute error (RAE). Sensitivity analysis was also applied to extract useful knowledge from NN model.
NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout) and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout) indicate different performances and the former was better (P Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better understanding on exploring the most significant parameters for the prediction of system performance. The findings of this study could build the foundation for prospective work on the characterization of WSP operations and optimization of their performances with a view to conducting statistical approaches.

Paper-pencil tests have always its own problems in the mental disorders evaluation, including learning questions, bad or good blazon are the problems with this methodology. This study aimed to propose a new alternative method of measuring mental disorders without paper-pencil test using EEG.
The research society involved depressed patients referred the psychiatrist clinics in Tabriz. 107 patients were selected as samples using a convenient sampling method. The Beck test was conducted. The EEG was recorded from the F4 point concurrent with displaying the film of 5 animated emotional images from Normed Images database (IAPS). The specialized screen of this recording was designed by the author in the Biograph Infinity software of device. Other software was written by the author in order to separate the αpeak frequency average associated with any image of the recorded EEG. Then the research variablesα1peak , α2peak , α3peak , α4peak , α5peak of each patient were analyzed with SPSS. After all, another 26 patients were selected to measure the Golden Standard, sensitivity, Positive predictability, Negative predictability and ROC.
The results of the multiple regression analysis showed that α1peak associated with αpeakfrequancy of image 1 had more explanatory power with a beta value of 0.289 compared with other variables. Then α3peak had a high explanatory power. The regression equation for the predicting the score based on his/her EEG was found in terms of αpeak frequency.
This research showed that Beck's depression score was predictable without using any questionnaire but according to EEG with a high sensitivity (100%), specificity (30.8%), PPV (59.1%), NPPV (100%), and ROC (57.4%).