multiple regression

This study examines the impact of predictor variables Information and Communications Technology (ICT) infrastructure, the level of interoperability in banking systems, human resources, financial resources, as well as policies and regulations on the development of big data infrastructure within the Iranian banking system.

Data were collected using a Likert-scale questionnaire administered to 104 employees from Meli, Saderat, Mellat, Tejarat, and Sepah banks in the Hamedan province.

Correlation tests revealed that all predictor variables are positively and significantly related to big data infrastructure. Multiple regression analysis further indicated that these factors explain approximately 56% of the variation in big data infrastructure (R² = 0.560). Additionally, the reliability of the instrument was confirmed with Cronbach's alpha values ranging between 0.769 and 0.821.

 The findings are consistent with previous domestic and international studies, underscoring the importance of improving ICT infrastructures, enhancing system interoperability, strengthening human resource expertise, securing appropriate financial investments, and formulating supportive policies as fundamental prerequisites for the development of big data technology. Consequently, addressing these factors may pave the way for increased efficiency and competitiveness of the Iranian banking system in the digital era.

The increasing population pressure, drastic land use changes, and rapid urbanization have made cities susceptible to various natural and human-induced hazards. Therefore, enhancing the resilience of urban settlements is crucial, particularly in the context of megacities. This research investigated urban resilience levels, prioritized resilience indicators, and compared resilience status between Districts 5 and 6 of Tabriz Metropolitan Area. The study employed a descriptive-analytical method with a developmental-applicative focus. Data were collected through library research, field surveys, and questionnaires, targeting the residents of Districts 5 and 6. A sample size of 342 individuals was determined using Structural Equation Modeling (SEM) and the gamma exponential method. In this study, 37 observed indicators of urban resilience were analyzed across 4 latent categories: social, economic, institutional-management, and physical-infrastructure. Latent indicators were prioritized based on their significance and impact through multiple regression analysis using SPSS software, while observed indicators were assessed via confirmatory factor analysis in LISREL software. A fuzzy logic model was employed within a GIS environment to map the effective indicators. Results revealed that the social latent index ranked highest in District 5 of Tabriz Metropolitan Area with a beta coefficient of 0.384 (33.83%) followed by the physical-infrastructural (0.305, 26.87%), economic (0.267, 23.52%), and institutional-management (0.179, 15.77%) indicators. In District 6, the physical-infrastructural latent index led with a beta coefficient of 0.528 (42.58%) followed by the institutional-managerial (0.337, 27.18%), social (0.265, 21.37%), and economic (0.110, 8.87%) indicators. Additionally, the findings indicated that the northwestern and central parts of District 5 exhibited better resilience, ranking in the upper-middle tier, while the southern areas showed poor resilience. Conversely, the northern parts of District 6 also displayed weak resilience, ranking in the lower average range, while the southern and central regions were categorized as upper average in terms of resilience.

This research focuses on predicting critical thinking based on the emotional intelligence and self-efficacy beliefs of nursing students, with the aim of examining the impact relationship of emotional intelligence and self-efficacy beliefs on the critical thinking of nursing students. The main question addressed in this article is: What is the relationship between critical thinking, emotional intelligence, and self-efficacy beliefs? The research method used was correlational, and data was collected using a questionnaire with suitable validity and reliability (Cronbach's alpha for the critical thinking questionnaire was 0.76, the emotional intelligence questionnaire was 0.83, and the self-efficacy beliefs variable was 0.83). A sample of 148 students was selected randomly from the statistical population. The data was analyzed using SPSS software. The results showed that self-efficacy beliefs and emotional intelligence can predict the critical thinking of the students.

Urban heat islands have become one of the most challenging issues in urban studies. In addition to their primary impact on average temperature, heat islands can also have secondary effects on local meteorology. Urban heat islands are primarily caused by excessive surface heating and heat storage, pollution emissions, human heating, wind circulation barriers, and reduced evapotranspiration.

In this study, the split-window algorithm of Landsat 8 sensor with a combination of thermal bands 10 and 11 was used to obtain land surface temperature during the day, and the MODIS sensor was used to obtain land surface temperature at night. To determine the heat island region in the city, global and local Moran's I autocorrelation analyses and Getis-Ord Gi hotspot analysis, both performed in ArcMap software, were used. Multiple linear regression analysis in SPSS software was used to obtain the maximum impact of indices.

The results indicate that the hottest points during the day were in Region 6 around Tabriz Airport with a temperature of 27.27 degrees Celsius, and at night, the temperature was 17.07 degrees in Region 3. According to spatial Moran's I analysis, both heat islands had a clustered pattern. According to regression results, open spaces and soil texture, with a score of 0.700, had the greatest impact on urban heat islands due to their high heat capacity and were the coolest surfaces at night. The least impact was from built spaces with reflective roofs, with a score of -0.192, which had a cooling effect. Building height with shading and random density had a negative effect on increasing heat. However, the influential index on nighttime heat islands was in conditions where the building density was moderate and the orientation was appropriate, but the height-to-width ratio of the passageway was 1.31 and 0.825, respectively, which caused heat retention.

The aim of the current research is to determine the cutoff score of the Tolimo test for doctoral candidates applying for study opportunities abroad by applying multiple regression in the Hurwicz method, which is a decision-making approach. The statistical population of this research includes the candidates of one course of Tolimo exam, which were 461 people. The research method of this study is based on a quantitative approach, and in terms of its purpose, it is part of applied research, and in terms of analysis, it is part of secondary analysis studies, and the scores of all 461 candidates in each of the grammar, reading comprehension, listening, and writing sections are used to determine the cutoff score. becomes Based on the results of the study and considering the small amount of classification error, if the total score is used, the average value of the Hurwicz index is the cutoff score of 481, and if the linear regression model is significant, using the results of the Hurwicz index in multiple regression, the cutoff score is 491. It can be used as a cutoff score. The proposed method significantly improves the accuracy of determining the cutoff score compared to Angoff and Bookmark methods. According to the findings obtained from cross-validation and error values, the cutoff score obtained from the combined strategy leads to more accurate results with zero classification error. 

This article was studied with the aim of investigating the relationship between managers' political intelligence and human resource productivity in government organizations. The study method was descriptive in terms of purpose and among correlational studies. The statistical population was formed by managers and officials of Ardabil University of Medical Sciences who have university level education. A sample size of 161 people was selected through the Morgan-Jersey table and from the statistical population of 293 members through stratified random random sampling. The validity of the questionnaire was confirmed by consulting university professors and the reliability of the questionnaire was also confirmed by calculating Cronbach's alpha coefficient. The characteristics of the sample members and research variables were described through descriptive statistics and the hypotheses were tested through inferential statistics. In this way, the normality of the data was confirmed through Smirnov and Kolmograf non-parametric statistics. The findings of the research indicate that there is a positive correlation and a significant relationship between political intelligence and human resource productivity of 63.1%. The results of the research showed that the effects of political intelligence have a positive and significant effect on the productivity of human resources in about 44.3% percent, and among the dimensions of political intelligence, the "political personality" component has the greatest effect on the productivity of human resources in about 36%. And also, the results indicate that the components of political personality, open honesty, and capturing emotions have the greatest net effect on human resource productivity, respectively. Also, according to the obtained results, the greatest impact on the productivity of human resources is related to the dimension of political personality of managers and the lowest impact is related to the social games of managers of Ardabil University of Medical Sciences.

Climate is one of the environmental factors that plays a seminal role in human activities including tourism. In this research, the human-biometeorological climatic conditions of Ardabil province have been investigated using physiological equivalent temperature (PET) based on body-atmosphere energy balance and multivariate regression. The data was collected from the National Meteorological Organization for 8 synoptic stations over 30 years (1991 to 2020). The results suggest that out of 9 climate classes of PET index, only 5 classes of “very cold, cold, relatively cool, cool and comfortable” can be found in this region and 4 classes of “relatively hot, hot, hot and very hot” do not exist in this province. In this regard, it turned out that very cold conditions prevail over the entire province in 5 months of the cold season, i.e., January, February, March, November and December, and the spatial distribution of these conditions is fully homogenous. This is relatively heterogeneous in the summer and the climatic conditions of this area are more variable in the hot season of the year. In general, in June, July and August, ideal conditions prevail in parts of this province. As such, the it was found that the major controlling factor of comfortable climatic conditions among longitude, latitude and altitude was altitude, so that there is a strong inverse relationship between altitude and PET index in 12 months of the year at a significant level of α=0.05 and α=0.01. The multivariate regression relationship of longitude, latitude and altitude with the PET indicate that Adj.R2 value of PET from January to December for the reference period is 0.713, 0.820, 0.783, 0.807, 0.807, 0.559, 0.841, 0.693, 0.745, 0.731, 0.824 and 0.662. The highest variations in the dependent variable were observed in the regression model. In this model, altitude has an inverse relationship with PET so that its value declines as altitude increases. This is true for two variables of latitude and longitude, but the effect of longitude is less substantial than that of latitude, both of which are eclipsed by altitude.

Atmospheric-oceanic macro scale phenomena have caused much of the worldchr('39')s climate change. Atmospheric-oceanic macro scale patterns are a suitable variable for predicting climatic elements, especially snowy days. In Ardebil province, light, semi-heavy and heavy snowfalls, in addition to local factors, are also related with Large-scale Atmospheric-oceanic circulation patterns. Forecasts for snow days in Ardabil province can provide by determinatiom of relationship between snowfalls and macro-scale phenomena of atmospheric-oceanic circulations in this province.

In this research, by snow days data of synoptic stations of Ardabil province as sample of climate of North West of Iran, statistical characteristics of snow days of this province was studied. The statistical period used in Ardebil, Khalkhal, Meshkinshahr and Pars Abad synoptic stations were 42, 31, 22 and 33 years from 1976, 1987, 1996 and 1985 to 2017, respectively. Independent two-sample T-test was used to compare the mean snow days of the stations under study. In this research, In addition to descriptive analysis, spearman correlation, order 6 polynomial and linear trend, multiple regression analysis based on backward method for determine of variability of snow days in Ardabil province by using 27 Large-scale Atmospheric-oceanic circulation pattern of Atlantic and Pacific oceans. Indicators of macro-scale atmospheric-ocean circulation phenomena used in this study were obtained from https://www.psl.noaa.gov/data/climateindices  which include: SOI, SENSO, EOF , MEI, NINA1, NINA3, NINA4, NINA3.4, ONI, PWP, TNI, EPO, NOI, NP, PDO, PNA, WP, AO, AMM, AMON, ATLTRI, CAR, NTA, TNA, TSA, WHWP and NAO.

The results showed that there is an average of about 35 days of snow at Ardabil station on an annual scale. The number of snow days in January, February, March, December, as well as annual scale, has an almost normal distribution. In Ardebil station, the relative normality of the distribution of the number of snow days on an annual scale and its similarity to December to March is due to the high share of snow days in these months in the composition of the number of annual snow days in this station. The coefficient of variation of most stations is also higher in May and October than in other months. Result of linear trend of snow days variability in Ardabil synoptic station shows a slow increase of snow days of this station. Snow days in most of the stations under study had significant correlations at 1% and 5% error levels by each there, which reflects the prevalence of snowfall at Ardabil province stations. In Khalkhal, Meshkinshahr and Parsabad stations, annual decrease in number of snow days is observed in annual scale. The 6th-order polynomial model predicted snow days in Ardabil station rather than other models. T-test results of two independent samples for snow days of the studied stations showed that significant value in Levenechr('39')s test was greater than 0.05, so the assumption of equality of variances cannot be rejected. The significant value of T test for equality of means indicates that in Ardebil and Khalkhal stations, the assumption of equality is confirmed, but in Khalkhal and Parsabad stations the assumption is rejected. At Parsabad station, the number of snowy days was significantly inversely correlated with the Atlantic oceanenic-atmospheric patterns. Results showed that the snowy days in synoptic stations of Ardabil province have a higher and more significant correlation with atmospheric-oceanic patterns of the Pacific Ocean than atmospheric-oceanic patterns of the Atlantic Ocean. At Ardabil, Khalkhal, Meshkinshahr and Parsabad stations, 48%, 90%, 99% and 49% of the changes in snowy days were explained by the atmospheric-oceanic patterns of the Atlantic Ocean, respectively. The results showed that in Ardabil station, NINO3.4, ONI, TNI and NINO1+2 patterns, in Meshkinshahr station, NP pattern and in Parsabad station, NEI, PNA, NP, NINO3.4 and NINO1+2 patterns, explained the highest percentage of changes by the number of snowy days of these stations with atmospheric-oceanic patterns of the Pacific Ocean. The results also showed that in Ardabil station, WHWP, NTA and AMM patterns, in Khalkhal station, WHWP, TSA and AMM patterns, AMO, CAR and TNA, in Meshkinshahr station, WHWP, ATLTRI, AMM and TNA patterns and in Parsabad station, The AMO pattern explained the highest percentage of changes by the number of snowy days of these stations with atmospheric-oceanic patterns of the Atlantic Ocean

Among synoptic station of Ardabil provinec, Khalkhal station has more significant correlation with Atmospheric-ocemic patterns of Pacific ocean. In Ardabil provinec, Atmosphoric-oceanic patterns of Atlantic ocean, determine variability of snowdays of Khalkhal and Meshkinshahr synoptic stotions more than Ardabil and Parsabad synoptic stations. The results of T-test of two independent samples showed that the difference of mean of snow days in all the studied stations in the two study periods were not significantly different from each other and it can not be claimed that the number of snow days in the studied stations has been affected by climate change. Distribution of correlation values of snow days in the studied stations with Atlantic and Pacific atmospheric patterns showed that in most of the studied models, correlation of snow days with selected patterns increased from south to north of Ardabil province.

Decision-making is considered to be the main essence of governance and management. Every decision is purposefully made from among different possible options, but during implementation, it has the power to change the future of the organization and all its stakeholders. Therefore, the science of decision-making has become an irreplaceable science for policy makers and managers, both in the private and public sectors. Part of this science focuses on decision-making methods and techniques. One of the most useful decision-making methods that has been favored by managers and policy makers around the world is the "Importance -Performance analysis technique". This technique helps policy makers in evaluating policies and managers in strategic appraisal a lots. The present study understands the philosophical foundations of this technique, its main elements and practical procedure using SPSS 23 software. Criticisms of this methodology and guidelines for improving the performance of this technique are the final part of this article.

The current study is aimed at investigating the role of agile enablers to achieve agility in Pegah Tehran Dairy Co. using multiple regression. In this study, a regression model was employed to achieve the ideal agility level, as well as analyze the effect of indicators and provide an optimal path to achieve agility. To this end, 96 questionnaires were distributed among the managers of Pegah Tehran Dairy Co., and the research tests were carried out by means of SPSS and Smart PLS software. The validity tests used in this research were average variance extracted (AVE) and the Lawshe test, respectively, with coefficients more than 0.5 and 0.8 for research variables. In fact, the resulting coefficients indicate the sufficient validity of the investigated questionnaire. Moreover, Cronbach’s alpha test was utilized to measure its reliability, with a coefficient of more than 0.7 for all dimensions of the questionnaire, exhibiting its reliability. The results revealed that the variables of automation and employees had the most effect, and the technology variable had the least one. Besides, to achieve an acceptable level of each of the dimensions of agility attributes, the key variable(s) extracted from the analysis of this research should be identified, and the desired results would be achieved by making changes to them. In this case, maximum efficiency can be achieved, and the agility level can be improved with minimum time and cost.

The worn-out fabric of cities, while being one of the most valuable urban textures, also has a high vulnerability to earthquakes, and therefore their resilience to natural hazards, including earthquakes, is of great importance. Therefore, the present study has been conducted with the nature of developmental-applied and descriptive-analytical method with the aim of evaluating the resilience of Shahrekord worn tissue against earthquakes. In this study, the required data were collected using library studies and field surveys at the household level and the sample size was estimated at 341 households according to the Cochran's formula. After collecting data using quantitative and statistical methods such as multiple regression, structural equations and fuzzy multivariate analysis in GIS environment, to evaluate the degree of urban resilience and prioritize the resilience indices of worn-out neighborhoods Earthquake has been dealt with in Shahrekord. A total of 44 urban resilience indicators were examined in the form of 4 hidden physical, economic, institutional and social indicators. Using factor load tests in LISREL software, explicit indicators are prioritized and summarized based on their degree of importance and effectiveness, and a fuzzy logic model in the form of GIS is used to map effective indicators. The results show that there is a big difference between Shahrekord neighborhoods in terms of resilience; Thus, the neighborhoods located in the east and south of Shahrekord, the neighborhoods of the central part and the neighborhoods of the southeast have very high, medium and low resilience. In terms of having resilience indices, the priority of physical index with 67.2 percent, economic index with 38 percent, institutional index with 36.9 percent and social index with 36.2 percent have had the most role in resilience in Shahrekord, respectively. Which indicates the importance of the physical dimension compared to other dimensions.

This research has been prepared to measure the tendency towards modern spirituality in people over eighteen years old in Gorgan (city in the north of Iran) and to identify social factors and determine their impacts. In this survey, stratified random sampling (appropriate to the population living in different parts of the city) and a researcher-made questionnaire (with Cronbach's alpha of 0.75) were used. By using appropriate statistical techniques, the findings showed that 55.5% of the population with a score above average (3.12 out of 5) had a tendency to modern spirituality. The two variables "age" (with a Standardized partial correlation coefficient -0.28) and "the importance of religiosity in everyday life" (with a Standardized partial coefficient -0.24) directly and significantly have affected on the modern spirituality. And explain 16% of its variance. Also, the two variables " The importance of religiosity for parents" and "social class" indirectly (with Standardized partial coefficient -0.11 and 0.031) have affected on this trend. It can also be inferred from all the findings that the hypothesis "Modern spirituality as destroyer of the religiosity" is probably more correct than the hypothesis of the transformation and compatibility of religiosity.

Water erosion has been a problem worldwide, which causes the phenomenon of land degradation in semi-humid areas of dry land. The risk of soil erosion in arid and semiarid areas, especially in mountain ranges is of significant cases in land use planning. Soil erosion in these areas is one of the important consequences of climate change, or in general, the obvious impacts of environmental and ecological changes. The erosion implications include soil loss, sediment deposition in reservoirs, and vegetation loss, and vegetation cover. Among the different cases of water erosion, gully erosion is one of the most important factors affecting soil destruction, land aspect change, and degradation of water resources and land setbacks. Due to the importance and role of gully erosion inland waste and land depletion, especially in the study area due to the existence of fertile agricultural land with high potential production and proximity of these lands to urban areas in Darrehshahr county, the aim of this study is identifying effective soil factors in the development of gully geometry characteristics and their prediction. Identifying effective factors in the development of this process can be an approach for helping managers and authorities to control and prevent the development of gully erosion.

There are many gully areas in the province of Ilam, where the study of Darrehshahr has been investigated. It is located in the Darrehshahr county and the eastern city of Ilam in the geographic range between 47° 30¢ 8² to 47° 46¢ 03² longitude and 33° 12¢ 14² to 33° 23¢ 03² latitude. Geological formation is composed of the sediments of the lake, which is caused by the landslide. It is a cold semi-arid climate that is based on synoptic stations at an average temperature of 21 C° and the average precipitation in the region is 497 mm. The basic data and statistics required are topographic maps of scale 1: 25,000 military organizations and the country's mapping organization, on a scale 1: 100,000 geological maps of the country, map of soil and land resources on a scale of 1: 25,000 of the institute for water conservation and soil. The measurement of gully geometric properties, including depth, length, high and low level, cross-sectional area, lateral slope, was done by surveying in the field using the Total survey camera at three cross-sections (head cut, median and outlet) of the gully. Then the region was divided into 12 work units, and in each case, three replicates were selected, and finally, 36 gullies were selected in the study area. The geometrical features of the gully using aerial photographs, digital maps and field operations, and physical-chemical properties of soil were determined using laboratory methods. To determine the impact of these factors on each of the geometric characteristics of the gully and the relationship between the dependent and independent variables was obtained using multivariate regression.

After grouping the gullies, the relationship of soil properties with geometry properties, including depth, length, Up and down width, was investigated based on the methods of correlation analysis and multiple regression models. The results showed that EC (3.43), SAR (7.55) and Na+ (3.95) have the highest impact factor and the most important role in the development of the gully. Whereas the amount of organic matter and total of Ca++Mg++, will be the role of adherence to the soil and prevent gully development. Also, based on the cross-section development model, the amount SAR (3.41), Na+ (2.87), and the Gypsum (2074) has the highest impact factor in the increasing cross-section of the gully.  But the amount of total of Ca++ and Mg++ (-0.36) and OM (-0.097), playing the most important role in the control and prevention of the gully development. The validation of the geometric characteristics of the gullies using statistical indexes (MRE and RMSE) was calculated. Due to very low RMSE values for both models, we can say that the proposed models are highly valid and can be used for regions with similar conditions of the study area.

Due to the importance and role of gully erosion in soil loss, especially in the case study area, this study aimed to identify effective soil factors in the development of protective gully characteristics. Therefore, according to the findings of this study, it was found that: Based on the conceptual model of extension of gully length, the amount of EC, SAR, and Na+ have the highest impact and the most important role in the development of the gully. Conversely amount of organic matter and total of Ca++Mg++, will be the role of adherence to the soil and prevent gully development. Based on the cross-section development model, the amount SAR and the Na+ have the highest impact in the increasing cross-section of the gully.  But the amount of gypsum and total of Ca++ and Mg++ playing the most important role in the control and prevention of the gully development.
Soil texture, especially silty texture, plays a more effective role in gulling and sedimentation of gullies.

The aim of this study is to investigate the relationship between rural partnership and poverty situation (in different social, economic and political aspects) in the rural district of Saravan, located in city of Rasht in Gilan province. Predictor variables in this sturdy include partnership (in different social, political and physical service aspects), attitude towards partnership, the approach to partnership and criterion variable includes poverty in political aspects. The type of research is applied-developmental and the study method is documentary, analytical and survey.In order to complete the information, a questionnaire is used. In analytical method, each desired component is introduced according to the proposed theory. Accordingly, each component with multi-reagents identify the degree of partnership and its role in rural development; then, components of poverty in political, economic and social aspects and the relationship between these two variables of partnership and poverty is investigated using multiple regression model in SPSS software. The statistical population of the study is all rural households in Saravan. 370 samples were chosen using Morgan table. The results indicate that the issue of poverty in different dimensions and levels of partnership are related.It means that each unit increase in partnership, 0/728 units reduce in political dimensions of poverty, respectively. In other words, by increasing each of the partnership indexes, the partnership approach and attitude towards partnership in social, political and economic dimensions of poverty reduce too.

Today’s global perspective of reducing damages has changed to increase resistance to disasters and taking steps towards building cities more resilient. Therefore, it is necessary to consider resilience as an integral part of community decision making. The present study has descriptive-analytical method and expansion – applied nature. Urban resilience indicators were investigated and prioritized in the region one of Tabriz metropolitan on the base of geographic information system, so this study has used quantitative and statistical methods such as multiple regression, structural equations and fuzzy multivariate analysis. Data were collected by using documentary studies and field surveys at household level, and according to Cochran formula the sample size was estimated to be 382 households. A total of 59 urban resilience indicators were evaluated and categorized in four economic, social, environmental and physical Latent indicators category. Using factor loadings tests, observed indicators were ranked and summarized according to the importance and effectiveness levels in the LISREL software and the fuzzy logic model in the form of GIS was used to prepare the map of influencing indicators. The results of the research indicated that all parts of the region of Tabriz metropolitan area have not the same resilience, so that northern neighborhoods are in the low level due to the above mentioned components, central neighborhoods are in the middle level and the southern and southeast neighborhoods are in a resilience higher level. On the other hand, among the indicators of the resilience, with respect to the degree of importance, it can be said that the economic index with 46.2%, the social index with 30.1%, the environmental index with 19.7%, and finally the physical index with the value of 4 percent, has explained the resilience of the whole study region. These results indicate the importance of the economic dimension in relation to other dimensions of resilience. Finally, strengthening the influencing indicators based on their priorities was emphasized by offering a number of recommendations.