116WHAT FACTORS KNOWN AT THE TIME OF ADMISSION ARE PREDICTIVE OF INCREASED MORTALITY: A RETROSPECTIVE LINEAR MULTIPLE REGRESSION ANALYSIS OF ADMISSION DATA FROM 23,151 NEW PATIENT EPISODES

2019 ◽  
Vol 48 (Supplement_1) ◽  
pp. i32-i35
Author(s):  
H C Li ◽  
M Taylor
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Linear Multiple Regression ◽  
Admission Data ◽  
Time Of Admission

scholarly journals Enterprise Value and Intellectual Capital: Study of BSE 500 Firms

2018 ◽  
Vol 7 (2) ◽  
pp. 123 ◽  
Author(s):  
Priti Sharma
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Intellectual Capital ◽  
Pearson Correlation ◽  
Market Value ◽  
Linear Multiple Regression ◽  
Listed Firms ◽  
Customer Capital ◽  
Almost All

The purpose of this paper is to estimate the intellectual capital coefficient of the firms under study and to study the relationship, if any between intellectual capital and intellectual capital and its constituents. In this empirical paper, analytical research design has been used. Pulic’s VAIC (modified) has been used to estimate the intellectual capital of BSE S&P 500 listed firms from 2007-2016. The data has been collected from CMIE and collected data has been analyzed using Pearson correlation and linear multiple regression analysis using CMIE PROWESS. Findings show that almost all firms under study have a good VAIC score means above 4 and the top VAIC scorer firms were mainly from refinery, metal, cement, steel, tobacco. Correlation analysis and Linear multiple regression analysis show that M/B ratio has a significant relationship with VACA, VAHU, Research and Development (Innovation capital) and Advertisement expenses (customer capital). Year-wise results depicts that value of adjusted R2 is increasing, in 2007 it was just .164 and in the year 2016 it is .607 which infers that VAIC’s role is improving in measuring the market value of firms under study. Year wise analysis shows that adjusted R2 is improving, so findings may serve as significant input for the firms to use intellectual capital as the main factor for improving the market value of firms. This paper will definitely contribute to the existing literature.

scholarly journals Application of multiple regression analysis in projecting the water demand for the City of Cape Town

2018 ◽  
Vol 13 (3) ◽  
pp. 705-711
Author(s):  
M. Lawens ◽  
C. Mutsvangwa
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Water Demand ◽  
Cape Town ◽  
Water Sources ◽  
Stress Factors ◽  
Water Losses ◽  
Linear Multiple Regression ◽  
The City

Abstract Historically, the city of Cape Town has been affected by water shortages and it can be assumed that the situation will be exacerbated in the coming decades by a growing population, economic development and climatic changes as additional stress factors. In order to defuse the situation, the city of Cape Town has commissioned various feasibility studies concerning the implementation of alternative water sources, with as yet unpublished conclusions. Since sustainable water resource planning requires a comprehensive understanding of the water demand, the objective of this study was to predict the future demand by the city of Cape Town by analysing its significant drivers. For this purpose, a linear multiple regression analysis was applied on parameters which influence water demand, namely: population, economy, water losses and water restrictions. In order to establish the linear multiple regression model and its regression coefficients, historical data was used for the period 2001 to 2012. The result of the regression analysis showed that the water demand of the city of Cape Town is only decisively influenced by population and water losses. In addition, the model indicated that a new source would be required by 2021. Thus, water conservation and water supply strategies can be adapted accordingly to ultimately enable a sustainable management of the water sources in the city of Cape Town.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENGUNGKAPAN CSR DAN DAMPAKNYA TERHADAP NILAI PERUSAHAAN

IQTISHODUNA ◽  
2016 ◽  
Vol 10 (2) ◽  
pp. 79-88
Author(s):  
Muhammad Sulhan
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Firm Value ◽  
Institutional Ownership ◽  
Simple Linear Regression ◽  
Slack Resources ◽  
Linear Multiple Regression ◽  
Csr Disclosure

This research aims to analyze factors that affect the company’s CSR disclosure and to see theimpact of CSR on firm value. The population in this study are all companies listed on the Indonesia StockExchange (IDX) and samples are15 companies manufacturing. Analysis of the data used to test the firsthypothesis is linear multiple regression analysis is to look at the effect of profitability, leverage, institutionalownership, size, and slack resources on the disclosure of CSR, while to test the hypothesis that both areseeing the effect of CSR on firm value, then used a simple linear regression. The results of this study indicatethat the variable profitability, size, and slack resources have a significant impact on CSR. As for the variableleverage and institutional ownership has no significant effect on CSR. The last, variable CSR has a significantinfluenceon the firm value

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