scholarly journals MULTILEVEL SEBAGAI ANALISA PENDAPATAN USAHA MIKRO, KECIL DAN MENENGAH (UMKM) DI PROVINSI ACEH

2021 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Wanda Sri Noviana ◽  
Miftahuddin Miftahuddin ◽  
Eddy Gunawan
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Data Sets ◽  
Similar Data ◽  
Multilevel Regression ◽  
Individual Level ◽  
Multilevel Regression Analysis ◽  
Aceh Province ◽  
Negative Effect ◽  
Level 3

Studies the influence of the MSME sector and regional categorical factors is often overlooked, so in this case a simple regression can be improved through classifying similar data sets and being handled properly. Therefore, to increase understanding of the factors that affect the income of MSMEs, it must be considered by looking at the relationship between the categories of the MSME sector and different districts / cities (regions) especially in Aceh Province. One of the analysis models suitable for MSME data is multilevel regression analysis. The purpose of this multilevel regression analysis is to form a regression model on the amount of MSME income in Aceh Province where individual level 1 business owners with factors of type of business, number of workers and amount of capital, are nested in the level 2 MSME sector, nesting in level 3 districts / cities. The data used are UMKM data obtained from the Aceh Cooperative and UMKM Service. The data were processed and analysed using the R-Studio software. The results showed that the type of mining, agriculture and livestock businesses had a significant negative effect of 5% on the income of MSMEs in Aceh Province, while the types of transportation, capital and labor businesses had a significant positive effect of 5%. The measure of suitability of the 3-level regression model obtained deviance, AIC and BIC, respectively, is 22,571, 22,585 and 22,636.

The Economic Roots of External Efficacy: Assessing the Relationship between External Political Efficacy and Income Inequality

2015 ◽  
Vol 48 (4) ◽  
pp. 791-813 ◽  
Author(s):  
Mikel Norris
Keyword(s):  
Public Policy ◽  
Income Inequality ◽  
National Level ◽  
State Level ◽  
The United States ◽  
Political Efficacy ◽  
Multilevel Regression ◽  
Individual Level ◽  
Negative Effect ◽  
The 1960S

AbstractExternal political efficacy, the belief that government is responsive to the demands of its citizens, has been declining in the United States since the 1960s. However, scholars do not yet fully understand the reasons for its decline. Nor have they found suitable explanations for why it fluctuates within the electorate. Drawing on the growing literature on the effects of income inequality on public policy, I posit that increasing income inequality factors into the decline of external political efficacy. Using multilevel regression models accounting for individual and contextual factors, I find increasing state-level income inequality has a substantial negative effect on external political efficacy. It is greater than most state and national-level economic measures or individual-level variables on external political efficacy. These results have important implications both for research on income inequality and political participation and also for research on income inequality and distributional public policy.

scholarly journals Religion and everyday cosmopolitanism among religious and non-religious urban youth

2020 ◽  
pp. 001139212093294
Author(s):  
Ariadne Driezen ◽  
Gert Verschraegen ◽  
Noel Clycq
Keyword(s):  
Regression Analysis ◽  
Urban Youth ◽  
Intrinsic Religiosity ◽  
Multilevel Regression ◽  
Anova Analysis ◽  
Policy Makers ◽  
Multilevel Regression Analysis ◽  
Religious Identification ◽  
The Difference ◽  
One Way Anova

While there is ample research on everyday cosmopolitanism, the relation with religion is less understood. This study examines the difference in everyday cosmopolitanism between Muslim, Christian and non-religious urban youth. Further, it studies the influence of religiosity, religious identification and perceived discrimination on cosmopolitanism. A one-way ANOVA analysis was conducted on data from 1039 students in 17 secondary schools in the super-diverse city of Antwerp. Multilevel regression analysis was conducted on a sample of Muslim ( n = 496) and Christian ( n = 225) youth. The results indicate no difference between religious and non-religious youth regarding their everyday cosmopolitanism. Moreover, for Muslim youth, intrinsic religiosity is positively associated with cosmopolitan orientations, while religious identification and discrimination negatively effect cosmopolitanism. For Christian youth, religious factors do not explain their cosmopolitan orientations. Overall, the article suggests that scholars and policy makers should discuss the potential of religion to foster cosmopolitan orientations.

A Review of Regression Models in Machine Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 40-47
Author(s):  
Sunil Kumar ◽  
Vaibhav Bhatnagar
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Analysis Model ◽  
Data Set ◽  
Data Regression ◽  
Different Types

Machine learning is one of the active fields and technologies to realize artificial intelligence (AI). The complexity of machine learning algorithms creates problems to predict the best algorithm. There are many complex algorithms in machine learning (ML) to determine the appropriate method for finding regression trends, thereby establishing the correlation association in the middle of variables is very difficult, we are going to review different types of regressions used in Machine Learning. There are mainly six types of regression model Linear, Logistic, Polynomial, Ridge, Bayesian Linear and Lasso. This paper overview the above-mentioned regression model and will try to find the comparison and suitability for Machine Learning. A data analysis prerequisite to launch an association amongst the innumerable considerations in a data set, association is essential for forecast and exploration of data. Regression Analysis is such a procedure to establish association among the datasets. The effort on this paper predominantly emphases on the diverse regression analysis model, how they binning to custom in context of different data sets in machine learning. Selection the accurate model for exploration is the most challenging assignment and hence, these models considered thoroughly in this study. In machine learning by these models in the perfect way and thru accurate data set, data exploration and forecast can provide the maximum exact outcomes.

When Unnecessary Tasks Weigh Heavily on the Back: A Diary Study on Musculoskeletal Pain

2021 ◽  
pp. 216507992199483
Author(s):  
Yannik Faes ◽  
Achim Elfering
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Decision Making ◽  
Regression Analysis ◽  
Musculoskeletal Pain ◽  
Body Mass ◽  
Office Workers ◽  
Multilevel Regression ◽  
Multilevel Regression Analysis ◽  
Prolonged Sitting

Background: Auxiliary tasks such as administrative work often include tasks that are unnecessary in the view of workers but still have to be done. These tasks can threaten a worker’s self-esteem. The purpose of this study was to examine the effects of unnecessary and unreasonable tasks on musculoskeletal pain. Methods: Fifty-five office workers (29 male; mean age = 41.96, SD = 14.2 years) reported their unnecessary and unreasonable tasks at the beginning of the study and kept a diary of their daily musculoskeletal pain over 5 weeks, using a visual analogue scale. Other work-related risk factors (prolonged sitting), job resources (participation in decision-making), and individual risk factors (sex, smoking, exercise, body mass index, maladaptive back beliefs) were controlled for in multilevel regression analysis. Findings: Multilevel regression analysis with 742 reports showed unnecessary tasks ( B = 4.27, p = .006)—but not unreasonable tasks ( B = 3.05, p = .074)—to predict the daily intensity of musculoskeletal pain, beyond other significant risk factors, such as prolonged sitting ( B = 2.06, p = .039), body mass index ( B = 1.52, p < .001), and maladaptive back beliefs ( B = 3.78, p = .003). Participation in decision-making was not a significant protective factor ( B = −1.67, p = .176). Conclusions/Application to Practice: The higher frequency of unnecessary tasks—compared with unreasonable tasks—could place workers at risk for musculoskeletal pain. Work redesign that reduces unnecessary and unreasonable tasks can make a valuable contribution to worker health and safety among office workers.

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