Linear Regression Method Research Articles

Investigating social media addiction and impact of social media addiction, loneliness, depression, life satisfaction and problem-solving skills on academic self-efficacy and academic success among university students.

The negative effects of post-COVID-19 restrictions have been detected in students' mental well-being due to internet addiction, changing habits, despair and uncertainty. Students' academic success is expected to be affected by social media addiction, loneliness, depression, life satisfaction, problem solving skills and academic self-efficacy factors. This study aimed to determine the level of social media addiction and the effects of these factors on the academic success of university students and define their interactions with each other. Four hundred nineteen questionnaires were collected between October-December 2022 at Bingöl University, Türkiye. Descriptive statistics, independent t-test, One-Way ANOVA, correlation and multiple linear regression methods were used to analyze data with the help of the SPSS 22 software. Middle level grade (GPA) average (71,17 ± 9,69 out of 100), low level social support from friends and family members (34,6%), spending more than 4 h on social media (42,5%), middle level social media addiction, moderate depression level (51,31%-PHQ > 10), mild loneliness and slight dissatisfaction with life were found among students. Furthermore, high academic self-efficacy, moderate agreement with academic performance and good problem-solving skills were indicated in the survey results. Significant differences, such as higher life satisfaction among males and higher depression among females, were measured. Academic self-efficacy scale, problem solving skills and satisfaction with life had a negative correlation with social media addiction and depression, while a positive correlation with academic performance measures. Problem solving skills, satisfaction with life, fourth class vs. others and living alone vs. others were positive predictors of the academic self-efficacy. Meanwhile, loneliness was a negative predictor of the academic self-efficacy, while higher problem-solving skills and being female were positive factors leading to a higher GPA. The fact that the participants were only students from Bingöl University limits the ability to generalize the results. Policymakers could implement social and problem-solving skills training to develop better academic programs and cognitive-behavioral therapy for students' academic success.

Climate Change Impact on Rain-Fed Maize Yield Cultivated with Small-Scale Landowners in Wolaita Zone, Ethiopia

Ethiopia is a country that heavily relies on rainfall-aided cultivation which is carried out by small-scale landowners, leaving it very vulnerable to climate change and fluctuation. The primary goal of this research is to investigate how climate change affects maize yield in Wolaita zone of Ethiopia. The authors were employed a linear regression method to evaluate the relationship between climate parameters and maize yield. Sen's slope magnitude estimator and the Mann-Kendal trend test were used to assess the significance of climate change. The outcome demonstrated that the temperature extreme indices of warm days and the length of warm days were considerably higher by 37.5% and 3.7% of days per year, however, cold days and cold spells were significantly decreased. Over the 1981-2021 periods, there was a significant upward pattern in TXx and TNn at an average of 0.033°C and 0.034°C. There was a considerable decline of 2.3% in the simple daily precipitation intensity index and 33% decreased in extremely heavy precipitation, respectively. The correlation analysis's findings indicated that growing period precipitation and maize outputs were positively correlated, but negatively correlated with maximum and minimum temperatures. Extreme temperature and precipitation were more explained a maize yield than average climate patterns. 12.4%, 14.76%, 13.08%, and 7.95% of maize output variability was attributed by the growing season mean climate conditions, which include precipitation, mean, minimum, and maximum temperature. The variability of maize output was explained by combined impact of precipitation and temperature extremes were 67.7% and 45.0%, respectively. Therefore, livelihood diversification and relevant policy formulation are suggested to adapt inevitable climate change by implementing irrigation and resistant varieties to improve maize yield production.

DETERMINAN PENYERAPAN TENAGA KERJA DI KOTA MALANG SEBELUM DAN SAAT PANDEMI COVID-19

One of the labor problems in Malang City is that the available jobs cannot absorb the rapidly growing labor force, causing high unemployment. The purpose of this study is to determine the effect of minimum wage, GRDP, inflation, education and COVID-19 dummy variables on labor deployment in Malang City. Data analysis uses multiple linear regression methods with the Stata 14 program. The data used are time series from 1993-2022 in Malang City. The results showed that the minimum wage has a positive and significant effect on labor absorption. Education level has a significant and negative effect on labor absorption. GRDP and Inflation have no significant effect on labor absorption. For COVID-19 dummy on employment, it has a negative and insignificant effect during the COVID-19 pandemic

ANALISIS PENGARUH INDEKS PERSEPSI KORUPSI TERHADAP KETIMPANGAN PENDAPATAN DI INDONESIA

This study attempts to examine the effect of corruption on income inequality in Indonesia. The study uses the gini ratio as a proxy for income inequality, serving as the dependent variable. The corruption perception index is used as a proxy for corruption, while several control variables such as economic growth, taxes, and foreign direct investment act as independent variables. Using the multiple linear regression method with time-series data, the results of this study show that the corruption perception index has a positive and significant impact on income inequality. These results indicate a tendency for the "grease the wheel" effect of corruption on income inequality in Indonesia.

Analysis of COVID-19 Virus Spread in Jakarta Using Multiple Linear Regression

COVID-19, first identified in Wuhan, China in December 2019, quickly spread worldwide and was declared a pandemic by WHO in March 2020. Indonesia reported its first case on March 2, 2020, and the pandemic has had a significant impact on the country's economic, social, and health sectors. This study aims to predict the death rate due to COVID-19 in Jakarta using multiple linear regression method. The dataset collected from Andra Farm - Go Green website includes COVID-19 cases recorded in all sub-districts in Jakarta on November 1, 2023. Pre-processing was performed to improve the quality and accuracy of the model. The method used was multiple linear regression. The analysis results show that variables such as total travel and discarded trip have a significant influence in predicting the number of positive cases. The study found that lowering the correlation threshold for selecting independent variables reduced the mean squared error (MSE) and improved model performance, highlighting the importance of variable selection in developing accurate predictive models. These findings provide important insights for the government in making informed decisions regarding post-pandemic healthcare. This research underscores the value of robust data processing and variable selection techniques in enhancing predictive accuracy for public health planning.

Analisis Pengaruh Produksi dan Luas Lahan Kelapa Sawit terhadap PDRB Sektor Pertanian: Pendekatan Regresi Linier Berganda menggunakan Data Sekunder 2013-2022

The issue in this research is that the increase and decrease of agricultural GDP per capita in Southeast Sulawesi are significantly influenced by several factors, one of which is the production factor and the availability of land for oil palm cultivation. Therefore, an analysis is needed regarding the impact of GDP on the production and land area of oil palm in Southeast Sulawesi. This study aims to understand the impact of oil palm production and cultivated area on the Gross Regional Domestic Product (GRDP) of the agricultural sector in Southeast Sulawesi Province. The sample for this study uses secondary data derived from the Southeast Sulawesi Provincial Statistics Agency and the Indonesian Palm Oil Statistics Book from 2013 to 2022. This research is analyzed using multiple linear regression methods with IBM SPSS Statistics 25 software. The results of this study indicate that oil palm production in the Southeast Sulawesi region has a negative and significant effect on the agricultural GRDP of Southeast Sulawesi, while the expansion of oil palm plantation area has a positive and significant effect on the agricultural sector GRDP in Southeast Sulawesi Province.

Strategy To Increase Stock Prices (Study of Bank Sub Sectors Listed on the Indonesian Stock Exchange in 2022)

This research aims to determine the condition of capital structure, profitability and share prices in the banking sub-sector, as well as to test and analyze the influence of capital structure as measured by the debt to equity ratio (DER) and profitability as measured by return on assets (ROA) on share price (closing price). This type of research uses quantitative research. The sample was taken using a purposive sampling method, namely 41 samples from 47 banking sectors listed on the Indonesia Stock Exchange in 2022. The secondary data source used came from the IDX.co.id website. The analysis was carried out using the multiple linear regression method using IBM SPSS version 25 software. The research results show that simultaneously capital structure (DER) and profitability (ROA) have a significant effect on stock prices. However, partial test results show different effects, capital structure (DER) does not have a significant effect on stock prices, while profitability (ROA) has a significant effect on stock prices.

Should regression calibration or multiple imputation be used when calibrating different devices in a longitudinal study?

In longitudinal studies, the devices used to measure exposures can change from visit to visit. Calibration studies, wherein a subset of participants is measured using both devices at follow-up, may be used to assess between-device differences (i.e., errors). Then, statistical methods are needed to adjust for between-device differences and the missing measurement data that often appear in calibration studies. Regression calibration and multiple imputation are two possible methods. We compared both methods in linear regression with a simulation study, considering various real-world scenarios for a longitudinal study of pulse wave velocity. Regression calibration and multiple imputation were both essentially unbiased, but correctly estimating the standard errors posed challenges. Multiple imputation with predicted mean matching produced close agreement with the empirical standard error. Fully stochastic multiple imputation underestimated the standard error by up to 50%, and regression calibration with bootstrapped standard errors performed slightly better than fully stochastic multiple imputation. Regression calibration was slightly more efficient than either multiple imputation method. The results suggest use of multiple imputation with predictive mean matching over fully stochastic imputation or regression calibration in longitudinal studies where a new device at follow-up might be error-prone compared to the device used at baseline.

The integration of Albanian women in the Balkan and European labor market

Abstract The integration of Albania in the EU is one of the greatest aspirations that Albanian citizens have in their future. The effort to adapt the workforce of Albanian citizens to the European market has been positive. This can be seen in the number of Albanian women who have moved and continue to move in the EU countries. The emancipation of women has led to an increase in the level of education and an increase in the professional level. The integration of women as equal power in Albanian families makes it possible for the spirit of gender discrimination to decrease significantly. The presented study will be based on the inclusion of the Albanian woman in the European market as well as her emancipation in professional development. The review of information includes the analysis of relevant and available data and sources (reports, reports from previous surveys, research, etc.) regarding the needs of the labor market. In this paper we will analyze the relationship between gender, education level, age and salary level. Each of these factors in the economy has defined roles, based on the primary indicators and on the distinguishing indicators of everyone’s knowledge in this scheme, but with a common goal to serve the stability and growth of the Albanian economy. The registered unemployment rate for 2011 was 13.3% of which women represent 14.3% and men, 12.4%. The highest unemployment rate corresponds to the 15-29 age group and it is estimated that there is at least 50% employment in the informal economy. The purpose of this paper is to analyze the women’s labor market in Albania based on the statistical data of INSTAT with a focus also in the EU and the Western Balkan market by using the linear regression method.

Techniques for quantification of organochlorine pesticides from a validated method by using gas chromatography-electron capture detector

Organochlorine pesticides (OCPs) are persistent organic compounds found in aquatic environments worldwide. A well-validated and well-established analytical method is crucial for detecting OCPs in the environment. In this study, an analytical method for quantifying OCPs in water was developed and evaluated. Here, the range of linearity, reproducibility, uncertainty, specificity, method detection limits (MDL), and special emphasis on detection and quantitation limits were assessed. Recovery studies were performed to measure the accuracy and precision of the method. This method exhibited excellent linearity in the range of 2.5–20 μg/L for all compounds. As none of the targeted compounds was detected in the chromatograms of the blank sample with no baseline noise, the limits of detection (LOD) and limits of quantification (LOQ) were determined using the linear regression method, external calibration curve slope, and laboratory fortified blank-based detection. All compounds showed different LOD and LOQ values, depending on the approach used. In particular, endosulfan sulfate, methoxychlor, endrin ketone, H. epoxide, heptachlor, and 4,4ʹ-DDT exhibited high detection limits. The recovery percentage of the 15 compounds at 5 μg/L spiked concentration was between 50 and 150 %, which is consistent with the accuracy of the APHA method. Except for endosulfan sulfate, the relative standard deviations of all other compounds were below 20 %, indicating good precision. This method has also been applied to real water samples. This validation technique is reliable, sensitive, simple, rapid, easy to comprehend, and reproducible. The application of this method in the real water samples was also conducted. Only α-BHC and γ-Chlordane were detected in the water sample.

Long-term trends of ozone in the Yangtze River Delta, China: Spatiotemporal impacts of meteorological factors, local, and non-local emissions

The Yangtze River Delta (YRD) region has witnessed a consistent decrease in NO2, CO, and PM2.5 from 2016 to 2023. However, ozone has exhibited fluctuating patterns. Quantifying ozone contributions from emissions, both within and outside the YRD, is essential for understanding city-cluster-scale ozone pollution (CCSOP). To address these concerns, a comprehensive approach combining Kolmogorov-Zurbenko filtering, Empirical Orthogonal Function, Absolute Principal Component Score, and Multiple Linear Regression methods (KZ-EOF-APCs-MLR) was employed to quantify the impacts of meteorological factors, local and non-local emission contributions of ozone (LECO and NECO). Emission changes were identified as the predominant factor shaping annual fluctuations in ambient ozone. Notably, during the previous and middle stages of the COVID-19 pandemic (from 2017 to 2021), emissions reductions led to a marked decrease in YRD ozone levels (-7.01 µg/m3), with a pronounced rebound post-pandemic (2022 to 2023) (+8.04 µg/m3). Seasonally, the emission-induced ozone exhibited fluctuating upward trend during autumn and winter, suggesting a transition of ozone pollution towards colder seasons. Spatially, high LECO concentrated in the eastern YRD (EYRD) across spring, autumn, and winter, becoming prominent in the central YRD (CYRD) during summer. During CCSOP, the CYRD exhibited the highest LECO and exceedance frequency (20.82 µg/m3 and 45.27 %). LECO explained a large portion of ozone variability during CCSOP, particularly in the EYRD, while NECO showed less explanatory power but consistently high contributions (148.05 ± 15.52 µg/m3). These findings offer valuable insights for a deeper understanding of the evolving patterns of ozone pollution and the issue of CCSOP in the YRD.

Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing. A three-stage AI-based data generating strategy is suggested to handle missing datasets, using a small sample dataset obtained from a smart healthcare program community in a specific city: Step one involves generating the dataset's basic attributes using a tree-based generation strategy that takes the original data distribution into account. Step two involves using the Naive Bayes algorithm to create basic indicators of behavioural capability assessment for the samples. Step three builds on stage two and uses a multivariate linear regression method to create evaluation criteria and indicators of high-level behavioural capability. Six problems involving multiple classifications and two tasks using multiple labels are implemented using various neural network-based training strategies on the obtained data to assess the usefulness of the dataset for downstream tasks. To ensure that the data collected is genuine and useful, the experimental data must be analysed and expert knowledge must be included.

KARAKTER SPASIAL DAN TEMPORAL CURAH HUJAN BULANAN KABUPATEN JEMBER BERDASARKAN DATA CHIRPS

Jember Regency is one of the national food storage areas. One of the most important resources in cultivating food crops is water which comes from rainfall. The existence of rainfall data is very important in planning and implementing agricultural activities. Limited rainfall data is the main problem for agricultural planning. One solution to this problem is to utilize rainfall data from CHIRPS (Climate Hazards Group Infrared Precipitation Station). The use of CHIRPS data is still biased data, so bias correction is needed to measure the level of accuracy of CHIRPS data for estimating rainfall in an area. Bias correction is carried out using two methods, namely multiple linear regression and power regression. The correction results show that the multiple linear regression method is able to produce monthly rainfall that is close to observations with an RMSE value of 3.9 and a coefficient of determination of 0.99. Temporally, the peak of the rainy season in Jember Regency occurs in December while the dry season is in August. In the rainy season (December-January and February) Jember Regency's rainfall is around 300-500 mm/month and in the dry season (June-July-August) it is in the range of 50-200 mm/month. Spatially, the northern part of Jember, especially Sumberbaru, Bangsalsari and Tanggul subdistricts, is wetter than the Jember Regency area as a whole. The southern part of Jember, especially those close to the coast, has lower rainfall than other regions. In the dry season, rainfall in this region is less than 20 mm/month.

Effects of Technological Advancement on the Functioning of Small and Midsize Enterprises (SMEs) in Khyber Pakhtunkhwa, Pakistan

Technological transformation aims to investigate small and medium-sized businesses' capacity to implement novel strategies that will boost production while endangering worker efficiency. Managers of SMEs in Khyber Pakhtunkhwa (KP), the province of Pakistan, made up a population sample of 273 respondents, who were compelled to do new research. The main statistical methods that are mandated are methods for linear regression. It is hypothesized that the medium-sized firm in KP's north and south areas is affected by the technological shift. The coefficient's determination (R2 = 0.101) shows that technical change accounts for 10.1% of the success observed in SME performance. When the forced p-value (0.000) matches the significance level of 0.05, it is considered statistically significant. results stated that the performance of SMEs in KP was positively and significantly impacted by technological progress.

Pengendalian Persediaan Bahan Baku Bawang Merah Untuk Produksi Bawang Goreng Dengan Metode Economic Order Quantity (EOQ) Dan Just In Time (JIT)

Effective planning to manage the procurement of raw materials is crucial for maintaining productivity in production activities and controlling costs. Indonesian enterprise UD. XYZ is dedicated to the manufacturing of fried onions. However, between April 2022 and June 2023, there have been regular fluctuations in the raw material availability for the production process, which has resulted in subpar output levels and unmet demand. To solve this problem, this study will use time series forecasting techniques to estimate the demand for fried onions and the Economic Order Quantity method to control raw material supply. Furthermore, this study uses the Just In Time approach to determine the minimum inventory cost. Both systems are anticipated to lessen the effects of changes in raw material availability. Based on the research results, the linear regression method was used for forecasting, which predicted an average demand of 1354 Kg of fried onions with a MAPE value of 2,79%, MAD 27,59, and MSD 1317,81. Then, an EOQ value of 1992,85 kg was calculated using the Economic Order Quantity (EOQ) and Just In Time (JIT) methods. This resulted in a total minimum storage cost of Rp. 2.989.271.

STUDY OF DWELLING TIME AT TANJUNG PERAK HARBOR SURABAYA INDONESIA

The research aims to determine the factors that influence dwelling time at Indonesian ports so that the government can make policies for releasing imported goods at ports more quickly and efficiently. This study uses secondary data obtained from KPPBC TMP Tanjung Perak quantitative data in the form of monthly pre-clearance, customs clearance, post-clearance, and dwelling time processing times from January 2020 to December 2022. Statistical analysis using the linear regression method uses the application SPSS. Based on the results of the analysis of the factors that influence dwelling time, it is concluded that the pre-clearance, customs clearance, and post-clearance processes simultaneously have an influence on dwelling time at the port of Tanjung Perak. Meanwhile, partially, the pre-clearance and post-clearance processes have a positive and significant effect on dwelling time, while the customs clearance process does not affect dwelling time. Currently, the customs clearance process is faster due to new policies and improved facilities. This research only discusses the management of imported goods, so other research using export variables is needed to increase the accuracy and efficiency of government trade policies.

Determinants of Intention to Use of Hospital Information Systems among Healthcare Professionals

Health information systems (HISs) are instrumental in improving the efficiency and effectiveness of hospital operations, from managing patient data to enhancing decision-making processes. This study, which holds significant implications for the healthcare industry, aimed to identify the factors that influence users’ intentions to use HISs. The research involved interviews with healthcare professionals licensed to use the HIS of a public hospital. The survey, conducted in 2020, received 113 responses. Statistical methods of descriptive analysis, correlation, and multiple linear regression were used. Two models were examined. The first model investigated the relationship between the dependent variable of the intention to use an HIS and the independent variables of perceived time savings and perceived privacy protection. The second model explored the impact of age and education as control variables in the connection between the intention to use an HIS and time savings and privacy protection. A significant moderate positive correlation was found between the intention to use an HIS and perceived time savings, while a significant weak positive correlation was detected between the intention to use an HIS and education. Regression analysis in the first model revealed a significant connection between the intention to use an HIS and perceived time savings. However, the perceived privacy protection variable did not show a significant relationship with the dependent variable. The second model showed statistical significance in the relationship between the intention to use an HIS and education, but not with the second control variable, age. It can be concluded that education strengthens the positive impact of perceived time savings on the intention to use a health information system (HIS), a finding that has immediate and practical implications for healthcare professionals and researchers in the field of health information systems and healthcare management, underlining the importance of this research in advancing the understanding and adoption of HISs in healthcare settings.

Enhancing TikTok Account Performance with Data Pattern Identification

TikTok is a social media platform widely used to share information through short videos to achieve goals and interests in business, education, politics, government, and personal existence. Every activity on this platform is recorded and presented privately to each account owner. However, this data has not been utilized optimally to improve account performance. This research aims to offer a data analysis concept that integrates statistical and machine learning approaches to identify data patterns in each user's data collection, enabling the improvement of account performance. The approach utilizes Linear Regression, k-means, and Decision Tree methods. The results obtained show that the concept of identifying data patterns in TikTok account data has successfully developed a predictive model for video posts that can potentially increase total viewership, video plays, and audience engagement. This is achieved through optimizing video components such as captions, text, hashtags, sound genre, and video type. The outcome yielded a classification model that can predict capable component content to enhance account performance.

AN ANALYSIS OF SOCIOECONOMIC INEQUALITIES AMONG LOW-INCOME INDIVIDUALS WITH A SPECIFIC FOCUS ON THE COMPLIANCE INDEX OF SHARIAH GENERAL BANKS IN INDONESIA

The aim of this study is to investigate the correlation between the focus on the overall compliance index of sharia banks in Indonesia, particularly the profitability role index, sharia supervisory boards, and independent commissioners, and reducing social disparity among low-income groups. This study specifically examines the sharia commercial banks in Indonesia, and the samples were chosen using the purposive sampling method. The data collected comprises secondary data, specifically the annual reports of sharia commercial banks from 2017 to 2021. The data underwent the usual assumption test for analysis. Applying the multiple linear regression method for hypothesis testing. The SPSS software is employed for data processing. The results of this study suggest that the presence of an audit committee has a significant and positive effect on the sharia compliance index. The variables of liquidity, profitability, leverage, and sharia supervisory board have a negative and statistically insignificant effect on the sharia compliance index. The independent commissioner variable has a favourable effect on the sharia compliance index, although this effect is statistically insignificant. Therefore, it may be inferred that the profitability index, Sharia Supervisory Board, and Independent Commissioners, which serve as benchmarks for guaranteeing Sharia compliance in Indonesian Sharia banks, are not successful in alleviating the prevailing social inequality in Indonesia.

The Influence of Visual Learning Styles on Learning Outcomes in PAUD Tegar Tangguh Kampung Gunung, Simalungun Regency

<p>This study aims to determine the effect of visual learning style on visual learning style on early childhood learning outcomes at PAUD Tegar Tangguh hamlet II village Bandar Jawa, Bandar Sub-district, Simalungun Regency. this research is motivated by the results of observations conducted and found interesting facts that the majority of learning activities still use auditory learning styles. The results of this observation revealed that learning methods that focus on hearing, such as listening to stories or verbal explanations, are not the main preference for students in absorbing information. The type of research conducted by researchers in this study is quantitative research with multiple linear regression methods. This research was conducted at PAUD Tegar Tangguh hamlet II village Bandar Jawa, Bandar District, Simalungun Regency with a sample of 30 students out of 120 students. Data collection was done by giving a questionnaire with a Likert scale model. Data analysis used is using multiple regression analysis. The results of this study indicate that visual learning styles have an effect on learning outcomes. early childhood, this can be seen from the significance value which is worth (Sig 0.000 <0.05) and for the value of F count> F table (28.830 > 3.34). The coefficient of determination of the effect of visual learning style variables on early childhood learning outcomes is 59.7% while the rest is influenced by other variables</p>