scholarly journals The performance factors of non-profit basketball clubs

2020 ◽  
Vol 1 (1) ◽  
pp. 17-39
Author(s):  
Igor Ivaskovic
Keyword(s):  
Regression Analysis ◽  
Organizational Performance ◽  
Causal Relationship ◽  
The Other ◽  
Direct Impact ◽  
Regression Analyses ◽  
Non Profit ◽  
Performance Factors ◽  
Sport Clubs ◽  
And Behavior

Using the data from 73 non-profit basketball clubs from four post-transitional Southeast European countries the article examines the potential causal relationship between 165 variables divided into four groups (organizational environment, strategies, human resource management (HRM), and behavior and feelings of organizational members) and two different aspects of organizational performance, namely competitive-financial and recreational-non-financial. The multiple regression analyses results disclose direct impact of the strategic focus on the organizational performance. The study provides explanation how the importance of different objectives from an aspect of club leadership affect the chances of enhancing the sport clubs performance from both perspectives. Higher performance is achieved by specialized clubs that pursue only one aspect of performance and strive for those goals that positively affect the same performance perspective. On the other hand, the HRM – performance analysis shows that many HRM factors correlate with organizational performance. However, regression analysis did not confirm any significant direct impact, which indicates that this causal relationship is indirect.

A Conceptual and Pragmatic Review of Regression Analysis for Predictive Analytics

2017 ◽  
pp. 1761-1776
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Regression Analysis ◽  
Organizational Performance ◽  
Predictive Analytics ◽  
Organizational Outcomes ◽  
Regression Analyses ◽  
Organizational Productivity ◽  
Intelligent Information ◽  
And Performance ◽  
Analytical Tools ◽  
Web Server Logs

Regression analysis and modeling are powerful predictive analytical tools for knowledge discovery through examining and capturing the complex hidden relationships and patterns among the quantitative variables. Regression analysis is widely used to: (a) collect massive amounts of organizational performance data such as Web server logs and sales transactions. Such data is referred to as “Big Data”; and (b) improve transformation of massive data into intelligent information (knowledge) by discovering trends and patterns in unknown hidden relationships. The intelligent information can then be used to make informed data-based predictions of future organizational outcomes such as organizational productivity and performance using predictive analytics such as regression analysis methods. The main purpose of this chapter is to present a conceptual and practical overview of simple- and multiple- linear regression analyses.

Regression and correlation analyses.

2021 ◽  
pp. 119-146
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Regression Analysis ◽  
Explanatory Variable ◽  
Continuous Variable ◽  
The Other ◽  
Strongly Correlated ◽  
Regression Analyses ◽  
Response Variable ◽  
Explanatory Variables ◽  
Correlation Analyses ◽  
Initial Assumption

Abstract This chapter focuses on regression and correlation analyses. Correlation and regression analyses are used to test whether, and to what degree, variation in one continuous variable is related to variation in another continuous variable. In correlation analysis, there are no control over either variable, they are just data collected, and indeed, even if two variables are strongly correlated, they may not be influencing one another but simply both being affected by a third which perhaps was not measured. The initial assumption of the analysis is that the values of both variables are drawn from a normal distribution. In regression analysis one of the variables are being controlled seeing whether changing its value affects the other. The variable being controlled is the explanatory variable (sometimes called the treatment) and the other is the response variable. As the explanatory variables are being controlled, they are probably going to be set at specified values or set increments and are therefore not normally distributed. There may be more than one explanatory variable. If all the explanatory variables are categorical then the regression is called an ANOVA.

scholarly journals How CSV and CSR Affect Organizational Performance: A Productive Behavior Perspective

2020 ◽  
Vol 17 (7) ◽  
pp. 2556 ◽  
Author(s):  
Kwang O. Park
Keyword(s):  
Job Performance ◽  
Organizational Performance ◽  
Causal Relationship ◽  
Organizational Citizenship ◽  
Work Engagement ◽  
Securities Market ◽  
Innovative Behavior ◽  
Performance Factors ◽  
Creating Shared Value ◽  
Corporate Social

Background: This study aims to shed light on the mutually beneficial causal relationship between creating shared value (CSV) and corporate social responsibility (CSR) activities and how they affect productive behavior through work engagement. Many preceding studies showed that work engagement and organizational citizenship behaviors (OCB) play a major role in the relationship between CSV and CSR activities and the organization’s internal performance. This study classified product behavior into OCB, innovative behavior, and job performance, based on the literature review. Methods: The subjects of this study were companies listed in KOSPI, which is Korea’s representative securities market. The companies listed on KOSPI are Korea’s leading companies as designated by the Korean government and financial authorities based on industry representation, market representation, and liquidity. Results: This study supported many preceding studies that analyzed the causal relationship between CSV and CSR activities, as well as OCB. In addition, this study has significant implications for businesses since it presents the possibility of studying the relationships between various organizational performance factors such as innovative behavior and job performance. Conclusions: It is expected that this study will help companies find more effective ways to strengthen their competitive advantage from a theoretical and practical perspective.

A Conceptual and Pragmatic Review of Regression Analysis for Predictive Analytics

2017 ◽  
pp. 277-292
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Regression Analysis ◽  
Organizational Performance ◽  
Predictive Analytics ◽  
Organizational Outcomes ◽  
Regression Analyses ◽  
Organizational Productivity ◽  
Intelligent Information ◽  
And Performance ◽  
Analytical Tools ◽  
Web Server Logs

Regression analysis and modeling are powerful predictive analytical tools for knowledge discovery through examining and capturing the complex hidden relationships and patterns among the quantitative variables. Regression analysis is widely used to: (a) collect massive amounts of organizational performance data such as Web server logs and sales transactions. Such data is referred to as “Big Data”; and (b) improve transformation of massive data into intelligent information (knowledge) by discovering trends and patterns in unknown hidden relationships. The intelligent information can then be used to make informed data-based predictions of future organizational outcomes such as organizational productivity and performance using predictive analytics such as regression analysis methods. The main purpose of this chapter is to present a conceptual and practical overview of simple- and multiple- linear regression analyses.

Regression and correlation analyses.

2021 ◽  
pp. 119-146
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Regression Analysis ◽  
Explanatory Variable ◽  
Continuous Variable ◽  
The Other ◽  
Strongly Correlated ◽  
Regression Analyses ◽  
Response Variable ◽  
Explanatory Variables ◽  
Correlation Analyses ◽  
Initial Assumption

Abstract This chapter focuses on regression and correlation analyses. Correlation and regression analyses are used to test whether, and to what degree, variation in one continuous variable is related to variation in another continuous variable. In correlation analysis, there are no control over either variable, they are just data collected, and indeed, even if two variables are strongly correlated, they may not be influencing one another but simply both being affected by a third which perhaps was not measured. The initial assumption of the analysis is that the values of both variables are drawn from a normal distribution. In regression analysis one of the variables are being controlled seeing whether changing its value affects the other. The variable being controlled is the explanatory variable (sometimes called the treatment) and the other is the response variable. As the explanatory variables are being controlled, they are probably going to be set at specified values or set increments and are therefore not normally distributed. There may be more than one explanatory variable. If all the explanatory variables are categorical then the regression is called an ANOVA.

Evaluation of the performance of sweet potato genotypes by joint regression analysis

1991 ◽  
Vol 117 (2) ◽  
pp. 171-176
Author(s):  
J. M. Ngeve
Keyword(s):  
Regression Analysis ◽  
Sweet Potato ◽  
Soil Type ◽  
The Other ◽  
Human Consumption ◽  
Regression Analyses ◽  
Specific Adaptation ◽  
Regression Slopes ◽  
Joint Regression Analysis ◽  
Linear Regressions

SUMMARYTwo experiments, each involving a set of six sweet potato clones, the first set developed in sites differing in altitude, and the second in sites differing in soil type, were done at three locations in 4 years in Cameroon. Data obtained were subjected to analyses of variance to determine the presence of genotype × environment (G × E) interactions, and to joint regression analyses to measure the performance of clones across environments. The first experiment produced higher yields and contained more stable clones than the second. In both experiments, mean yields were almost twice as high in 1984 (21·1 t/ha) as in each of the other years (c. 11·0 t/ha), and highest at Nyombe (18·0 t/ha). In Expt 1, the G × E interaction mainly concerned interaction with location, whereas in Expt 2 it concerned interaction with years.Clones 1611 (Expt 1) and 048 (Expt 2) yielded above average and gave linear regressions significantly above unity (b > 1·0) for most traits, indicating specific adaptation to high-yielding environments and hence below average stability. Clones 1112, 1639 and TIbl (Expt I) yielded above average and had regression slopes equal to unity (b = 1·0), indicating average stability and thus general adaptability. Clones TIb2 (Expt 1) and 1487 (Expt 2) produced below average yields (b < 1·0), indicating specific adaptation to low-yielding environments. Since sweet potato is grown mainly for human consumption in Cameroon, a preferred clone must have stable marketable yields. Only clones 1112, TIbl and 1639 could be considered desirable for release to growers.

Plasma Levels of Protein S, Protein C, and Factor X: Effects of Sex, Hormonal State and Age

1995 ◽  
Vol 74 (05) ◽  
pp. 1271-1275 ◽  
Author(s):  
C M A Henkens ◽  
V J J Bom ◽  
W van der Schaaf ◽  
P M Pelsma ◽  
C Th Smit Sibinga ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Postmenopausal Women ◽  
Protein C ◽  
Blood Donors ◽  
Premenopausal Women ◽  
Protein S ◽  
The Other ◽  
Factor X ◽  
Normal Ranges

SummaryWe measured total and free protein S (PS), protein C (PC) and factor X (FX) in 393 healthy blood donors to assess differences in relation to sex, hormonal state and age. All measured proteins were lower in women as compared to men, as were levels in premenopausal women as compared to postmenopausal women. Multiple regression analysis showed that both age and subgroup (men, pre- and postmenopausal women) were of significance for the levels of total and free PS and PC, the subgroup effect being caused by the differences between the premenopausal women and the other groups. This indicates a role of sex-hormones, most likely estrogens, in the regulation of levels of pro- and anticoagulant factors under physiologic conditions. These differences should be taken into account in daily clinical practice and may necessitate different normal ranges for men, pre- and postmenopausal women.

Pengaruh Kotbah, Musik Gereja Dan Fasilitas Gereja Terhadap Tingkat Kehadiran Jemaat

2016 ◽  
Vol 1 (1) ◽  
pp. 1-15
Author(s):  
Frederich Oscar Lontoh
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Church Music ◽  
Pearson Correlation ◽  
Descriptive Analysis ◽  
Target Population ◽  
The Other ◽  
Pearson Correlation Coefficient ◽  
Christian Church ◽  
The City

This research is titled " The influence of sermon, church music and church facilities on the level of attendance”. The purpose of research is to identify and analyze whether sermon, church music and church facilities have influence on the the level of attendance. The target population in this study is a Christian church members who live in the city of Surabaya.. Sample required is equal to 47 respondents. Through sampling stratified Random techniques.These influence was measured using Pearson correlation coefficient and multiple regression analysis, t-test and analysis of variance. Descriptive  analysis  were taken to analyze the level of attendance according to demographic groups.The hypothesis in this study are the sermon, church music and church facilities have positive and significant on the level of attendance. The results showed that collectively, there are positive and significant correlation among the sermon, church music and church facilities on the level of attendance  96,2%. It means that 96,2 % of level of attendance influenced by sermon, church music and church facilities and the other 28,9% by others. All of the variable partially have significant correlation to level of attendance.

PENGARUH KUALITAS PRODUK DAN RELIGIUSITAS TERHADAP KEPUTUSAN NASABAH PRODUK SIMPANAN PADA BSM CABANG PURWOKERTO

2017 ◽  
Vol 4 (1) ◽  
pp. 189-215
Author(s):  
Yoiz Shofwa Shafrani
Keyword(s):  
Regression Analysis ◽  
Product Quality ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Analytical Tool ◽  
The Other ◽  
Financial Assets ◽  
Islamic Bank

Perkembangan dunia perbankan syariah tidak lepas dari peran para nasabah yang memberikan kepercayaan terhadap pihak perbankan untuk penyimpanan asset keuangannya. Faktanya banyak kelompok nasabah yang memutuskan untuk menjadi nasabah di perbankan syariah karena faktor religiusitasnya. Faktor lain yang dapat ikut mempengaruhi keputusan nasabah adalah kualitas produk. Di mana kualitas produk merupakan karakteristik yang melekat dari suatu produk. Kemungkinan yang terjadi bahwa kebanyakan nasabah pada perbankan syariah juga masih merupakan nasabah perbankan konvensional.Tujuan yang ingin dicapai dalam penelitian ini adalah untuk mengetahui pengaruh kualitas produk dan tingkat religiusitas nasabah terhadap keputusan nasabah untuk menyimpan dananya atau tidak di BSM Cabang Purwokerto. Alat analisis yang digunakan adalah analisis regresi linier berganda, dengan jumlah sampel 100 nasabah. Diperoleh hasil Y = 5,046 + 0,101X1 + 0,218X2. Berdasarkan uji F yang sudah dilakukan maka dapat diketahui bahwa variabel kualitas produk dan religiusitas secara bersama – sama berpengaruh terhadap keputusan nasabah untuk menyimpan dananya di BSM Cabang Purwokerto. Berdasarkan uji t yang sudah dilakukan dapat diketahui bahwa secara partial baik variabel kualitas produk maupun variabel religiusitas berpengaruh terhadap keputusan nasabah untuk menyimpan dananya di BSM Cabang Purwokerto. The progress of the Islamic bank cannot be separated from the role of its customers who give trust to the bank to deposit their financial assets. It is a fact many groups of customers decide to be the customers of the Islamic bank because of their religiosity. The other influences factor of a customer’s decision is the quality of the product. The aim of this research was to determine the effect of product quality and level of customers’ religiosity towards customers’ decision whether to keep their funds in Syariah Mandiri Bank, Branch of Purwokerto, or not. The analytical tool used was multiple linear regression analysis, with a sample of 100 customers. The results indicate Y = 5,046 + 0,101X1 + 0,218X2. Based on F, it can be seen that both variables of product quality and religiosity simultanously affect the customers’ decision to keep theirfunds in BSM Branch of Purwokerto. Based on t test, it can be seen that independently, either variable of product quality or variables of religiosityinfluences the customers’ decision to keep their funds in BSM Branch of Purwokerto.

Cultura organizacional y desempeño innovador en cuatro sectores productivos

Oikos ◽  
2015 ◽  
Vol 15 (31) ◽  
pp. 61
Author(s):  
Rafael G. Ricardo Bray ◽  
Ignacio Gómez Roldán
Keyword(s):  
Organizational Culture ◽  
Organizational Performance ◽  
Innovation Performance ◽  
The Other ◽  
Economic Sector ◽  
Economic Sectors ◽  
Other Hand

RESUMENPara un conjunto de ciento cuarenta y una organizaciones colombianas (95% bogotanas) clasificadas bajo la nomenclatura CIIU pertenecientes a 4 sectores económicos, se estimó el perfil de cultura organizacional de cada una a partir de la aplicación del Organizational Culture Survey (DOCS) de Daniel Denison y asociados. Las organizaciones bogotanas del estudio presentan unos perfiles culturales muy similares, no obstante pertenecer a diferentes sectores económicos. Por otro lado, las relaciones existentes entre las diferentes variables de cultura organizacional y el desempeño innovador difieren entre sectores económicos.Palabras clave: perfil de cultura organizacional, encuesta Denison de cultura organizacional, sector Económico, desempeño organizacional, innovación. Organizational culture and innovation performance in four different sectorsABSTRACTFor a sample of one hundred forty one Colombian organizations (95% located in Bogotá) classified under the CIIU ranking, the organizational culture profile of each one was estimated using the Organizational Culture Survey (DOCS) of Daniel Denison and associates. The organizations studied show very similar cultural profiles, although they belong to different economic sectors. On the other hand, the relationships existing between the different variables of organizational culture and innovation performance differ among economic sectors.Keywords: organizational culture profile, Denison organizational culture survey (DOCS), economic sector, organizational performance, innovation.

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