Search

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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Sciences, Life & Applied Sciences is the property of Arab Journal of Sciences & Research Publishing (AJSRP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study determined the contribution of magic square activity in teaching beginning reading in a multi-grade classroom. This study utilized a one group pretest-posttest experimental research design. frequency, percentage, mean and standard deviation, paired t-test, simple linear regression and independent samples t-test were the statistical techniques employed in the study. The paired samples t-test conducted to determine if there were significant differences comparing the pre-test with post-test performance of the pupils using the Magic Square activity. The results indicate a significant improvement in the performance of the pupils, with an average score of 10.87 in the pretest and 18.80 in the posttest. This suggests that the use of the Magic Square activity has a positive impact on the pupils' performance in reading and decoding words. Moreover, the attitudes of the pupils towards the activity become more positive as well as the posttest performance also tends to improve. Furthermore, the t-tests for both pretest and posttest scores were not significant, indicating that there was no significant difference in the pretest and posttest scores between male and female pupils. The study concluded that the used of magic square activity has a positive impact and significantly improved the learner's performance in reading. [ABSTRACT FROM AUTHOR]

Purpose: This research has two main objectives: to investigate the relationship between instructional strategies, students’ learning styles, and learning performance at public universities, and to identify the preferred learning styles of male and female students in Pakistani universities. Design/Methodology/Approach: A survey methodology was utilized to achieve the study objectives. Two research objectives were put forward to conduct this research. Existing research instruments on learning styles and instructional strategies were adopted to collect data. A sample of N=545 students was engaged in this research from the two public universities through purposive sampling technique. All the collected data were entered into SPSS for analysis. Descriptive statistics provided the base to run the stepwise simple linear regression. Findings: The results showed a significant but weak relationship between learning style and instructional strategies. No relationship between learning style and the learning performance of students was identified. Similarly, we could not detect any relationship between instructional strategies and the learning performance of students. As for measuring students’ preferred learning styles, the reflector learning style was found to be the most preferred style by male and female students. Surprisingly, the pragmatist learning style was the least adopted style by both male and female students at higher education. These findings were discussed with existing available research. Implications/Originality/Value: The study urges a comprehensive approach in Balochistan's secondary education, targeting regional disparities, improving teacher qualifications, refining instructional methods, and enhancing resource availability. This aims to foster positive reforms for an inclusive and technologically enriched learning environment. [ABSTRACT FROM AUTHOR]

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.

This paper establishes a new estimator for simple linear measurement error models with correlated errors. Under a small-noise asymptotic regime and using perturbation theory, an unbiased estimator has been developed. The consistency and the asymptotic normality of the new estimator have been established and then validated by using synthetic data. Moreover, the confidence intervals of the slope have been revisited. Simulation results show that our estimator is certainly the most stable of existing methods and its performance exceeds that of other existing methods in terms of coverage and interval length. [ABSTRACT FROM AUTHOR]

The goal of this study is to comprehend the connection between petrographic features especially the grain size of dolomitic rocks and engineering properties. Three types of dolomite were selected for this study: fine, medium and coarse, all from the same formation with the same mineral content but varying grain sizes. Samples of dolomite from the Rus Formation, Damman Dome, eastern Saudi Arabia province, were studied. The dolomite samples were mineralogically similar but varied in crystal grain size from fine-grained, medium-grained, to coarse-grained types. The experimental tests included the point load strength index, uniaxial compressive strength, P wave, dry and saturation densities of samples. The results suggest that textural factors have a greater influence on the engineering properties of dolomite than mineralogical composition. It was also revealed that the crystal size (fine to coarse) is a textural element and that it has a significant impact on the mechanical and physical properties of the dolomite under investigation. In addition, multivariate linear regression was employed in four separate stages for each engineering parameter, using different combinations of petrographical properties. Density and point load strength, uniaxial compressive strength, tensile strength and Böhme abrasion rise with increasing crystal size. Finally, the optimum equations with special arrangements for estimating engineering properties of Rus Formation dolomite were proffered. The correlation between these values allowed more than 95% accuracy in generating equations for predicting mechanical performance from the mineralogical composition of Rus Formation dolomite. [ABSTRACT FROM AUTHOR]

Competing procedures, involving data smoothing, weighting, imputation, outlier removal, etc., may be available to prepare data for parametric model estimation. Often, however, little is known about the best choice of preparatory procedure for the planned estimation and the observed data. A machine learning‐based decision rule, an "oracle," can be constructed in such cases to decide the best procedure from a set C$$ \mathcal{C} $$ of available preparatory procedures. The oracle learns the decision regions associated with C$$ \mathcal{C} $$ based on training data synthesized solely from the given data using model parameters with high posterior probability. An estimator in combination with an oracle to guide data preparation is called an oracle estimator. Oracle estimator performance is studied in two estimation problems: slope estimation in simple linear regression (SLR) and changepoint estimation in continuous two‐linear‐segments regression (CTLSR). In both examples, the regression response is given to be increasing, and the oracle must decide whether to isotonically smooth the response data preparatory to fitting the regression model. A measure of performance called headroom is proposed to assess the oracle's potential for reducing estimation error. Experiments with SLR and CTLSR find for important ranges of problem configurations that the headroom is high, the oracle's empirical performance is near the headroom, and the oracle estimator offers clear benefit. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

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. [ABSTRACT FROM AUTHOR]

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.

The current study aimed at identifying the impact of guessing on the accuracy of estimating simple linear regression equation parameters and the ability to predict. To achieve the objectives of the study, an achievement test was built in its final form of (40) multiple-choice items in a measurement and evaluation course. After verifying the psychometric properties of the test, it was applied to a sample of (134) male and female general diploma students registered at AL-Hussein Bin Talal University in the second semester of the year 2020/2021. The test was divided into two sections: the first section was tested based on the traditional correction method, while the second section was tested based on the correction formula for the impact of guessing. The results of the study concluded that there are statistically significant differences among the predicted values in the scores of the examinees when using the traditional method and the correction formula for the impact of guessing in favor of the traditional method, and that there are statistically significant differences among the means of residual squares for estimating the scores of the study sample attributed to the correction method and in favor of the traditional method. The results of the study also found that the value of the explained variance (R2) increases when using the correction equation for the guessing effect, and that the values of the parameters of the simple linear regression equation increase when using the correction equation for the impact of guessing, and that the values of the standard error in estimating the parameters of the simple linear regression equation decrease when using the correction equation for the impact of guessing. [ABSTRACT FROM AUTHOR]

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.

This study aimed to investigate and analyse the impact of the transition to the use of renewable energies on the dimensions of sustainable development (economic, environmental, social) in Algeria during the period 2000-2019. The data were submitted to linear regression analysis through structural equation modelling using SPSS software v.25.0. The results show that the transition to renewable energy use has significantly impacted the economic dimension of sustainable development, as represented by Gross Domestic Product per capita (GDP/capita). In addition, the results demonstrate a significant correlation between the use of renewable energy and the environmental dimension of sustainable development represented by the Emissions of Carbon Dioxide (CO2). However, the study concluded that there was no significant correlation between renewable energy consumption and the social dimension of sustainable development represented by the Human Development Index (HDI). [ABSTRACT FROM AUTHOR]

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.

Copyright of Alustath is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Administration & Economics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of surface elevation table (SET) instruments to monitor elevation changes at low elevation coastal locations has steadily increased in recent years. A primary focus in the analysis of SET data is the estimation of the overall rate of elevation change, and numerous approaches have been used for this purpose. In this work, we compare and contrast several methods used for estimating the true rate of elevation change at SET station locations, including a novel approach proposed in this work that incorporates spatial dependence. We also discuss theoretical properties of one class of estimators, and undertake a comprehensive simulation study. Additionally, we present two case studies where we illustrate these differences using real SET data. All methods considered here tend to produce similar point estimates, but some confidence interval procedures can generate intervals with empirical coverage rates lower than specified. However, the best analysis approach is likely dependent upon selecting the method that best coincides with the true underlying process. Thus, we do not uniformly recommend one approach for all situations. Instead, we suggest carefully weighing potential advantages and disadvantages of each method before conducting analysis, while keeping in mind the ways in which modeling assumptions may impact this decision. [ABSTRACT FROM AUTHOR]

Plain ecosystem is highly vulnerable to environmental changes, and drought is the most famous ecosystem change driver that is difficult to identify after its occurrence. In this research study, the effect of drought on changes in vegetation changes were studied. For this, the NDVI index of MODIS images and the SPI index from 2001 to 2016 were used. The map of vegetation changes against five drought stress classes (very low classes, low, moderate, high and very high) was analysed. The results showed that across the plain vegetation changes have declined, and from east to west of Qazvin plain, the slope of vegetation changes and land susceptibility to drought have reduced. The very low drought class had the highest percentage of area in a one-month period, but at 3, 6, 9, 12, 24 and 48 months, the highest percent of the area belonged to moderate and high drought classes. The results of this study, can be used in the planning and optimal use of resources, and control changes in the future. [ABSTRACT FROM AUTHOR]

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.

Most of the software development organisations frequently use an appreciable amount of resources to estimate the effort in the beginning of the development process. In most of the cases, inaccurate estimates tend to wastage of these resources. Very few generalised models have been found in the literature. These models have been developed using the prototype dataset of the organisation. The project management team of an organisation tries to predict the effort needed for the development of software using various mathematical techniques. These techniques are mostly based on statistical methods (viz. simple linear regression (SLR), multi linear regression, support vector machine, cascade correlation neural network (CCNN) etc.) and some probability‐based models. They use historical data of similar projects. The work presented in this article envisages the use of Support Vector Regression (SVR) and constructive cost model (COCOMO), where SVR can be used for both linear and non‐linear models and COCOMO can be used as a regression model. The proposed hybrid model has been tested on the International Software Benchmarking Standards Group dataset. The data has been grouped according to the size of man power. It has been found that the proposed model yields better results than the SVR or SLR for each group of data in general. [ABSTRACT FROM AUTHOR]

This paper tries to introduce a time-series of temperature parameters as a potential method for studying the global warming. So, we investigated the spatial–temporal variations of warm-season temperature parameters (WSTP), including start time, end time, length of season, base value, peak time, peak value, amplitude, large integrated value, right drive, and left drive, using a database of 30 years' period in different climates of Iran. We used daily temperature data from 1989 to 2018 over Iran to extract the parameters by TIMESAT software. We studied the trend analysis of WSTP through the Mann–Kendall method. Then, we considered the Pearson correlation coefficient to calculate the correlation between WSTP and time. We assessed the trends of the slope using a simple linear regression method. Then, we compared the results of the WSTP trend analysis in climatic zones. Our results accused the hyper-arid climatic zone has the longest warm season (194.89 days a year). The warm season in this region starts earlier than other regions and increases with moderate speed (left drive, 0.19 °C day−1). Then, it reaches a peak value (31.3 °C) earlier than the different climatic zones. On the other hand, the humid regions' warm season starts with the shortest length and ends later than the other climatic zones (112.1 and 297.5 days a year for start and end times, respectively). We detected that the trend of the start time parameter has decreased by 98.02% of the study area during the last 30 years. The base value, length, and large integrated value parameters have an increasing trend of 66.47%, 80.11%, and 92.95% in Iran. The highest correlation coefficient with time was for start time and large integrated value parameters. Hence, the start time and large integrated value parameters have almost the most negative (< − 0.5) and positive (> 5) trend slope, among other parameters, respectively. In general, these results demonstrate that the studied region has faced global warming impacts over time by increasing the warm season and thermal energy, especially in arid and hyper-arid. We highlight the necessity of planning the land use under the high natural vulnerability of the studied local, especially in this new age of global warming. [ABSTRACT FROM AUTHOR]

This article presents a case study motivated by a plot of data that suggested an emerging trend that the authors were faced with explaining. When the measurement error of the data is accounted for, it turns out there was no real trend. This article shows how to use a Bayesian modeling approach to account for the measurement error. [ABSTRACT FROM AUTHOR]

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.

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Universiti Putra Malaysia.)

The development of precise vehicle emission models is crucial for estimating vehicular exhaust emissions. Though measuring emissions using an on-board emissions measurement system can be promising, it is essential to improve the precision of emission rates (ERs) prediction through effective statistical methods. A novel framework of simple linear regression (SLR), support vector regression (SVR), and piecewise linear regression (PLR) approaches was employed to develop a speed-based emission model. In total, 30 trips data from six professional drivers were collected to understand the variability of tailpipe emissions. The developed SLR, SVR, and PLR models demonstrated high accuracy, as indicated by mean absolute percentage error (MAPE), root-mean-square error (RMSE), and coefficient of determination (R 2) values. PLR outperformed SLR, and SVR in predicting CO, CO 2, HC and NO x ERs. These models can be useful tools for policymakers to understand emissions in heterogeneous traffic conditions and develop appropriate solutions to improve air quality. [ABSTRACT FROM AUTHOR]

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.