Hypothesis Testing Method Research Articles

Statistical considerations in long-term efficacy evaluation of anti-cancer therapies.

Anti-cancer therapy has been a significant focus of research. Developing and marketing various types and mechanisms of anti-cancer therapies benefit a variety of patients significantly. The long-term benefit to patients in evaluating the risk-benefit ratio of anti-cancer therapy has become a significant concern. This paper discusses the evaluation of long-term efficacy within the estimand framework and summarizes the various strategies for addressing potential intercurrent events. Non-proportional hazards of survival data may arise with novel anti-cancer therapies, leading to potential bias in conventional evaluation methods. This paper reviews statistical methods for addressing this issue, including novel endpoints, hypothesis testing, and efficacy estimation methods. We also discuss the influences of treatment switching. Although advanced methods have been developed to address the non-proportional hazard, they still have limitations that require continued collaborative efforts to resolve issues.

Korelasi Antara Human Development Terhadap Anggaran Pertahanan Negara Menurut GDP

Human development is one of the important factors in economic growth. Qualified human resources have high productivity so as to be able to increase the efficiency of economic activity and in the aggregate can affect economic growth. This study aims to describe the relationship between human development and the size of a country's defense budget allocation. This research approach uses descriptive quantitative based on hypothesis testing based on the method of testing hypotheses through chi-square distribution. Through this method, independent variables and dependent variables which consist of several categories are displayed in a table. then, a series of procedures and calculations must be carried out and test criteria must be formulated to determine the null hypothesis status. This study concludes that there is a correlation between human development and a country's defense budget. The correlation value between the two variables is 36.30%.

Analisis Kebijakan Dividen Sebagai Variabel Moderasi Pada Pengaruh Kinerja Keuangan Terhadap Nilai Perusahaan Multifinance

The purpose of this study is to determine and analyze the effect of liquidity, leverage, and profitability on firm value simultaneously and partially, with dividend policy as a moderating variable. For the 2018-2022 timeframe, this research was conducted on multi-finance companies listed on the Indonesia Stock Exchange. The population of this study consisted of 19 companies. The sample was selected using a purposive sampling strategy, and a sample of 13 companies was obtained with 5 years of observation, resulting in 65 research data. However, there are outlier data so that 64 research data are obtained. In this study, the hypothesis testing method was carried out using a panel data regression model with the Random Effect Model approach and for testing the moderating variable using the Moderated Regression Analysis (MRA) model. Data analysis of the two models using the EViews program version 10. The results showed that 1) liquidity had a positive and insignificant effect on company value, 2) Leverage had a negative and insignificant effect on company value, 3) Profitability had a positive and significant effect on company value, 4) Dividend policy was unable to moderate (weaken) the effect of liquidity on company values, 5) Dividend policy is not able to moderate (weaken) the effect of leverage on company value, and 6) Effect of profitability on company value.
 Keywords : Dividend Policy, Firm Value, Liquidity, Leverage, Profitability

The Effect of Motivation and Job Satisfaction on Performance of Land Documentation Employees at BP Batam

Motivation and job satisfaction are several factors that can affect the quality of HR activities in an organization. The purpose of this study was to use BP Batam Land documentation to determine the effect of work motivation and job satisfaction on employee performance. This study used baseline data from a sample of 100 land registrars at BP Batam. This study used data collection methods in the form of observation, questionnaires and documentation, as well as SPSS 25 as a data processing tool. The test method used is multiple linear regression using test-T and test as a hypothesis testing method. The results of this study indicate that work motivation partially has a positive and significant effect on the performance of land document workers at BP Batam. Partially job satisfaction also has a positive and significant effect on the performance of land documentation officers at BP Batam. Meanwhile, motivation and job satisfaction have a positive and significant effect on the performance of land documentation workers at BP Batam.

Inference on optimal treatment assignments

We consider inference on optimal treatment assignments. Our methods allow inference on the treatment assignment rule that would be optimal given knowledge of the population treatment effect in a general setting. The procedure uses multiple hypothesis testing methods to determine a subset of the population for which assignment to treatment can be determined to be optimal after conditioning on all available information, with a prespecified level of confidence. A Monte Carlo study confirms that the inference procedure has good small sample behavior. We apply the method to study Project STAR and the optimal assignment of a small class intervention based on school and teacher characteristics.

Robust confidence intervals for the process capability index Cpk with bootstrap improvement

The process capability index (PCI), C pk , one of the widely used tools for assessing the capability of a manufacturing process, expresses the deviation of the process mean from the midpoint of the specification limits. The C pk is known to perform well under the general assumption that the experimental data are normally distributed without contamination. Under this assumption, the sample mean and sample standard deviation are used for the estimation of the PCI. However, the sample mean and sample standard deviation are quite sensitive to data contamination and this will result in underperformance of C pk . Therefore, in this article, we propose alternatives to the conventional method by replacing the sample mean and sample standard deviation with robust location and scale estimators. We also propose a method for constructing a robust PCI C pk confidence interval which lends itself to robust statistical hypothesis testing. The robust hypothesis testing methods based on this confidence interval are shown to be quite efficient when the data are normally distributed yet also outperform the conventional method when data contamination exists.

Detection of copy number variations based on a local distance using next-generation sequencing data.

As one of the main types of structural variation in the human genome, copy number variation (CNV) plays an important role in the occurrence and development of human cancers. Next-generation sequencing (NGS) technology can provide base-level resolution, which provides favorable conditions for the accurate detection of CNVs. However, it is still a very challenging task to accurately detect CNVs from cancer samples with different purity and low sequencing coverage. Local distance-based CNV detection (LDCNV), an innovative computational approach to predict CNVs using NGS data, is proposed in this work. LDCNV calculates the average distance between each read depth (RD) and its k nearest neighbors (KNNs) to define the distance of KNNs of each RD, and the average distance between the KNNs for each RD to define their internal distance. Based on the above definitions, a local distance score is constructed using the ratio between the distance of KNNs and the internal distance of KNNs for each RD. The local distance scores are used to fit a normal distribution to evaluate the significance level of each RDS, and then use the hypothesis test method to predict the CNVs. The performance of the proposed method is verified with simulated and real data and compared with several popular methods. The experimental results show that the proposed method is superior to various other techniques. Therefore, the proposed method can be helpful for cancer diagnosis and targeted drug development.

Board Diversity Terhadap Underpricing IPO dan Firm Value pada Perusahaan terdaftar di Bursa Efek Indonesia (BEI)

This research aims to determine the effect of Board Diversity on IPO Underpricing and Firm Value in companies listed on the Indonesia Stock Exchange (BEI). This research method uses hypothesis testing method. Hypothesis testing in this research examines the influence of the variables Bsize, outside, AGE_SD, and woman on the underpricing and firm value variables. secondary data is data obtained indirectly. The data sources in this research were obtained from the BEI website (https://www.idx.co.id), investing.com website (https://www.investing.com). There are control variables, namely CAPEX, Age, leverage, ROA, size and new shares. The data used in this research is panel data, namely a combination of cross sectional and time series data on companies that conducted an IPO in 2017 on the IDX with observations for 5 years (2017 - 2021). In conclusion, that Board Size has no effect on IPO Underpricing and Board Size has a significant effect on Firm Value.
 Keywords : Board Diversity, Firm Value, IPO, Underpricing

Sample size calculation in clinical trials with two co-primary endpoints including overdispersed count and continuous outcomes.

Count outcomes are collected in clinical trials for new drug development in several therapeutic areas and the event rate is commonly used as a single primary endpoint. Count outcomes that are greater than the mean value are termed overdispersion; thus, count outcomes are assumed to have a negative binomial distribution. However, in clinical trials for treating asthma and chronic obstructive pulmonary disease (COPD), a regulatory agency has suggested that a continuous endpoint related to lung function must be evaluated as a primary endpoint in addition to the event rate. The two co-primary endpoints that need to be evaluated include overdispersed count and continuous outcomes. Some researchers have proposed sample size calculation methods in the context of co-primary endpoints for various outcome types. However, methodologies for sample size calculation in trials with two co-primary endpoints, including overdispersed count and continuous outcomes, required when planning clinical trials for treating asthma and COPD, remain to be proposed. In this study, we aimed to develop a hypothesis-testing method and a corresponding sample size calculation method with two co-primary endpoints including overdispersed count and continuous outcomes. In a simulation, we demonstrated that the proposed sample size calculation method has adequate power accuracy. In addition, we illustrated an application of the proposed sample size calculation method to a placebo-controlled Phase 3 trial for patients with COPD.

Defining The Pattern of the Thai Osteoarthritis Movement Data Model

This study proposed an algorithm to identify the patterns of Thai osteoarthritis, a condition that has seen an increase in prevalence in recent years, significantly affecting patients' quality of life and mobility. We aimed to define the data patterns for analysis. For data collection, we employed a Razor-IMU sensor and a WIFI transmitter, allowing testers to independently perform full-range knee motions. Data collection was categorized into two groups: healthy and unhealthy. Our goal was to categorize the data and establish criteria for pattern classification. Once the patterns and criteria were established, our objective was to validate movement models for both normal individuals and osteoarthritis patients. To achieve this, we conducted statistical hypothesis testing to verify the accuracy of the data. This testing comprised three main steps: first, evaluating the accuracy of data collection and data cleaning. Second, assessing the precision of converting data from linear to angular format, including degree coordinates selection. The last, Evaluating the accuracy of data sorting and grouping using Louvain clustering. The researcher thoroughly scrutinized each step to confirm the results. Each step demonstrated an accuracy test As Thailand transitions into an aging society, the prevalence of osteoarthritis is increasing due to the natural deterioration of the body. This deterioration can be decelerated by avoiding risky behaviors. Osteoarthritis significantly impacts patients' physical and mental well-being, making it a critical health concern. The objective of this research is to develop movement prototypes for both normal individuals and osteoarthritis patients by leveraging computer knowledge. This includes data collection with motion sensor devices, enhancing data quality using data mining techniques such as data cleaning and data transformation into suitable formats, and grouping of data. Additionally, the study seeks to validate the accuracy of the algorithm using statistical hypothesis testing methods and graph pattern detection. Based on the experimental results, an accuracy rate of 97% was achieved, demonstrating a high level of reliability. This prototype can be applied in treatment analysis, monitoring treatment outcomes, and even injury prevention. Furthermore, the dataset can serve as a model for the Thai population and can be expanded to accommodate larger datasets.

Factors Influencing Student Financial Literacy (Case Study of Students at the Faculty of Economics and Government Science, Indo Global Mandiri University)

This study aim to analyze the factors that affect student financial literacy with variables of class rank, GPA and pocket money. The object of this research is students of the faculty of Economics and Government Sciences, Indo Global Mandiri University. Data collection techniques were carried out by distributing questionnaires to 263 respondents individually. The hypothesis testing method is done by the chi-square test. The results of this study indicate that the class rank and GPA factors influence students’ financial literacy, while the pocket money factor do not affect students’ financial literacy.

Impact of Financial Distress and Accounting Conservatism

Abstract
 This study aims to investigate the effect of financial distress, sustainability reports, and sustainability reports on accounting conservatism. By using quantitative research methods. The data used in this study are secondary data in the form of financial reports and annual reports. The population in this study is 67 companies listed on the Indonesia Stock Exchange for the period 2020 to 2022. The sample for this study was selected using a purposive sampling technique in order to obtain 47 manufacturing companies that met the criteria. This research was conducted at manufacturing companies in the goods and consumption industry sector. In this study, the hypothesis testing method was used using Structural Equation Modeling (SEM) with the help of SmartPLS version 3.0 software.

Determinants of Company Value with Asset Revaluation as an Intervening

The study aims to determine the effect of Investment Opportunity Set, Liquidity, and Company Size both directly and indirectly on Company Value in companies that revalued assets listed on the Indonesia Stock Exchange in the 2016-2019 period. The research method used is a hypothesis testing method with a quantitative approach. The statistical test used is multiple linear regression with the use of partialsignificance test for hypothesis testing. The number of samples studied was 136 using panel data. The results showed that the direct effect of investment opportunity set and liquiditypartially had no effect on firm value, but firm size partially had an effect on firm value. While the indirect effect of investment opportunity set, and company size partially affect the value of the company. Liquidity partially has no effect on firm value through revaluation of fixed assets as an intervening variable.

FACTORS THAT AFFECT FIRM VALUE IN MANUFACTURING FIRMS

This research aims to obtain empirical evidence from the effect of profitability, leverage, liquidity, firm size, and activity ratio on firm value. Totals of 198 observational data originated from 66 manufacturing companies listed on Indonesia Stock Exchange (IDX) throughout 2018-2020. The sampling technique used in this research is purposive sampling. The hypothesis testing method in this research uses the multiple linear regression model. The multiple linear regression model estimation that suits this research is Fixed Effect Model (FEM). The statistical tool used for data processing in this research is EViews 12. This research uses Tobin's Q as a parameter to measure firm value. Based on the outcome of the processed data, profitability, leverage, and firm size all have significant effects on firm value, while liquidity and activity ratio both have insignificant effect on firm value.

FACTORS THAT AFFECT FIRM VALUE IN MANUFACTURING FIRMS

This research aims to obtain empirical evidence from the effect of profitability, leverage, liquidity, firm size, and activity ratio on firm value. Totals of 198 observational data originated from 66 manufacturing companies listed on Indonesia Stock Exchange (IDX) throughout 2018-2020. The sampling technique used in this research is purposive sampling. The hypothesis testing method in this research uses the multiple linear regression model. The multiple linear regression model estimation that suits this research is Fixed Effect Model (FEM). The statistical tool used for data processing in this research is EViews 12. This research uses Tobin's Q as a parameter to measure firm value. Based on the outcome of the processed data, profitability, leverage, and firm size all have significant effects on firm value, while liquidity and activity ratio both have insignificant effect on firm value.

The Effect of Earnings Per Share, Current Ratio, and Debt to Equity Ratio on Stock Prices Before and During the Covid-19 Pandemic with Price Earning Ratio as a Moderating Variable

This study aims to determine the effect of earnings per share (EPS), current ratio (CR), and debt to equity ratio (DER) on stock prices with price earning ratio (PER) as a moderation variable. The data used is secondary data with a quantitative approach, where the population of health sector companies listed on the IDX is 25 companies. Using purposive sampling techniques, there were 7 companies that met the criteria, so there were 112 samples in this study. The hypothesis testing method used is multiple regression analysis and absolute difference test with the help of IBM SPSS 26. The results of this study show that EPS and PER have a positive effect on stock prices before and during the COVID-19 pandemic. CR had no effect on share prices before and during the COVID-19 pandemic. DER negatively affected stock prices before the COVID-19 pandemic, while during the COVID-19 pandemic it had no effect. PER moderated the effect of EPS on stock prices before and during the COVID-19 pandemic. PER did not moderate the effect of CR on stock prices before the COVID-19 pandemic, while for conditions during the pandemic PER moderated the influence of CR on stock prices. PER did not moderate the influence of DER before and during the COVID-19 pandemic. Overall, these results show that during the COVID-19 pandemic there were changes in investor observations of stock prices, where investors prefer factors/variables that are directly related to stock value and profit potential over other variables that are not directly related.

Analysis of chlorophyll-a, sea surface temperature, and sea surface salinity in the Bay of Bengal

The waters of the Bay of Bengal (BoB) are influenced by the northeast monsoon (November-February) and the southwest monsoon (June-September). This study aims to determine the relationship between sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chl-a) during January 2021 in the BoB. The method used in this study is the method of correlation analysis, hypothesis testing, and analysis of variance using monthly average data for January 2021. The data comes from the Copernicus Marine Environment Monitoring Service (CMEMS) portal. Our analysis showed that high Chl-a was inversely related to low SST and SSS conditions and vice versa. This finding confirms the results of other previous studies. Medium and negative correlations occurred for the Chl-a - SST and Chl-a - SSS pairs. Meanwhile, the SST- SSS pair shows a strong and positive correlation. The hypothesis test shows that the conclusions drawn from these correlation relationships are acceptable. Furthermore, from the analysis of variance obtained, it was found that Chl-a had a significant effect on variations in SST and SSS, while SST had a significant effect on variations in SSS.

Ghost introgression facilitates genomic divergence of a sympatric cryptic lineage in Cycas revoluta.

A cryptic lineage is a genetically diverged but morphologically unrecognized variant of a known species. Clarifying cryptic lineage evolution is essential for quantifying species diversity. In sympatric cryptic lineage divergence compared with allopatric divergence, the forces of divergent selection and mating patterns override geographical isolation. Introgression, by supplying preadapted or neutral standing genetic variations, can promote sympatric cryptic lineage divergence via selection. However, most studies concentrated on extant species introgression, ignoring the genetic legacy of introgression from extinct or unsampled lineages ("ghost introgression"). Cycads are an ideal plant for studying the influence of ghost introgression because of their common interspecific gene flow and past high extinction rate. Here, we utilized reference-based ddRADseq to clarify the role of ghost introgression in the evolution of a previously identified sympatric cryptic lineage in Cycas revoluta. After re-evaluating the evolutionary independency of cryptic lineages, the group-wise diverged single-nucleotide polymorphisms among sympatric and allopatric lineages were compared and functionally annotated. Next, we employed an approximate Bayesian computation method for hypothesis testing to clarify the cryptic lineage evolution and ghost introgression effect. SNPs with the genomic signatures of ghost introgression were further annotated. Our results reconfirmed the evolutionary independency of cryptic lineage among C. revoluta and demonstrated that ghost introgression to the noncryptic lineage facilitated their divergence. Gene function related to heat stress and disease resistance implied ecological adaptation of the main extant populations of C. revoluta.

Multivariate Spectral Analysis and Hypothesis Testing-Based Robust Attack Detection for Multiarea Frequency Control

Frequency control is one of the critical systems responsible for maintaining grid stability, making it a vulnerable and attractive choice for cyber-attacks. This article presents a multivariate singular spectrum analysis (MSSA) for extracting the system’s dynamics under normal operation and proposes a projection-based distance tracking and hypothesis testing method to detect multiarea load frequency control (MA-LFC) attacks. The proposed methodology is robust, adaptive, and computationally efficient, especially, when system and measurement noises are considered. The three main features of the proposed method are that: 1) it uses standard SCADA data and does not require attack data; 2) it can be integrated with the existing grid control system with minimal hardware; and 3) it is independent of system configuration and upgrades. The attack detection algorithm can successfully detect different types of false data injection attacks (FDIAs), including stealth attacks on multiple sensors. The algorithm is tested on an IEEE 39-bus New England test system, 300 bus test system, and 1888 bus RTE system. The attack detection algorithm was found to be reliable, fast, robust, and scalable under noisy measurements compared to the existing methods.

Pengaruh Pengungkapan Corporate Social Responsibility Terhadap Kinerja Keuangan Perusahaan Perkebunan Kelapa Sawit

This research aims to determine how the influence of Corporate Social Responsibility (CSR) Disclosure on Financial Performance represented by Return on Asset (ROA) and Return on Equity (ROE). This study uses secondary data on Palm Oil Sub-Sector companies for 5 years (2017-2021) obtained from www.idx.co.id and the company's official website in the form of Annual Reports and Sustainability Reports. Through purposive sampling method as a sampling technique, 15 (fifteen) oil palm plantation sub-sector companies were obtained. CSR disclosure indicators are guided by the GRI Index with 30 indicators. The hypothesis testing method used is panel data regression analysis using the eviews program version 9. The results of the study through the REM model test analysis show that CSR disclosure has no effect on ROA where the probability is 0.3080 (prob> 0.05), the correlation between variables is weak at 1.4%, and the FEM model test that CSR disclosure has no effect on ROE where the probability is 0.5953 (prob> 0.05), the correlation between variables is also quiet weak at 43%. This means that the disclosure of Corporate Social Responsibility implemented by palm oil sub-sector companies during the period of this study does not directly affect the financial performance of these companies.