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Knowing the rate of penetration of a drill bit in rocks is among the most important parameters in their behaviour measurement. However, the direct measurement of ROP in rocks is a high-cost and time-intensive process. Therefore, obtaining the ROP parameter through a method other than direct measurement can be very useful and effective. Predictive machine learning methods are among the strong and precise techniques for the indirect measurement of ROP. To this end, 492 samples were tested under different UCS, µ, WOB, and ω conditions to obtain the corresponding ROP. To achieve an accurate model, three methods of linear regression analysis, lasso regression, and ridge regression were compared in terms of prediction accuracy. These models were compared through performance criteria of the prediction process and error-based charts. The performance criteria were measured using three measures: mean absolute percentage error, D-squared pinball score, and mean Poisson deviance error. For the MAPE index, the Lasso and Ridge models performed the best with values of 0.2557. Concerning the D2PS index, the linear regression model and Ridge performed better with values of 0.4083 and 0.4025, respectively. Finally, for the MPDE index, the Ridge model provided a more accurate performance with a value of 0.0105. For a better comparison, an objective function was created and calculated by combining these three indicators. The results showed the best rank for the Ridge model with an estimated value of 659.475. Finally, it was concluded that the Ridge model is a reliable and accurate model for predicting the ROP.

Prediksi penjualan menjadi elemen penting dalam perencanaan perusahaan karena akan menentukan rencana anggaran penjualan, yang mempengaruhi banyak faktor dalam perusahaan. Produsen penjualan sering kali mengalami kesulitan untuk memprediksi kisaran jumlah produk yang terjual pada periode yang akan datang. Permasalahan tersebut mengakibatkan ketidakefektifan dalam pengelolaan stok dan jadwal produksi, sehingga stok produk digudang menumpuk yang mengakibatkan beberapa produk mengalami kerusakan karena disimpan terlalu lama. Tujuan dari penelitian ini adalah mengimplementasikan metode Simple Linear Regression untuk memprediksi penjualan produk agar dapat mengetahui tentang kisaran jumlah penjualan produk di periode yang akan datang, sehingga dapat menyesuaikan penyedian stok yang lebih efektif berdasarkan prediksi yang diperoleh. Metode Simple Linear Regression digunakan untuk mengevaluasi dan memahami arah serta kekuatan hubungan antara dua variabel, yaitu variabel independen (X) dan variabel dependen (Y). Parameter yang digunakan adalah periode bulan dan jumlah penjualan. Periode bulan yang merujuk pada rentang waktu, bagaimana perubahan waktu dapat memengaruhi jumlah penjualan produk sedangkan jumlah penjualan merupakan output yang menjadi fokus utama, mengetahui sejauh mana variabilitas dalam periode bulan dapat dijelaskan oleh variasi dalam jumlah penjualan produk. Dataset yang digunakan untuk uji tingkat kesalahan terhadap hasil prediksi menggunakan data penjualan mulai periode Februari 2021 sampai dengan September 2023. Hasil uji menyatakan nilai rata-rata dari selisih absolut antara nilai prediksi atau MAD sebesar 3,778563, rata-rata dari kuadrat selisih antara nilai prediksi dan nilai aktual atau MSE sebesar 21,661444 dan rata-rata persentase kesalahan absolut antara nilai prediksi dan nilai aktual atau MAPE sebesar 12%. Berdasarkan nilai MAPE yang diperoleh, dapat disimpulkan bahwa prediksi penjualan untuk penjualan produk ini dapat dikategorikan Baik. Hasil penelitian ini dapat dijadikan rekomendasi untuk memprediksi kisaran jumlah penjualan produk diperiode yang akan datang agar menyesuaikan persediaan stok, anggaran dan jadwal produksi.

Traditional approaches to monitoring water quality in aquaculture tanks present numerous limitations, including the inability to provide real-time data, which can lead to improper feeding practices, reduced productivity, and potential environmental risks. To address these challenges, this study aimed to create an accurate water quality monitoring system for Asian seabass fish farming in aquaculture tanks. This was achieved by enhancing the accuracy of low-cost sensors using simple linear regression and validating the IoT system data with YSI Professional Pro. The system's development and validation were conducted over three months, employing professional devices for accuracy assessment. The accuracy of low-cost sensors was significantly improved through simple linear regression. The results demonstrated impressive accuracy levels ranging from 76% to 97%. The relative error values which range from 0.27% to 4% demonstrate a smaller range compared to the values obtained from the YSI probe during the validation process, signifying the enhanced accuracy and reliability of the IoT sensor by using simple linear regression. The system's enhanced accuracy facilitates convenient and reliable real-time water quality monitoring for aquafarmers. Real-time data visualization was achieved through a microcontroller, Thingspeak, Virtuino application, and ESP 8266 Wi-Fi module, providing comprehensive insights into water quality conditions. Overall, this adaptable tool holds promise for accurate water quality management in diverse aquatic farming practices, ultimately leading to improved yields and sustainability.

One of the most widely used robust regression methods for solving simple linear regression problems is the Theil-Sen (TS) estimator. This estimator has some notable advantages; however, it does not belong to the most robust estimation methods (called high-breakdown estimators) and is prone to outliers whose distribution is highly asymmetric with respect to the correct data points. This paper presents a modification of the TS estimator, the Robustified Theil-Sen (RTS) estimator. The new method uses a heuristic-based selection procedure to reduce the number of initial estimates of the regression function parameters computed with at least one outlier, thereby improving the regression results. The use of this heuristic procedure only slightly increases the computational time required for using the RTS estimator compared to the TS estimator. Preliminary results of two numerical experiments presented in the paper show that the RTS estimator outperforms other comparable estimators, i.e., the TS estimator and the repeated median estimator, in terms of robustness. The results presented also suggest that the breakpoint value (which is a measure of the robustness of estimators) of the RTS estimator is higher than the breakpoint value of the TS estimator and equal to the breakpoint value of the high-breakpoint estimators.

Exposure measurement error is a pervasive problem for epidemiology research projects designed to provide valid and precise statistical evidence supporting postulated exposure-disease relationships of interest. The purpose of this commentary is to highlight an important real-life example of this exposure measurement error problem and to provide a simple and useful diagnostic tool for physicians and their patients that corrects for the exposure measurement error. More specifically, prostate-specific antigen doubling time (PSADT) is a widely used measure for guiding future treatment options for patients with biochemically recurrent prostate cancer. Numerous papers have been published claiming that a low calculated PSADT value (denoted PSADT̂) is predictive of metastasis and premature death from prostate cancer. Unfortunately, none of these papers have adjusted for the measurement error in PSADT̂, an estimator that is typically computed using the popular Memorial Sloan Kettering website very often visited by both physicians and their patients. For this website, the estimator PSADT̂ of the true (but unknown) PSADT for a patient (denoted PSADT∗) is computed as the natural log of 2 (i.e., 0.6931) divided by the estimated slope of the straight-line regression of the natural log of PSA (in ng/mL) on time. We utilize PSADT̂ to derive an expression for the probability that the unknown PSADT∗ for a patient is below a specified value C (>0) of concern to both the physician and the patient. This probability is easy to interpret and takes into account the fact that PSADT̂ is a statistical estimator with variability. This variability introduces measurement error, namely, the difference between a computed value PSADT̂ and the true, but unknown, value PSADT∗. We have developed an Excel calculator that, once the [time, ln(PSA)] values are entered, outputs both the value of PSADT̂ and the desired probability. In addition, we discuss problematic statistical issues attendant with PSADT∗ estimation typically based on at most three or four PSA values. We strongly recommend the use of this probability when physicians are discussing PSADT̂ values and associated treatment options with their patients. And, we stress that future epidemiology research projects involving PSA doubling time should take into account the measurement error problem highlighted in this Commentary.

Inaccessibility to extension services by smallholder farmers remains one of the impediments to achieving high agricultural productivity and food security. Extension services play a critical role in information dissemination that can avert food insecurity and increase smallholder dairy farmers' incomes. However, access to extension services remains a significant challenge in developing countries. This study investigated the influence of access to extension services on milk productivity among smallholder dairy farmers in Njoro Sub-County, Nakuru County, Kenya. The study's target and accessible population was 17,000 smallholder dairy farmers. The study used simple random and proportionate sampling techniques to select study farmers. Nassiuma's formula generated a sample of 120 smallholder dairy farmers. The hypothesis underwent testing using simple linear regression. The regression results found a statistically significant influence between access to extension services and milk productivity at a 5% significance level (p

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R2). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R2 in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R2: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R2: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

Structural Health Monitoring (SHM) is a valuable tool for keeping track of the performance and health of engineering structures over extended periods. The purpose of SHM is to ensure structural safety by relying on information derived from real-time measured data and offer suitable recommendations for the structure's maintenance and management. The fundamental idea of SHM is that the structure's response to vibrations will change noticeably if its mass or stiffness is altered. It is a way to keep tabs on how well a building keeps itself safe and sound with as little human intervention as possible. Throughout this research, a Finite Element (FE) model was updated, and an ambient vibration test was performed on an Ultra-High-Performance Concrete (UHPC) bridge, given its slimmer as well as lighter properties to get modal parameters, including natural frequency and mode shape. Measured modal parameters were acquired and verified with the aid of several Operation Modal Analysis (OMA) techniques, and the FE model has then modified accordingly. The bridge's condition was evaluated and predicted using a mathematical method called simple linear regression, which considered the variations in natural frequency produced by the structure's mass and stiffness. The findings indicate that the stiffness index ratio is a practical and efficient tool for evaluating the bridge's effectiveness. The applied method demonstrated the Sungai Raia bridge's safe functioning in real-world situations.

Paying attention to the welfare level of soldiers is also an important task in building and maintaining morale and fighting spirit in the military. This study aims to examine the effect of the Service Housing Provision Program (PPMD) on the level of welfare of Indonesian Navy soldiers. The method used is a survey with a simple random sampling technique and data analysis using simple linear regression. The results of the study show that PPMD ​​has a positive and significant influence on the welfare of TNI AL soldiers, with an R Square value of 0.463. These findings show that PPMD ​​can increase the level of welfare of TNI AL soldiers, so efforts need to be made to continue to improve the quality of the PPMD ​​program in providing housing for TNI AL soldiers.

Estimating the height component of Global Navigation Satellite System (GNSS) stations is widely known to be more challenging than estimating the horizontal position. In this study, we utilized height time series data from 37 continuous GNSS stations that were part of the Turkish RTK CORS Network called TUSAGA-Active (Turkish National Permanent GNSS Network Active). The data covered the period from 2014 to 2019, and the selection of stations focused on the Eastern Anatolia region of Turkey due to its topographic characteristics and the pronounced influence of seasonal changes, which facilitated the interpretation of the effects on the height component. The daily coordinates of the GNSS stations were derived using the GAMIT/GLOBK software solution. We identified statistically significant trends, periodic variations, and stochastic components associated with the stations by applying time series analysis to these daily coordinate values. As a result, the vertical velocities of the GNSS stations were determined, along with their corresponding standard deviations. Furthermore, examining the height components of the continuous GNSS stations revealed seasonal effects. We aimed to investigate the potential relationship between these height components and meteorological parameters. The study provides evidence of the interconnectedness between the height components of continuous GNSS stations and various meteorological parameters. Simple linear regression analysis and ARMA time series modeling were utilized to establish this relationship.

The energy crisis has become a global issue. It can be seen in various countries worldwide experiencing an energy crisis. The energy crisis and global warming encourage countries to use renewable energy sources significantly from time to time. Based on historical data of installed capacity, it can be calculated the installed capacity in 2025 using a simple linear regression method. Renewable energy generating capacity is obtained from this installed capacity, meeting 23% of the total. The results of a simple linear regression show that in 2025, the installed capacity of renewable energy plants is 22.66%. It means that there is a deviation of 0.34% from the target. Meanwhile, for 2050, the achievement of the energy mix is 28.47%, which means that it is 2.53% lower than the target of 31%. Conformity between the general national energy plan and the regional one is essential.

Abstract Rock drilling is an essential operation in mining industries. Temperature at the bit-rock interface plays a major role in the wear rate of the drill bit. This paper primarily focuses on the wear rate of tungsten carbide (WC) drill bit and the interrelationship between temperature and wear rate during rotary drilling operations conducted using a computer numerical control (CNC) machine. The interrelationship between the temperature and wear rate was studied with regard to three types of rock samples, i.e., fine-grained sandstone (FG) of uniaxial compressive strength (UCS) that is 17.83 MPa, medium-grained sandstone (MG) of UCS that is 13.70 MPa, and fine-grained sandstone pink (FGP) of UCS that is 51.67 MPa. Wear rate of the drill bit has been measured using controlled parameters, i.e., drill bit diameter (6, 8, 10, 12, and 16 mm), spindle speed (250, 300, 350, 400, and 450 rpm), and penetration rate (2, 4, 6, 8, and 10 mm/min), respectively. Further, a fully instrumented laboratory drilling set-up was utilized. The weight of each bit was measured after the bit reached 30 mm depth in each type of the rock sample. Furthermore, effects of the bit-rock interface temperature and operational parameters on wear rate of the drill bits were examined. The results show that the wear rate of drill bits increased with an increase in temperature for all the bit-rock combinations considered. This is due to the silica content of the rock sample, which leads to an increase in the frictional heat between the bit-rock interfaces. However, in case of medium-grained sandstone, the weight percentage (wt%) of SiO2 is around 7.23 wt%, which presents a very low wear rate coefficient of 6.33×10−2 mg/(N·m). Moreover, the temperature rise during drilling is also minimum, i.e., around 74 °C, in comparison to that of fine-grained sandstone and fine-grained sandstone pink. In addition, this paper develops the relationship between temperature and wear rate characteristics by employing simple linear regression analysis.

Maps are a primary method of displaying statistical data that comes from a geographical frame. Maps are esthetically appealing and make it easier to identify geographic patterns in a dataset. However, few introductory statistical texts and courses explicitly present maps as a way to display data. In this article, we will present examples of different types of statistical maps and illustrate how these maps can be used in the instruction of an introductory statistics course.

The anthropometric measurements needed for school furniture assessment can be difficult, time-consuming, and expensive to obtain. However, assessment can be important since sitting in the wrong position too long on inappropriate school furniture may lead to negative health effects in both the short- and long-term. Therefore, this paper proposes a relatively simple methodology to evaluate school furniture suitability using only height and weight measurements and simple linear regression models for the relevant anthropometric values. The models were used to examine possible incompatibility between student body dimensions and the dimensions of school furniture. The results obtained by the proposed method were confirmed by repeating the furniture assessment using actual anthropometry data from the population which yielded mis-match differences of 8% or less.

The study is part of agricultural studies that address the productivity problem of the two most prominent types of dates in areas known to have been cultivated since ancient times. Palm cultivation and date production are constantly deteriorating in most of its production areas, so the study summarized the spatial variation of the production of Al-Zahdi and Al-Khistawi in iraq's production provinces for the period (2010-2019), to determine the volume of production and future predictions using approved statistical methods. The study was followed by two main approaches (geospatial analysis method) to determine spatial variations in production (and the inference analysis method) as the simple linear regression model was chosen to find the predictive value of its production in the study area by relying on a time series of production quantity (2010-2019) by provinces and for the two categories through a set of results such as correlation value coefficient and F value of the overall morality of the regression equation and the value (t) of the partial parameters of the parameters. According to this model, the production of the two categories was predicted until 2025, and the results showed a spatial and temporal variation in production at the level of the producing provinces. The hierarchical cluster analysis was used to divide the provinces into clusters with common elements and characteristics in production and which is unique from the rest of the provinces by producing one of the categories, indicating the specificity of these provinces in their production in quantity and quality. Based on the data available from the Ministry of Planning/Central Bureau of Statistics/Directorate of Agricultural Statistics on production volumes at the level of the provinces producing for the calculated period. The study included many tables, charts and illustrative maps, the study also came up with a number of conclusions and proposals, and the study relied on many important sources in the topic.

The purpose of this paper is to investigate the strength, physical and engineering index parameters of selected dolomitic rocks with emphasis on grain size. For this purpose, three groups of dolomite from north western Iran, with the same mineral composition but different grain size, were selected; fine grain, medium grain and coarse grain. Three sets of laboratory experiments are performed on 32 samples: first; petrography tests for determining mineral composition and their percentage, and microstructure of rock containing grain size and grain size distribution, second; experiments to determine the physical properties of the rocks included density, compressional and shear wave velocity, and the third category of experiments included uniaxial compressive strength test, Brazilian tensile strength and point load strength. According to the results; there are significant positive correlation between grain size and uniaxial compressive strength (r = 0.89), point load strength (r = 0.58), Brazilian strength (r = 0.69), and average Young’s modulus (r = 0.64). Also, with increasing grain size, density decreases (r = –0.77). There is strong correlation between compressional wave velocity and shear velocity (r = 0.88). There are also a strong correlation among the uniaxial compressive strength, Brazilian tensile strength and point load strength.

Nowadays, Internet banking is a service provided by all Romanian banks which has become a daily need for millions of users. This study contains a brief overview of the Internet banking services usage level in the European Union for the period 2007-2016. An increasing in the use of Internet banking services in the EU can be observed at the level of the analysed period. At the same time we compared the users (individuals aged 16 to 74) of Internet banking services to other European countries and we found that the users of the Internet banking services in Romania are still far below the European average but the trend is an upward one. In this study we have focused our attention only on Internet banking, as it is the most commonly used payment instrument in the Romanian practice of remote access payment instruments. The indicators considered in this study were the Internet banking users and the Internet users. We have used the model of the simple linear regression to highlight the existence of a relationship between the variables. In this respect we used statistical data taken from Eurostat and Romanian Ministry of Communications and Information Society as well as data provided by the Romanian National Institute of Statistics for the period 2009-2014. The results of the model have pointed out the fact that the rise of the number of Internet users leads to an increase of the number of Internet banking users.

The goal of this article is to point at the possibilities of estimating the beta coefficient by means of accounting variables. Its advantage is that it can be applied for the companies which shares are not traded at the stock exchange, or for newly established companies (e.g. start-ups) with no history of their activities and with very limited possibilities of risk diversification. We are of the opinion that these data can be considered as one of the most appropriate means of estimating the overall risk of the particular company. Beta coefficient estimate is not based on the market information, but on the accounting data of the company being analysed. By testing of the Brimble-Hodgson model on the constructing area we have identified significant variables with serious influence over the systematic risk.

Software size is one of the most important inputs of many software cost and effort estimation models. Early estimation of software plays an important role at the time of project inception. An accurate estimate of software size is, therefore, crucial for planning, managing, and controlling software development projects dealing with the development of software games. However, software size is unavailable during early phase of software development. This research determines the utility of CK (Chidamber and Kemerer) metrics, a well-known suite of object-oriented metrics, in estimating the size of software applications using the information from its UML (Unified Modeling Language) class diagram. This work focuses on a small subset dealing with board-based software games. Almost sixty games written using an object-oriented programming language are downloaded from open source repositories, analyzed and used to calibrate a regression-based size estimation model. Forward stepwise MLR (Multiple Linear Regression) is used for model fitting. The model thus obtained is assessed using a variety of accuracy measures such as MMRE (Mean Magnitude of Relative Error), Prediction of x(PRED(x)), MdMRE (Median of Relative Error) and validated using K-fold cross validation. The accuracy of this model is also compared with an existing model tailored for size estimation of board games. Based on a small subset of desktop games developed in various object-oriented languages, we obtained a model using CK metrics and forward stepwise multiple linear regression with reasonable estimation accuracy as indicated by the value of the coefficient of determination (R^2 = 0.756).Comparison results indicate that the existing size estimation model outperforms the model derived using CK metrics in terms of accuracy of prediction.

Electric power load forecasting is an essential task in the power system restructured environment for successful trading of power in energy exchange and economic operation. In this paper, various regression models have been used to predict the active power load. Model optimization with dimensionality reduction has been done by observing correlation among original input features. Load data has been collected from a 33/11 kV substation near Kakathiya University in Warangal. The regression models with available load data have been trained and tested using Microsoft Azure services. Based on the results analysis it has been observed that the proposed regression models predict the demand on substation with better accuracy.

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).

The research aims to determine the financial performance of the companies listed and traded on the Bucharest Stock Exchange from the manufacturing sector in Romania, compared with the performance recorded by the Bucharest Stock Exchange, based on the exchange rates. It was concluded that the financial performance of the companies included in the research, quantified on the basis of the exchange rates, decreased significantly with the arrival of the financial and economic crisis, currently, the companies being unable to reach the level of performance recorded before the crisis.

In line with the development of the city, the insistence on changes to the natural spaces and undeveloped lands, such as for housing, offices, shops, roads and parking space, and others becomes higher. This condition will effect the decreasing of space for agriculture, vegetables planting and horticulture. On the other side, products of agriculture and horticulture are needed for food. A special case in North Sumatera, the development of highlight/featured sector (rubber and palm planting) also need more space. So, thereupon a competition to get land between many activities in North Sumatera. The purpose of research is to analyze the need for space in North Sumatra Province in comparison to the availability and suitability of the land. In this study, the requirement of space is calculated by the amount of the food needs of the entire population of North Sumatra Province which is adapted to Target Consumption of Food Top North Sumatra Province in 2025, the amount of land area required for settlements that are tailored to the population of North Sumatra Province in 2025, the amount of land area needed for the development of the production of palm oil plantations as the leading sectors of North Sumatra Province, the vast amount of land required for the development of agricultural production and horticulture to meet the needs of residents of North Sumatra Province until 2025. Forecasting the number of agricultural production is using simple linear regression. Suitability and land capability are measured through the depiction of Unit Ability Land (SKL) that will show the suitability of land for protected areas, residential areas, forests, mangroves, cattle grazing, agriculture annuals, perennial plants, and others. The results of the analysis showed that there will be a shortage of land for agriculture in 2025 in the amount of 596,485.44 ha due to the conversion of agricultural land to residential areas, plantations, and others. Based on the suitability of land for agricultural seasonal wet and dry land, there are 1,410,363.07 hectares of land are appropriate for planting vegetables in 2025. In 2025, also will be a shortage of land for housing amounted to 185,638.54 ha. Another finding is that if productivity remains, then by 2025, rice production in North Sumatra Province can meet the needs of rice for food security as long as the harvest of the land until 2025 is provided.

The objective of this article consists of an analysis of Internet Banking services in Romania, especially of Internet Banking web based services, Home Banking and Mobile Banking services, form the point of view of number of users, number of transactions in RON and value of transactions in RON, in 2009-2014 period. Although an intense development of the Romanian market of Internet Banking services has been observed in the recent years, Romania is still at the beginning of this process, comparing to other states, yet with a potential of considerable progress. The more people will use internet services, the more likely this category of services is to develop. As Internet Banking web based indicator is one of the most important, its analysis goes further by studying the correlation between the value of transactions and number of users of this service by using the model of simple linear regression which stressed out a powerful and direct relation between these two indicators.

This paper assesses profitability of small scale local shea butter production in Ghana. The study used cross-sectional data from 110 sampled shea butter processors randomly selected from Kaleo in the Upper West region of Ghana. Profitability methods such as gross margins, net incomes and net returns on investments of production units were employed to evaluate the profitability of shea butter producers. Simple linear regression model was also employed to estimate identified determinants of profitability using gross margin as the dependent variable. The results showed that shea butter making in the study area is profitable with positive average values of gross margins, net incomes and net returns on investments. Access to market information, access to credit, length of production cycle (time), savings, experience and household size were found to be significant determinants of gross margin. It is recommended that existing producer groups/cooperatives where members interact to gain understanding of the market trend besides, negotiations on prices and other terms of trade should be strengthened to attract more members, prioritize business-development through routine training and also explore market opportunities both locally and internationally. Furthermore, microcredit and financial institutions should develop low cost lending models in order to provide affordable credit to small scale local shea butter processors in Ghana.

The increasing search for a reduction in air emissions has provided a challenging analytical scenario. This paper discusses metrologically, the best practices relating to analytical curves for the determination of very low concentrations of sulfur content in diesel S-10 by monochromatic wavelength dispersive X-ray fluorescence spectrometry (MWDXRF). For weighted linear regression, a new strategy to correlate the weights with the output variable is proposed, which is lacking in the literature. The analytical curve 0-500 mg kg-1, built in compliance with international standards, featured heteroscedastic behavior. The results showed that weighted linear regression is the most suitable choice for assessing concentrations at the beginning of an analytical curve, such as 10 mg kg-1 (product specification limit) rather than simple linear regression which holds that the measured results must be closest to the centroid of the regression line. Finally, it was observed that, when a shorter analytical curve is built, it loses both in accuracy and in precision for very low concentrations, as compared to t simple and weighted linear regression.

This study examines the predictive role of meaning in life and gender-specific differences on psychological well-being of 226 Spanish undergraduates (87 men, 38.5%; 139 women, 61.5%) ranging in age from 17 to 25 years, M = 21.08, SD = 2.18. Measures included both the Spanish adaptations of the Crumbaugh and Maholic's Purpose-In -Life Test and the Ryff's Scales of Psychological Well-Being. The hypothesis stated that meaning in life would predict psychological well-being and that women would reach a higher score in several dimensions of psychological well-being. Statistical analysis included simple linear regressions, and a t-test. Results showed that: (1) meaning in life was a significant predictor variable of psychological well-being, especially of global psychological well-being, self-acceptance, purpose in life, and environmental mastery; and (2) women reached a higher score, statistically significant, in global psychological well-being, environmental mastery, personal growth and purpose in life. Findings were discussed in the light of previous researches.

Background: Predicting the size of unerupted teeth during the mixed dentition period is a critical factor in managing the developing occlusion. Different studies found that the combined width of only the four mandibular permanent incisors is not a good predictor of the sum of unerupted mandibular permanent canines and premolars (SPCP). In 2007, Melgaço et al. developed a new method for SPCP by measuring the sum of the mandibular first permanent molars and four mandibular permanent incisors (SMI). Aim: It was aimed to evaluate the accuracy of this new method in comparison with Moyers′ mixed dentition analysis table in contemporary Indian population. Settings and Design: Sixty boys and 60 girls from Gandhinagar district (age ranged from 12 to 14 years) were included. Materials and Methods: The mesiodistal crown widths of all fully erupted teeth were measured with digital vernier callipers and the odontometric values obtained were then subjected to statistical and linear regression analysis. Results: Student′s unpaired t-test gave statistically significant difference between the original values of teeth and the values obtained by Melgaço′s prediction equation as well as Moyers′ mixed dentition analysis table (P < 0.001). High values of correlation (r = 0.77) and determination coefficients (r2 = 0.59) were found while considering Melgaço′s method. Also, no statistically significant difference was found between the tooth sizes of males and females. Conclusion: From this study, it can be evaluated that Melgaço′s method gives better prediction and a simplified equation Y = 0.925X can be suggested for the present population.

BACKGROUND & OBJECTIVE: Modeling is one of the most fundamental methods of denoting statistical variables which by using it, we can found distribution of noted response variable. For analysis data same as length of hospital stay (LOS), we have not data normality and error variances homogeneity, so, we can use nonparametric methods or distribution correction likes logarithmic transferring for using parametric methods. According to experiences in this situation, considering mixture distributions approximately can improve goodness- of- fit distribution. The goal of this study was to introduce mixture Poisson modeling and using mixture Poisson regression models for explaining duration of patient hospitalization in hospital and gaining effective factors on this time of duration and also comparing these models with common regression models in these data.METHODS: After interdicting mixture Poisson modeling and its regression, we applied these models for modeling LOS in two wards in Arak Vali-e-Asr hospital. Variables of age, marriage status, birth location and living location as independent variables and duration of hospitalization in hospital as countable response variable were considered and LOS was considered as response variable for application these models.FINDINGS: The findings have shown that in base Log-likelihood value and more dispersion LOS data in the surgical ward mixture Poisson model was a suitable for explain LOS with the other variables and in internal ward the variation of hospitalization time is not great, so this model cannot describe this variable explanation.CONCLUSION: By consideration Log-likelihood value and variation of LOS in surgical ward, the Poisson mixture model is a good model for describing this variable. By using general models, the Log-likelihood value is more than mixture Poisson modeling and there are less significant factors in models. Application of these models in cases which the countable response variable has great variation, is recommended.

La aplicación de un análisis de regresión lineal simple a datos de lluvia de polen y de vegetación, en una zona de la Patagonia (Provincia de Chubut, Argentina) permitió relacionar cuantitativamente los porcentajes de lluvia polínica y de cobertura de la vegetación para los taxa Poaceae, Papilionoideae, tipo Senecio y Mulinum spinosum. Se obtuvo un buen ajuste de las rectas de regresión para cada uno de los taxa considerados, con coeficientes de correlación r de Pearson altos para M. spinosum (0,82) y tipo Senecio (0,81) y coeficientes algo menores en el caso de Poaceae (0,66) y Papilionoideae (0,61). Estas regresiones permitieron inferir la presencia o ausencia de polen regional y el valor predictivo de la presencia de polen en ausencia de la vegetación que lo produce. Al respecto, la lluvia de polen estudiada incluye el aporte regional (excepto Papilionoideae) aun cuando M. spinosum tiene una baja contribución. Esta sobrerrepresentación es atribuible al transporte de larga distancia, a través de los vientos que provienen del oeste. Estas relaciones cuantitativas nos permitirán extrapolar cambios vegetacionales pasados en estos taxa a partir de espectros de polen fósil para el área de estudioThe use of simple linear regression to data of pollen rain and vegetation cover in Patagonia (Province of Chubut, Argentina) allowed us to develop quantitative relationships between modern pollen rain perecentages and vegetation cover for the taxa Poaceae, Papilionoideae, Senecio type, and Mulinum spinosum. The analysis revealed a good fit of data to regression lines for all taxa, with high Pearson r coefficient values in the case of M. spinosum (0.82) and Senecio type (0.81), and somewhat lower values in the case of Poaceae (0.66) and Papilionoideae (0.61). These regressions allowed us to infer the presence or absence of regional pollen and predict the quantity of pollen in the absence of the vegetation producing it. In this regard, the studied pollen rain includes the regional contribution (except Papilionoideae), even though M. spinosum has a low contribution. This overrepresentation is attributed to long-distance transport associated to the westerly winds. These quantitative relationships will allow us to know past vegetational changes of these taxa from fossil pollen spectra in the studied area

In the new economy, R&D potential of the companies (reflected by intangible assets) tends to make the difference between competing enterprises. Although investing in tangible assets is still viewed as important, the researchers have discovered that investors on capital market tend to buy stocks of the high-tech companies promoting a policy of increasing R&D expenditures. The results of the present study on the Romanian economy reveals the correlation between the variation of intangible assets of the chemicals and pharmaceuticals companies (high-tech) and their performance – reflected by EPS and PER coefficients.

Se examinaron el papel predictivo del Sentido de la Vida y las diferencias en función del género en el Bienestar Psicológico en un grupo de 226 estudiantes universitarios españoles (87 hombres, 38.5%; 139 mujeres, 61.5%), con edades entre los 17 y los 25 años, M = 21.08, DT = 2.18. Se usaron adaptaciones españolas del Purpose-In-Life Test de Crumbaugh y Maholic y de las Escalas de Bienestar Psicológico de Ryff. Las hipótesis a contrastar fueron que de manera significativa el Sentido de la Vida predeciría el Bienestar Psicológico y que las mujeres alcanzarían puntuaciones más altas en algunas dimensiones del mismo. Los análisis estadísticos incluyeron regresiones lineales simples y la prueba t para muestras independientes. Los resultados mostraron que: (1) El Sentido de la Vida predijo significativamente el Bienestar Psicológico, especialmente el Bienestar Psicológico global, la Autoaceptación, el Propósito en la Vida y el Dominio del Entorno, y (2) las mujeres alcanzaron puntuaciones significativamente superiores en Bienestar Psicológico global, Dominio del Entorno, Crecimiento Personal y Propósito en la Vida. Estos resultados fueron discutidos a la luz de la investigación precedente.