Linear Regression Method Research Articles

Assessing the correlation between Gamma passing rate and clinical dosimetric variations in breast cancer IMRT plans with multi-leaf collimator errors: perspectives from the ArcCHECK QA system.

This study aimed to comprehensively investigate the influence of multi-leaf collimator (MLC) position errors on both the clinical absolute dose distribution and Gamma passing rate (%GP) in intensity-modulated radiation therapy (IMRT) plans for breast cancer. Additionally, the correlation between %GP and the clinical absolute dose relative difference (%DE) caused by MLC position errors was analysed. Ten IMRT plans for breast cancer were randomly selected. Systematic and random MLC position errors were introduced into DICOM files representing the investigated treatment plans by modifying the plan files and adjusting the MLC positions. Systematic errors were categorized as MLC opening errors, closing errors, and shift errors. The %DE in the tumour planning target volume (PTV) and organs at risk (OARs) caused by MLC errors were statistically analyzed using dose-volume histogram (DVH) analysis. The ArcCHECK quality assurance (QA) system was used to detect the %GP differences between baseline plans and plans with MLC errors. The correlation between %GP and %DE was obtained using linear regression methods. The results of this study indicate that MLC opening and closing errors have a significant impact on %DE and %GP in IMRT plans for breast cancer. Opening and closing errors can be detected at a gamma level of 3%/2mm, if error values are greater than or equal to 0.5mm, and %GP can predict DVH dosimetric changes caused by MLC opening and closing errors. It is concluded that DVH-based verification of IMRT plans can serve as an adjunct method to Gamma analysis to improve QA accuracy for breast cancer cases. Additionally, it is concluded that greater attention should be given to MLC leaf opening and closing errors in clinical practice.

Market Predictability Before the Closing Bell Rings

This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate decision days and the subsequent three days, the US dollar index, month effects, weekday effects, and market volatilities. Market-adaptive trading strategies are developed and backtested on the basis of the study’s insights. Unlike the commonly employed multiple linear regression methods with Gaussian errors, this research utilizes a Bayesian linear regression model with Student-t error terms to more accurately capture the heavy tails characteristic of financial returns. A comparative analysis of these two approaches is conducted and the limitations inherent in the traditionally used method are discussed. Our main findings are based on data from 2007 to 2018. We observed that well-studied factors such as overnight effects and intraday momentum have diminished over time. Some other new factors were significant, such as lunchtime returns during boring days and the tug-of-war effect over the days after a federal fund rate change decision. Ultimately, we incorporate findings derived from data spanning 2022 to 2024 to provide a contemporary perspective on the examined components, followed by a discussion of the study’s limitations.

The Influence of Tariff and Non-Tariff Implementation on International Trade in the Textile Industry on Madura Island, East Java Province

This research explores the impact of tariff and non-tariff barriers on international trade within the textile industry in Madura Island, East Java Province. The study assumes that trade policies, both in the form of tariffs and non-tariff barriers, have significant effects on the competitiveness of the local textile industry in the global market. Data were collected through a quantitative survey of 100 textile industry players involved in international trade. A multiple linear regression method was employed to analyze the impact of independent variables (tariff and non-tariff barriers) on the dependent variables (exports and imports). The analysis results indicate that tariffs significantly reduce textile export volumes, while non-tariff barriers have a more significant impact on import volumes. These findings provide valuable insights for policymakers regarding the importance of balancing tariff and non-tariff policies to enhance the competitiveness of the textile industry in the international market.

A Spatial Mapping for Profitability Consideration of Rental Houses in South Jakarta

Abstract: The population of South Jakarta reached 2.235.606 people as of 2023 (BPS, 2024), with a percentage of occupying owned residential buildings of 51,36% (BPS, 2022). Estimates indicate that only approximately 1,148,207 people currently reside in private assets. Rental prices need to be considered based on the strategic location to minimize the risk of rental house business owners suffering losses. The imbalance in the supply and demand of the residential market will cause the availability of land in productive areas to be increasingly limited. The existence of an imbalance in market supply and demand can be an opportunity for prospective property entrepreneurs in the field of rental houses. The analysis was carried out by calculating income predictions using multiple linear regression methods and spatial analysis between rental houses and the accessibility of public facilities in South Jakarta City. The results obtained indicate that the accessibility of public facilities in Setia Budi Sub-district provides an opportunity for high income. Keywords: Boarding House, Income Rediction, Multiple Linear Regression, Spatial Analysis

The Electroencephalogram (EEG) Study for Estimating Endurance Sports Performance Base on Eigenvalues Extraction Method

Objectives. Brain–behavior connections are a new means to evaluate sports performance. This electroencephalogram (EEG) study aims to estimate endurance exercise performance by investigating eigenvalue trends and comparing their sensitivity and linearity. Methods. Twenty-three cross-country skiers completed endurance cycling tasks. Twenty-four-channel full-brain EEG signals were recorded in the motor phase and recovery phase continuously. Eighteen EEG eigenvalues calculation methods were collected, commonly used in previous research. Time-frequency, band power, and nonlinear analyses were used to calculate the EEG eigenvalues. Their regression coefficients and correlation coefficients were calculated and compared, with the linear regression method. Results. The time-frequency eigenvalues shift slightly throughout the test. The power eigenvalues changed significantly before and after motor and recovery, but the linearity was not satisfactory. The sensitivity and linearity of the nonlinear eigenvalues were stronger than the other eigenvalues. Of all eigenvalues, Shannon entropy showed completely non-overlapping distribution intervals in the regression coefficients of the two phases, which were −0.1474 ± 0.0806 s−1 in the motor phase and 0.2560 ± 0.1365 s−1 in the recovery phase. Shannon entropy amplitude decreased more in the F region of the brain than in the other regions. Additionally, the higher the level of sport, the slower the decline in Shannon entropy of the athlete. Conclusions. The Shannon entropy method provided more accurate estimations for endurance exercise performance compared to other eigenvalues.

Accurate Prediction of 327 Rice Variety Growth Period Based on Unmanned Aerial Vehicle Multispectral Remote Sensing

Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice.

Factors Impacting Students’ Learning Performance in Higher Education in Chengdu, China

Background and Aim: The effectiveness of education has always been a focus of social attention. Previous research has mostly focused on the level of education in primary and secondary schools, and some studies have shown that college students may have different motivations and learning concepts. Therefore, this study aims to take Xihua University in Chengdu, China as an example to systematically examine various factors that affect the academic performance of university students. Through quantitative analysis, this study investigated the impact of five independent variables (learning attitude, learning anxiety, learning motivation, teacher ability, and student participation) on the dependent variable (academic performance), especially in higher education. Moreover, it aims to identify significant differences between variables. Methodology: Research Design, Data, and Methods: This study used the Project Objective Consistency Index (IOC) to test the validity of the questionnaire, and Cronbach's Alpha was used as reliability in the pilot study (n=30). They used the multiple linear regression method to analyze the effective responses of 80 students at Xihua University and verify the significant relationship between variables. Afterward, a group of 30 students underwent a 14-week planned study. Then, the quantitative results after and before the scheduled study were compared in paired sample t-tests. Results: In multiple linear regression, studies have shown that learning attitude, learning anxiety, learning motivation, teacher ability, and student participation have a significant impact on student academic performance. Finally, the comparison results of paired sample t-tests indicate substantial differences in student academic performance between the post-strategic and pre-strategic plan stages. Conclusion: This research endeavors to foster students' learning performance in the context of Chengdu, China.

EVALUATION OF THE FUNCTIONAL STATE OF KIDNEYS IN PATIENTS WITH ACUTE MYOCARDIAL INFARCTION BASED ON THE DETERMINATION OF CREATININE AND CYSTATIN C LEVELS

Aim – to evaluate the functional state of kidneys, glomerular filtration rate based on the determination of creatinine and Cystatin C in STEMI patients undergoing urgent revascularization.Methods and materials. The study included 286 patients with acute myocardial infarction and ST-elevation myocardial infarction (STEMI). Among the examined persons, 202 (70.6%) were men and 84 (29.4%) were women, with an average age of (62.8±9.8) years.Research results. The GFR calculated by the CKD-EPI formula in the examined group of STEMI patients ranged from 15.6 to 87.4 and averaged 51.0±14.3 ml/min∙m2.7. The value of the calculated GFR according to the CKD-EPI Cystatin C formula ranged from 11.1 to 88.1 and averaged 45.7±13.7 ml/min/1.73 m2. The non-parametric Spearman rank correlation analysis showed a high direct correlation between the values of GFR calculated by different formulas (CKD-EPI and CKD-EPI Cystatin C) - R=0.81; p<0.0001. Using the linear regression method, a formula was derived for the conditional calculation of GFR value according to CKD-EPI Cystatin C = 4.15 + GFR value according to CKD-EPI 0.814. Regression coefficient R2 = 0.84 (Fisher's correlation coefficient - F = 728.34 at the proper level = 1.28), p < 0.000001.Conclusions. Determination of GFR based on Cystatin C demonstrates lower rates in both men and women. A decrease in estimated GFR was related to older age and female gender.

Manufacturing Sub-Sector Dynamics and Economic Growth in Nigeria (1981-2022)

This study examined the impact of manufacturing sub-sectors dynamics on economic growth in Nigeria covering the period 1981-2022. Data for the study were extracted from the Central Bank of Nigeria (CBN) statistical bulletin and World Development Index (WDI) 2022. The method of data analysis used is the linear regression method with the application of the Error Correction Model (ECM). The major findings of the study reveal that Cement production (CP) contributes positively but insignificantly to economic growth in Nigeria (β = 2.622724 and p-value = 0.4414 > 0.05), Beverages and Tobacco Production (BTP) contributes positively but insignificantly to economic growth in Nigeria (β = 2.040513 and p-value = 0.0018 > 0.05), Chemical and Pharmaceuticals Production (CHP) contributes negatively but insignificantly to economic growth in Nigeria (β = -17.39120 and p-value = 0.5188 > 0.05) and Pulp paper and paper products (PPP) contribute positively but insignificantly to economic growth in Nigeria (β = 56.85029 and p-value = 0.5062 > 0.05). It is therefore the recommendation of the study that the government should create an enabling environment and adjust its taxation rate on these companies which is currently at 24% to around 18% and the federal and state governments should create a system to protect forests from illegal loggers who are indiscriminately exploiting and felling the national trees.

An Investigation of Indoor Environment Quality on Occupants’ Thermal Responses, Health, and Productivity: A Study Based on Physiological Data in Occupied Office Space

This study explores the impact of Indoor Environment Quality (IEQ) on the health and productivity of office workers in an office building in Fujisawa, Kanagawa, Japan. Previous studies have shown that IEQ can affect the physiological responses of occupants, such as of skin temperature, heart rate, and metabolic rate, which are indicators of health and productivity. However, most studies took place in controlled laboratory environments, which may not accurately represent real-life experiences. The study collected subjective and objective data from actual occupied office space, including on perceptions of IEQ, health, and productivity, and measurements of IEQ parameters such as on the thermal environment, light environment, indoor air quality, and acoustics. The study used correlation and linear regression methods to examine the relationship between IEQ, physiological data, and subjective responses to health and productivity. The stable thermal environment and low physical intensity of office work may contribute to the weak correlation between physiological data, thermal responses, and health–productivity variables. The results of this study can provide insights into how IEQ affects the psychological responses, well-being, and performance of office workers in real-world settings.

THE INFLUENCE OF TAXATION KNOWLEDGE, MOTIVATION, PERCEPTION, AND SELF EFFICACY ON ACCOUNTING STUDENTS' INTEREST IN A CAREER AS TAX CONSULTANTS

The goal of this research is to investigate how understanding of tax laws, drive, interpretation, and confidence impact the desire of accounting students at Universitas Mercu Buana Yogyakarta to pursue a profession as a tax advisor. This study utilizes a quantitative methodology through conducting a survey with 61 accounting students at Mercu Buana University Yogyakarta who have finished taxation courses. The study utilized purposive sampling to gather data via an online questionnaire. The collected information was analyzed using the multiple linear regression method facilitated by SPSS software. Findings revealed that a deep understanding of taxation greatly impacts students' desire to pursue a career as a tax advisor. The more students comprehend taxation concepts, the more likely they are to consider a profession in this field. However, the results also show that motivation, perception, and self-efficacy do not have a significant influence on interest in a career as a tax consultant. This indicates that even though students have high motivation or self-efficacy, these factors do not directly increase their interest in becoming tax consultants if their tax knowledge is inadequate.

Indicator dynamics of the “Combating Cardiovascular Diseases” regional program aimed at reducing mortality from acute coronary syndrome

Aim. To analyze the dynamics of the application of medical and organizational technologies for achieving the target reduction of mortality from acute coronary syndrome (ACS) in the region. Materials and methods. In 2018, the research covered 1 regional vascular center (RVC), 8 primary vascular departments (PVD), and 41 medical organizations (MO). In 2022, the structure of medical organizations changed to include 3 RSCs, 9 PSOs, and 39 MOs. The indicators characterizing the provision of medical care to ACS patients in the Irkutsk region were calculated and analyzed. The presence, direction, and strength of correlation between the number of deaths from ACS and the number of medical and organizational activities was studied using linear correlation analysis by the Pearson method. Results. Over time, the share of hospitalization of patients with ACS in the early stages up to 12 hours increased from 42.1% to 46.5%. The mortality rate from ACS decreased by 20.4%, primarily due to a decrease in mortality in the first 24 hours (by 28.7%). The key medical and organizational technologies that have the greatest impact on mortality rates were identified. A strong relationship (r = -0.729) was established between the number of deaths from ACS and the number of patients transferred to the regional vascular center for emergency coronoplasty; two medium-level relationships (r = -0.343) were found between the number of deaths from ACS and the number of hospitalized patients with ACS in the first 12 hours; a correlation (r = -0.415) was determined between the number of deaths from ACS and the number of hospitalized patients who underwent thrombolytic therapy. The linear regression method showed that the availability of such procedures as emergency coronoplasty and thrombolytic therapy has prognostic significance for further reduction of the expected number of deaths from ACS in the region. Conclusion. The creation of the RVC and PVD infrastructure in the region allowed the major indicators of the “Combating Cardiovascular Diseases” regional program to be achieved.

Simultaneous Quantification of 66 Compounds in Two Tibetan Codonopsis Species Reveals Four Chemical Features by Database-Enabled UHPLC-Q-Orbitrap-MS/MS Analysis.

Codonopsis canescens Nannf. (CoC) and Codonopsis nervosa Nannf. (CoN) are two traditional Tibetan medicinal herbs (Zangdangshen), which have been widely used in the treatment of various diseases. In this study, their aerial and underground parts were systematically analyzed using database-enabled UHPLC-Q-Orbitrap-MS/MS technology. This technology introduced three adduct ions, [M - H]-, [M + H]+, and [M + NH4]+, to putatively identify a total of 66 compounds. During the putative identification, at least 16 isomers were successfully differentiated, such as isochlorogenic acid A vs. isochlorogenic acid B vs. isochlorogenic acid C. Thereafter, all these identified compounds were further quantified for their contents based on a linear regression method. Their contents were observed to vary from 0.00 to 39,127.03 µg/g. Through multiple comparisons of these quantification results, the study found the following four chemical features: (1) Four sesquiterpenes (especially atractylenolide III) enriched mainly in CoC and rarely in CoN; (2) four quinic acid derivatives were abundant in the aerial part of two species; (3) sixteen flavonoids (particularly diosmetin and chrysoeriol) showed higher content in CoC than in CoN; and (4) lobetyolin was ubiquitously distributed in four parts of both CoC and CoN. Based on these features and the relevant principles, four compounds (lobetyolin, atractylenolide III, diosmetin, and chrysoeriol) are recommended as the quality markers of two Tibetan Codonopsis species. All these findings can facilitate the sustainable development and quality control of the two traditional Tibetan medicinal herbs.

Force model of robot bone grinding based on finite element analysis

Bone grinding represents a formidable challenge during open epiphyseal surgery, as grinding force may result in potential damage to bone and surrounding tissues. To address this issue, this study establishes a correlation between grinding force and bone density, as well as feed rate and rotational speed. Given the limited availability of experimental data on bone grinding, a finite element analysis (FEA) model is developed. Subsequently, the impact of bone density, feed rate and rotational speed on grinding force is determined through linear regression method. The accuracy and sensitivity of the grinding force model are then assessed, revealing a maximum prediction deviation of 4.87%. Finally, based on the established model, safe operating parameters and boundary conditions for robot bone grinding are identified.

Predicting insurance charges using linear regression models

Abstract. Linear regression method can be performed to predict the outcome from one or many input values. Its versatility allows it to be applied on many datasets that contain correlated values. However, researches on the application of linear regression on medical insurance costs, a highly important part of peoples life, are few. This paper studies an insurance dataset from Kaggle by applying linear regression on it. The author validates the dataset at first and explores the correlation between each individual factor and their corresponding charges to better show how insurance costs differ from person to person with different background. Many figures are also included to help visualize the correlation between factors. In the end, the author creates a multilinear regression model to predict the insurance charges. The R-Squared score of the model and a result table including regression coeffects are also provided to show the accuracy and details of the model.

Identification of candidate genes and genomic prediction for early heifer pregnancy in Nelore beef cattle

This study aimed to assess the influence of differential weighting in genomic regions harboring candidate causal loci on the prediction accuracy and inflation for early heifer pregnancy (heifers that calved up to 30 months of age) in Nellore (Bos indicus) heifers using the single step genomic BLUP model (ssGBLUP). Phenotypic records of 102,294 Nellore heifers born between 2010 and 2017 were used in this study. The pedigree dataset harbored information from 176,107 animals born between 1998 and 2017, including 5,145 sires and 35,705 dams. Seven different models for genomic prediction were defined by combining the SNP weights obtained in the iterations (1st and 2nd) of the weighted single step GWAS (ssw1GBLUP and ssw2GBLUP) or candidate QTLs reported in the literature. Hence, the lambda (λ) values estimated in the WssGWAS were used to weight the SNPs adjacent to the candidate regions or QTL previously reported in the literature. To estimate the genetic parameters and perform the WssGWAS and WssGBLUP for early heifer pregnancy, a single-trait Bayesian analysis considering a threshold animal model was used. Accuracy, bias, and inflation parameters were evaluated in the validation subset based on the linear regression (LR) method. Genomic windows of ten consecutive SNPs that explained more than 0.5% of the additive genetic variance were selected to explore and determine possible candidate genes. Among the identified genes, we can highlight the PGRMC2, TENM3, GRIP1, TMEM45A, and KLF3, given their roles in endocrine fertility, expression of contractile proteins, average daily gain, dry matter intake, and fat deposition. Several genomic regions associated with QTL related to early heifer pregnancy were identified. The identification of such regions and the respective candidate genes associated with sexual precocity and fertility would contribute to improve the genetic knowledge regarding early sexual precocity of Nellore cattle. The prediction accuracy increased roughly 25.6% using the ssGBLUP compared to BLUP models. The prediction accuracy with the WssGBLUP when incorporating weighted SNPs with the λ values obtained in the 1st (ssw1GBLUP) and 2nd iteration (ssw2GBLUP) of the WssGWAS was higher (∼18%) than that described for the ssGBLUP model. The inflation also increased the weighting of the most relevant SNPs obtained with the GWAS, most likely overestimating the GEBV. The models that weighted SNPs close to QTLs reported in the literature yielded to less biased and deflated predictions compared to ssw1GBLUP and ssw2GBLUP models. Genomic selection is a feasible alternative for genomic evaluation of early heifer pregnancy in Nellore beef cattle by increasing the prediction accuracy of young animals. In addition, the use of information obtained from the WssGWAS is an alternative to increase reliability and reduce genomic prediction bias. Therefore, the results obtained herein indicate that it is possible to improve the prediction accuracy and reduce the bias of genomic prediction by using genomic information and differentially weighted genomic regions harboring candidate QTLs previously reported in the literature for early heifer pregnancy.

Extending the horizon in cardiovascular risk prediction: A synergy of conventional predictors and spirometry data

Aim. To assess a correlation between conventional cardiovascular risk factors and unprovoked pulmonary volumes and flows.Material and methods. The subjects (n = 153, median age 43 [32; 51] years) were divided into two groups depending on age. Questionnaires, anthropometry, biochemical blood analysis, spirometry without bronchodilation test, calculation of the lower limit of normal (LLN – low limit of normal) were carried out. Using the Mann – Whitney U-test, the two groups were compared quantitatively. When analyzing four-field conjugacy tables, a comparison was performed using the Pearson criterion χ2. The direction and closeness of the correlation were estimated using Spearman’s rank correlation coefficient. A predictive model was developed using the linear regression method. The differences were considered statistically significant at p < 0.05.Results. In group I (median age 29 [26; 33] years), reliable connections were established between the volume of forced expiratory volume in one second and high-sensitivity C-reactive protein (hsCRP); modified Tiffno index (m-TI) and total cholesterol, lowdensity lipoproteins, glucose. When analyzing the LLN data, reliable associations between m-IT and glucose (p = 0.004), hsCRP (p = 0.011) were obtained. In persons over 40 years of age with a smoking index of more than 10 packs/year, a relationship was found between a decrease in LLN m-TI and LLN of forced expiratory flow between 25 and 75% of forced vital capacity and an increase in creatinine (p < 0.001) and lipoprotein (a) (p = 0.03), respectively. Conclusion. There are differences in the relationship between the function of external respiration and laboratory indicators of cardiovascular risk in people before and after 40 years of age, which makes it possible to individualize the prediction of chronic non-communicable diseases.

How do policy measures affect the ability of poverty-stricken households to cope with external shocks? From the perspective of differences in the human capital endowment of households.

Human capital plays a vital role in poverty-stricken households' efforts to cope with external shocks. Improving the human capital of poverty-stricken households to help them address external shocks can enhance the sustainability of poverty-stricken household livelihoods and support poverty alleviation. In this study, households with dependent children and older members were selected from questionnaires covering 6,463 poverty-stricken households from 33 poverty alleviation districts and counties in Chongqing municipality, China. Multiple linear regression model and stepwise regression methods were then used to compare the effects of the number of household members supported and the number of those working on the increase in income among poverty-stricken and nonpoverty-stricken households. Finally, the correlations between policy measures, dependent household members and household labor were examined. The results show the following: (1) Policy measures can alleviate the negative impact of a household's dependent population on household income. (2) Poverty-stricken households' access to policy support is closely related to the characteristics of their human capital. The household's dependent members and laborers are effectively protected by safety net and cargo net policies. (3) Policy measures can mitigate the impact of COVID-19 on the income of nonagricultural laborers while increasing the income of agricultural laborers. The findings show that the ability of poverty-stricken households to overcome poverty and deal with external shocks can be increased by revitalizing rural industries, linking the development of rural industries with the income of poverty-stricken households, and rationally using rural labor resources.

The Impact Of Audit Fee, Size Of Public Accounting Firm, Company Size, And Leverage On The Communication Of Key Audit Matters (Implementation Of The First Year Of KAMs Adoption In Independent Auditors' Reports In Indonesia)

Auditing standard 701 "Communicating Key Audit Matters (KAMs) in the Independent Auditor's Report" will be implemented in Indonesia in 2022. The communication of KAMs aims to increase the transparency of financial statements for users. This study examines the effect of audit fees, Size of public accounting firm, company size and leverage on the communication of Key Audit Matters (KAMs). The research population is companies that are listed on the Indonesia Stock Exchange and report audited financial statements for the fiscal year 2022. The sample was selected using purposive sampling method. The research data was processed using the Multiple Linear Regression method using IBM SPSS Statistics software ver. 25. This study proves that audit fees (sig 0.000 < 0.05) and Size of public accounting firm (sig 0.000 < 0.05) have a positive and significant effect on communicating KAMs. While company size (0.188 > 0.05) leverage (0.378 > 0.05) has no significant effect on communicating KAMs. Based on the results of the study, it is hoped that companies can consider an adequate audit fee budget and choose a Public accounting firm whose competence is in accordance with the conditions of the company to be able to increase the communication of KAMs. Thus, companies can increase investor and other stakeholder confidence through the communication of KAMs as an effort to demonstrate commitment to transparency and good corporate governanc

Uncovering the Secrets of the 2023 Box Office: Analysis of Factors Affecting the Success of the Top 200 Films Using the Linear Regression Method

The film industry plays a crucial role in the global economy and popular culture, realizing creative outcomes through complex processes involving production to marketing. This research analyzes the factors influencing the success of the top 200 films at the Box Office in 2023 using linear regression. Independent variables such as film ratings, number of theaters, and distributors are examined in relation to total gross revenue. The dataset processed for this research consists of 200 data points specifically for the year 2023, sourced from Kaggle and processed with Python in Google Colab. The analysis revealed that films with a rating of 7.0 and above averaged a total gross revenue of approximately $100 million, while those rated below 7.0 averaged around $50 million, indicating a negative correlation between film ratings and gross revenue. Additionally, films shown in an average of 2,000 theaters grossed approximately $150 million, demonstrating a positive correlation between the number of theaters and gross income. The analysis also indicated that films distributed by major companies tend to have higher grosses, with the top distributors achieving an average gross of $120 million compared to $70 million for smaller distributors. Nonetheless, this analysis highlights the complexity of other factors influencing a film's success. Further research is needed for a better understanding. The relevance of these findings for the film industry lies in supporting strategic decision-making and the development of more sophisticated analytical methodologies.