The results of predicting employee absenteeism based an Ordinary Least Square (OLS) have been criticized. The present research compared the results from predicting absenteeism based on four varibales of job involvement, absenteeism compared to others, stress from life events, and number of children with the OLS and Tobit regression models. A sample of 197 male employees of factories in Esfahan city mean age: 28, SD : 10 responded to the Life Events Stress Inventory (Thoits, 1981), The Absenteeism Compared to Others Inventory (Baba, 1990), and the Job Involvement Inventory (Lodahl & Kejner, 1965). Tobit model analysis predicted employee absenteeism more precisely and explained a furhter 13% of the variance (R2 = 42.1) than did OLS.

Spatial modeling is one of the method for understanding and predicting environmental variables. soil surface moisture is a key variable for drought description, water and energy exchanges between earth and atmosphere. In addition soil moisture affects many environmental phenomena such as runoff, soil erosion, and crop production, due to the non-constant spatial and temporal conditions Environmental parameters is highly changeable. the purpose of this paper is to evaluate the overall regression model and geographically weighted regression in spatial modeling of soil moisture in Fars province. soil moisture distribution as dependent variable and precipitation, snow equivalent water, vegetation index and topographic wetness index were selected as independent variables and then, using the general regression model and geographically weighted regression is used to model the spatial modeling. based on the evaluation criteria, the results showed the GWR model has better explanatory power with the R2=0/71 and a better estimate than the overall regression model with the R2=0/66. The spatial factors of precipitation and topographic wetness had the most positive effect and evapotranspiration had a negative effect on soil moisture in the study area. Extended abstract Introduction soil surface moisture, in addition to its contribution to the hydrological cycle, is one of the most important components of the earth 's crust because it plays a controlling role between the earth 's surface and the atmosphere as well as water, energy and carbon. soil moisture plays an important role in the global energy cycle and controls the energy conversion process. researchers have shown that there is strong feedback between the soil and the climate of the region. spatial and temporal variations of soil moisture variability differ both at the surface and below the surface. although remotely sensed images have made good estimates of soil moisture in a large area, there is no systematic global network to monitor soil moisture. estimation of soil moisture from remote sensing data due to extensive coverage can optimize surface and surface moisture conditions in different time periods. soil moisture can be estimated by using variables and soil models. identification and modeling of the variables involved in soil moisture can be a key step in the prediction of the nasal. by examining the past researches, soil moisture can be obtained depending on several factors such as climate conditions and soil moisture conditions in the past which are very diverse in different locations. the importance of changes in soil moisture in the region and its relation with other variables has not much attention to the effect of other variables. complex relationships between variables affecting soil moisture can be understood and predicted by modeling. climate variables such as precipitation or evapotranspiration have a great impact on increasing and decreasing soil moisture. these variables have cause and effect relationship with soil moisture distribution map, so the need for modeling and determination of their role on soil moisture is revealed. the purpose of this study is to evaluate and model soil moisture behavior using two methods of public regression ( OLS ) and geographically weighted regression ( GWR ) and evaluate the accuracy of the models using model validation indexes. Materials and Methods Fars province is located in the south of the central region of Iran between the latitudes of "42 '31 °,27 to" 23 '37 °,31 north to "14 '32 °,55 to" 41 '30 °,50 east with an area of 122. 799 square kilometers. In this study, ECMWF database data were used for spatial analysis and modeling. The data of this database, starting from 1979, is becoming more complete every month, so that at the time of writing until April 2018, it has been released and ready to be downloaded. In this study, according to the studies of previous researchers and geographical studies, as well as cognition obtained from the spatial territory of the study area, the middle of rainfall climatic variables as the most important climatic variable-affecting soil and water moisture equivalent to snowmelt because part of the study area is mountainous. It was extracted after melting mountain snow and the resulting runoff could be a good source of soil moisture. The mean actual evapotranspiration layer was also obtained from ECMWF database data. A moisture topography variable that is used for quantitative studies of watersheds is a good indicator of soil moisture status. Regression modeling allows the relationship between independent and dependent variables to be identified and quantified. In ordinary linear and nonlinear regressions, it is assumed that the independent variables are the same throughout the study area, in reality this is not the case. In spatial regression, the coefficients of the independent variables are calculated to different degrees and it is assumed that they have more weight in places close to the complication. Among the regression models, the ordinary least squares regression method is the most common and simplest method. In spatial modeling by the OLS method, it is assumed that the coefficients or parameters of the statistical model are fixed to a place (geographical coordinates),therefore, the value of the dependent variable that is estimated by this model is the same for all parts of the region, which is considered as a weakness of this method in spatial modeling. There are several indicators to evaluate the validity and efficiency of regression models, some of which were used in this study: Coefficient of determination (R 2) and AICC criterion method (AICc). Results The average soil moisture in the study period was extracted as a dependent variable in modeling. The highest value is related to the northwest and the lowest value is related to the south and northeast of the province. The rainfall distribution was extracted as the most important independent climatic variables in soil moisture distribution in the study area shows the highest rainfall from mid-autumn to early spring and can indicate the maximum frequency of rainfall systems in this time range. Due to the mountainous nature of some areas, water equivalent to snow was studied as one of the independent variables. The median of evapotranspiration was also estimated as an independent variable. Vegetation indices may also indicate soil moisture. In this study, the average NDVI index in the study period extracted from Landsat 8 satellite images was obtained using the Google Earth Engine system. the province's rainfall, the vegetation index in the west and northwest is the maximum, and as it goes east and south of the province, the amount of this index decreases. By examining the equivalence of the independent variables by taking the correlation between the five independent variables and also examining the increase in the inflation index of VIF variance and examining the rate of change of regression coefficients by deleting or adding individual variables in a multivariate regression model, by performing about 6 transformations of events, the best transformation of the natural logarithm (ln (x)) was identified and used. Then the general least squares error (OLS) regression fitting was performed on the data. P-estimated = 1/75 + 0. 024416WI + 0. 3208 PR-0. 0164AET + 0. 002811NDVI + 0. 000724SWE Equation seven is a linear relationship between independent and dependent variables according to which independent variables can to justify and explain 66. 59% of soil moisture changes. According to the general regression. The rain variable layer has the greatest effect among the 5 variables used in modeling on soil moisture. In other words, with each millimeter of increase in rainfall, the soil moisture in the province may increase by an average of 0. 32 cubic meters per cubic meter. The second effective variable of topographic moisture index was extracted, which ranged from one to 0. 03, and by comparing this layer with the slope, it can be seen that wetter areas of the soil in the west and northwest of the province are fed by upstream moisture, which have more slope, while areas with moisture. Less east and northeast of the province, because of rainfall, are directed downwards. Evapotranspiration has a decreasing effect on soil moisture, because one of the ways to get water out of the soil is by using plants called transpiration and water consumption by evaporation from the surface of the soil. The next variable of water is equivalent to snow. The effect of this variable has shown itself more due to the snow-covered western and northern regions of the province. The vegetation variable has the lowest coefficient of rainfall in the general regression equation due to slow growth and delayed reaction. One of the most important factors for water penetration in the soil and creating moisture in it is the soil texture, i. e. its constituents of clay, silt and sand. In this research, a lot of effort was made to create a soil texture layer, but due to the size of the study area and the need for many samples for testing, this layer was not considered. Regarding the vegetation layer in this study, the average interval studied was considered. Conclusions In the present study, to identify the relationships between spatial factors and soil moisture dispersion, using the generated information, the average of each factor in each cell and according to the spatial characteristics of each cell, using conventional regression (OLS) and geographic balanced regression (GWR) techniques. Was modeled. First, a logarithmic transformation was performed on the data to remove the alignment and reduce the inflation index of variance, and then the general regression equation was fitted to the data. In this study, the results of geographic rhythmic regression model showed that the highest soil moisture is seen in the northwest. The most negative effects of evapotranspiration are seen in the east and southeast. Based on the error scattering map, the spatial regression model was able to explain more than 50% of soil moisture changes in more than 68. 04% of the area. Model residual maps also show a decrease in the amplitude of the GWR model residuals compared to the OLS model residues. However, the error rate was much lowerThe main advantage of spatial weighted regression method over the conventional regression method is its ability to study the spatial effect of variables. Finally, it can be said that using GWR forecasting maps, areas prone to significant decrease or a significant increase in soil moisture in Fars province can be identified and used to improve the decisionmaking process and forecast the service needs of relevant agencies. To improve this modeling of soil moisture, it is suggested to use the texture map and soil type as an independent layer in the modeling. Depth 10-12 cm (penetrates) to be used as independent variables. It is also recommended to use radar images that have high resolution to extract the moisture layer with high resolution for more detailed examination.

Due to the importance of the import management, this study applies generalized ARDL approach to estimate MIDAS regression for wheat import value and to compare the accuracy of forecasts with those competed by the regression with adjusted data model. Mixed frequency sampling models aim to extract information with high frequency indicators so that independent variables with lower frequencies are modeled and foorcasted. Due to amore precise identification of the relationships among the variables, more accurate prediction is expected. Based on the results of both estimated regression with adjusted frequency models and MIDAS for the years 1978-2003 as a training period, wheat import value with internal products and exchange rate was positively related, while the relative price variable had an adverse relation with the Iran's wheat import value. Based on the results from the conventional statistics such as RMSE, MAD, MAPE and the statistical significance, MIDAS models using data sets of annual wheat import value, internal products, relative price and seasonal exchange rate significantly improves prediction of annual wheat import value for the years 2004-2008 as a testing period. Hence, it is recommended that applying prediction approaches with mixed data improves modeling and prediction of agricultural import value, especially for strategic import products.

Objective: In this study, the ordinary least squares (OLS) regression model was applied to estimate the impacts of  land use and land cover (LULC) changes from 1999 to 2023 derived from Landsat satellite imagery on water productivity as a key ecosystem service essential for environmental sustainability. Focusing on the Lavasanat district in Tehran province, which has undergone rapid urbanization and severe land use/cover changes, this study determined the extent to which water production performance (runoff) responded to land use/cover changes, thereby providing significant information on the environmental consequences of land use/cover changes under increasing human pressures.Method: This study used Landsat satellite imagery to assess the trends in land use/cover (LULC) changes over time at a spatial resolution of 30 m. Water yield modeling was performed using the annual water yield index of the InVEST software. The model inputs included land use and cover maps from three different time series along with data on precipitation, potential evapotranspiration, soil depth, water availability for plants, and local biophysical tables. The results from the InVEST model were analyzed using an ordinary least squares (OLS) regression model to estimate the impact of land use/cover changes on water yield. This method allows for a detailed examination of the relationship between land use/cover changes and their impacts on the water yield.Results: The results showed that between 1999 and 2023, the area of green spaces, including agricultural lands, gardens, and pastures, decreased by 161.21 km², or 31 percent. Consequently, the annual water production (runoff) increased from 105 to 130 million cubic meters. In addition, the area of the minimum error zone decreased from 445.3 to 23.5 km², indicating a decrease in the reliability of the model. Such findings indicated the high variability and complexity of hydrological interactions and indicated the severe effects of overdevelopment and increasing land use/cover pressures.Conclusions: The results of this study showed that the water production capacity (runoff) of the ecosystem was vulnerable to changes in land use and land cover. These changes increased runoff, reduced water permeability in the soil, and disrupted the hydrological balance of the region. Therefore, it is necessary to use integrated modeling approaches and simultaneously pay attention to climate change, socio-economic factors, and land use/cover planning. Regional planning should also emphasize preservation and restoration of green spaces and smart management of urban development, which is an inevitable necessity to maintain ecosystem resilience and water resource sustainability.

The purpose of this research is to predict the amount of ground subsidence in Mashhad city. In order to achieve this goal, the D-insar technique of radar interferometry has been used. Sentinel 1 satellite data was done in the Snap8.1 software environment. Also, using the Ols model, the spatial analysis of factors affecting subsidence was done. In this research, an attempt has been made to carry out a spatial analysis of the subsidence of the city of Mashhad and to determine the extent and trend of its expansion during a period of 3 months in 2022. The effective factors of subsidence in this research include land use, distance from the fault, changes in the underground water level, distance from the river, elevation, slope and soil. The results of this method show that the rate of subsidence in Mashhad is increasing. In addition, during this period, the size of these areas has increased and the process of expansion towards the western areas is moving. Also, the results show that the highest amount of subsidence happened in the western and northwestern parts. The spatial comparison of subsidence with the level of underground water showed that in order to predict subsidence, the role and effect of other factors controlling subsidence should be obtained. According to the ols model, which sig shows a significant relationship between 7 effective factors, the most influential factor in the subsidence of the underground water level and the least influential factor of the subsidence was the slope

Objective: Land subsidence is one of the most important environmental hazards that has affected many plains of the country today. The purpose of this research is to predict the amount of ground subsidence in Mashhad city. Methods: In order to achieve this goal, the D-InSAR technique of radar interferometry has been used. Sentinel 1 satellite data was done in the Snap8. 1 software environment. Also, using the OLS model, the spatial analysis of factors affecting subsidence was done. In this research, an attempt has been made to carry out a spatial analysis of the subsidence of the city of Mashhad and to determine the extent and trend of its expansion during a period of 3 months in 2022. The effective factors of subsidence in this research include land use, distance from the fault, changes in the underground water level, distance from the river, elevation, slope and soil. Results: The results of this method show that the rate of subsidence in Mashhad is increasing. In addition, during this period, the size of these areas has increased and the process of expansion towards the western areas is moving. Also, the results show that the highest amount of subsidence happened in the western and northwestern parts. The spatial comparison of subsidence with the level of underground water showed that in order to predict subsidence, the role and effect of other factors controlling subsidence should be obtained. According to the OLS model, which sig shows a significant relationship between 7 effective factors, the most influential factor in the subsidence of the underground water level and the least influential factor of the subsidence was the slope. Conclusions: The subsidence event is due to excessive extraction of groundwater in most plains and cities of Iran, such as Mashhad. Given that the main and fundamental cause of the subsidence of Mashhad is the excessive use of groundwater and groundwater levels, proper management of groundwater extraction should be carried out by the relevant organizations.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.

Using the quantitative tools, methods and techniques in various sciences has been expanded during the recent years. The quantitative methods’ utilization in different branches of Humanities, especially the urban and regional planning have been always faced to various challenges. The reason of generated challenges is the complex nature of the human behaviors. Ordinary least Squares (OLS) is one of the popular methods in spatial model domain. It is supposed, in this method, that there is no spatial anisotropy among the observations and the spatial dependence doesn’ t exist among the noise terms. It can be seen, in spatial data, using of the general regression methods such as Ordinary Least Squares (OLS) and will cause the model parameter dispersion. So it is necessary to use some other spatial modelling methods such as Geographically Weighted Regression (GWR). The experimental studies, have been done in this domain, reveal that the spatial regression methods can consider the spatial anisotropy among the observations and the noise terms dependence and will cause the estimations without the swearing and compatible with the parameters of the statistical society.