scholarly journals The Association of Family-Related Adversity With Fighting in Adolescents: Does Hopelessness Mediate This Association?

2021 ◽  
Vol 66 ◽  
Author(s):  
Jaroslava Macková ◽  
Zuzana Dankulincova Veselska ◽  
Andrea Madarasova Geckova ◽  
Danielle Jansen ◽  
Jitse P. van Dijk ◽  
...  
Keyword(s):  
Substance Abuse ◽  
Linear Regression ◽  
Confidence Intervals ◽  
Regression Models ◽  
Mediation Effect ◽  
Linear Regression Models ◽  
Family Adversity ◽  
Physical Fighting ◽  
Death Of A Parent ◽  
Product Method

Objectives: To explore the association of family-related adversities with physical fighting, and whether this association is mediated by hopelessness.Methods: The sample consisted of 3712 Slovak adolescents (mean age: 13.9, 50.7% girls). Participants answered questions regarding experienced family-related adversities, involvement in physical fighting in the last 12 months and the Hopelessness Questionnaire. First, the association of family adversities in general with fighting and of each of family-related adversity separately was assessed using linear regression models and second, mediation was assessed using the a*b product method with bootstrapped 95% confidence intervalsResults: Adolescents who had experienced at least one family adversity reported more frequent fighting. Similarly, each of reported family adversities (death of a parent, substance abuse problems of a parent, conflicts/physical fights, divorce) was associated with more frequent fighting among adolescents. The mediation effect of hopelessness was found in each association of family-related adversity with fighting.Conclusion: These findings suggest that interventions to support adolescents who had experienced family adversities could among other things be directed at better coping with hopelessness.

scholarly journals Investigating Students’ Difficulties in Understanding Confidence Intervals in Linear Regression Models

2018 ◽  
Vol 7 (4.33) ◽  
pp. 60
Author(s):  
Nor Rashidah Paujah @ Ismail ◽  
Fadzilah Abdol Razak ◽  
Norhayati Baharun
Keyword(s):  
Linear Regression ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Standard Error ◽  
Regression Models ◽  
Degrees Of Freedom ◽  
Good Explanation ◽  
Correct Interpretation ◽  
Linear Regression Models ◽  
Regression Slope

How do tertiary students perform when finding confidence intervals of linear regression models? Do they have strong understanding on how to compute the interval and provide good explanation on the interval obtained? To answer these questions, 197 answer scripts were examined to investigate students’ ability to calculate the confidence interval of the regression slope and their ability to make comprehensive interpretation afterwards. It was found that only 48% of the students managed to compute the confidence interval correctly. The errors made by most of the students were caused by the failure to identify the correct degrees of freedom and the failure to evaluate the correct value of the standard error of the slope. Of those who were able to compute the correct values, the percentage that were able to give complete and correct interpretation dropped to only 7.1%. 68.5% of them provided incorrect interpretations which showed their inability to understand the concept of regression slope. It is hoped that this study will give some ideas to educators in providing better understanding on computing and interpreting the confidence interval among students.  

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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