scholarly journals Regression analysis to predict the impact of dietary neutral detergent fiber on carcass yield in swine1

2019 ◽  
Vol 3 (4) ◽  
pp. 1270-1274 ◽  
Author(s):  
Jose A Soto ◽  
Mike D Tokach ◽  
Steve S Dritz ◽  
Márcio A D Gonçalves ◽  
Jason C Woodworth ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Large Intestine ◽  
Negative Impact ◽  
Statistical Significance ◽  
Regression Equation ◽  
Neutral Detergent Fiber ◽  
Yield Reduction ◽  
Regression Equations ◽  
Carcass Yield ◽  
The Impact

Abstract Research has shown that carcass yield in swine is reduced when ingredients with high neutral detergent fiber (NDF) content. Carcass yield reduction from feeding high-fiber ingredients results from an increase in the weight of intestinal contents. NDF has been shown to result in the digestive contents to swell in the large intestine by absorbing water thus increasing the fecal volume in the large intestine. Considering the financial implications of changing carcass yield, the objective of this project was to develop a regression equation to estimate carcass yield from dietary NDF and strategies where high-NDF ingredients are taken out of the diet in the last dietary phases before slaughter (withdrawal period; WP). Data from 8 experiments (43 observations) originated from 6 journal articles and 1 technical memo were used to develop the regression equation. The WP of high NDF ingredients was either none or ranged from 5 to 63 d in the experiments. Treatment diets of each trial were reformulated to obtain dietary nutrient content using the NRC ingredient library (NRC, Nutrient requirements of swine. 11th ed, 2012). Composition of experimental diets was used to calculate dietary net energy, crude protein, crude fiber, NDF, and acid detergent fiber in the last two dietary phases. These dietary compositions along with the number of days of WP were used to develop regression equations. The model was determined using a step-wise selection procedure starting with guided forward selection through individual predictor variables, with a statistical significance at P < 0.05 used to determine inclusion of terms in the final model. The regression analysis showed that WP, NDF level in the dietary phase prior to the final phase (NDF1), NDF level in the last dietary phase before marketing (NDF2), and the interaction between NDF2 and WP were the most important variables in the dataset to predict carcass yield. The resulting regression equation was as follows: carcass yield, % = 0.03492 ± 0.02633 × WP (d) – 0.05092 ± 0.02862 × NDF1 (%) – 0.06897 ± 0.02931 × NDF2 (%) – 0.00289 ± 0.00216 × (NDF2 [%] × WP [d]) + 76.0769 ± 1.33730. In conclusion, high levels of NDF up to slaughter had a negative impact on carcass yield. Increasing the length of the WP improved carcass yield; however, the effect of WP was dependent on the level of NDF2. The equation herein provides a tool to estimate of the impact of dietary NDF on carcass yield.

295 Regression Analysis to Predict the Impact of Dietary Neutral Detergent Fiber on Carcass Yield.

2018 ◽  
Vol 96 (suppl_2) ◽  
pp. 158-159
Author(s):  
J A Soto ◽  
M D Tokach ◽  
S S Dritz ◽  
M A D Goncalves ◽  
J C Woodworth ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Neutral Detergent Fiber ◽  
Carcass Yield ◽  
The Impact

scholarly journals Regression Analysis to Predict the Impact of High Neutral Detergent Fiber Ingredients on Carcass Yield

2017 ◽  
Vol 3 (7) ◽  
Author(s):  
J. Soto ◽  
M. D. Tokach ◽  
S. S. Dritz ◽  
M. A. Goncalves ◽  
J. C. Woodworth ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Neutral Detergent Fiber ◽  
Carcass Yield ◽  
The Impact

THE MODEL FOR MANAGING INVESTMENTS IN THE MOSCOW ENVIRONMENT BASED ON A POWER REGRESSION EQUATION

2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  
Keyword(s):  
Solid Waste ◽  
Influencing Factors ◽  
Pair Correlation ◽  
Approximation Error ◽  
Regression Equation ◽  
Regression Equations ◽  
The Impact ◽  
The Relationship ◽  
Average Approximation ◽  
Average Approximation Error

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.

scholarly journals Impact of Working Capital Management on Corporate Profitability- Empirical Evidence from Pharmaceutical Industry of Bangladesh

2018 ◽  
Vol 10 (9) ◽  
pp. 136
Author(s):  
Rakibul Islam ◽  
Mohammad Emdad Hossain ◽  
Mohammad Nazmul Hoq ◽  
Md. Morshedul Alam
Keyword(s):  
Regression Analysis ◽  
Correlation Matrix ◽  
Negative Impact ◽  
Pearson Correlation ◽  
Capital Management ◽  
Working Capital ◽  
Data Set ◽  
Working Capital Management ◽  
Pharmaceutical Firms ◽  
The Impact

Working capital management plays centric role in enhancing operational efficiency and their ultimate profitability. Globally financial managers have been searching the proper way on how to utilize working capital components which prolong profitability. The purpose of this study is to assess the impact of working capital components on profitability indicators of selected pharmaceutical firms in Bangladesh. The paper used financial data of 9 pharmaceutical firms listed in Dhaka stock exchange (DSE) covered 2011-2015. Two methods were used in this study for analysis data set. Firstly, to measure the relationship between selected variables Pearson Correlation matrix was used. Secondly, multiple regression analysis was used to investigate the impact working capital components on profitability of selected pharmaceutical firms. The study also conducted Durbin Watson test to assess autocorrelation of selected variables. In this study the correlation matrix identified a negative correlation between working capital components and profitability, whereas regression analysis found number of days account receivable (AR) had significant positive and current ratio (CR) and debt ratio (DR) had appeared a significant negative impact on profitability.

scholarly journals PENGARUH KECERDASAN EMOSI TERHADAP STRES KERJA PADA PENGAJAR MUDA DI GERAKAN INDONESIA MENGAJAR

2019 ◽  
Vol 8 (1) ◽  
pp. 1-11
Author(s):  
Fitri Lestari Issom ◽  
Fiany Aprilia
Keyword(s):  
Emotional Intelligence ◽  
Regression Analysis ◽  
Job Stress ◽  
Negative Impact ◽  
Quantitative Approach ◽  
Stress Scale ◽  
The Impact

The present study attempted to analyze the impact of emotional intelligence towards job stress of Pengajar Muda in Gerakan Indonesia Mengajar. The approach used in the study is quantitative approach. The population of the study was the Pengajar Muda in Gerakan Indonesia Mengajar and the sample of the study was 72 people from Pengajar Muda class of 14, 15, and 16. The instrument used in this study is a job stress scale from Robbins and Emotional Intelligence Questionnaire from Goleman. The data were analyzed using regression analysis. The result of analytics regression is F in the amount of 15,462 with value p in the amount 0,000 < 0,05 is significant, the study revealed that there is negative impact of emotional intelligence towards job stress which indicates that the higher the emotional intelligence means the lower the job stress, and vice versa. The effect of emotional intelligence showed at 16,9% towards job stress.

scholarly journals Economic Growth : The Impact of Zakat Funds and Tax on Business Capital

IKONOMIKA ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 141-156
Author(s):  
Sri Dewi Anggadini (Universitas Komputer, Indonesia) ◽  
Surtikanti Surtikanti (Universitas Komputer, Indonesia) ◽  
Faez M. Hassan (Mustansiriyah University, Iraq)
Keyword(s):  
Economic Growth ◽  
Regression Analysis ◽  
Statistical Analysis ◽  
Data Processing ◽  
Multiple Regression Analysis ◽  
Distribution Data ◽  
Regression Equations ◽  
West Java ◽  
Local Taxes ◽  
The Impact

This study aims to determine the effect of Zakat Funds and Tax on Business Capitales on Economic Growth. The population in this study is the data of the Province of West Java from 2010 to 2017 where the economic growth report, Tax on Business Capital report and zakat fund distribution report are used.   The sample taken by the author in this study is the Zakat Fund Distribution Data, Tax on Business Capital Data and the Gross Regional Domestic Product (PDRB) data of West Java Province from 2010 to 2017. Researchers used multiple statistical analysis and correlation methods. To obtain more accurate results in multiple regression analysis, classic assumption testing is executed so that the results obtained are regression equations. The results of the study show that the Zakat Fund Distribution and Tax on Business Capital data have a significant effect on economic growth. Based on data processing, it shows that the distribution of zakat funds and local taxes simultaneously in providing an influence on economic growth variables of 76%. While the rest of 100% - 76% = another 24%  is the influence of other variables not examined in this study.Keywords : Zakat Funds; Tax on Business Capital; Economic Growth

scholarly journals FORMATION OF FORECAST ON THE INFLATION LEVEL IN SFD TAKING INTO ACCOUNT DYNAMICS OF DEVELOPMENT OF REGIONAL ECONOMY

2020 ◽  
pp. 223-231
Author(s):  
Olga S. Sivash ◽  
Roman S. Usenko
Keyword(s):  
Russian Federation ◽  
Inflation Rate ◽  
Negative Impact ◽  
Statistical Significance ◽  
Pair Correlation ◽  
Federal District ◽  
Statistical Dependence ◽  
Factors Affecting ◽  
The Russian Federation ◽  
The Impact

Inflation is a complex multilateral process, which, in general, has a negative impact on the economy, reduces the level of economic activity of the population and leads to a decrease in the level of real income. The article studies the main constituent elements of the inflation category, reveals the parameters of the impact on the economy of the inflation process, studies the dynamics of factors affecting the forecast inflation rate in the Southern Federal District, develops a regional multifactor inflation model, and based on the approximated data, a forecast of the annual inflation rate is constructed in the Southern Federal District at the end of 2020. The most significant factors affecting the inflation rate in the Russian Federation were identified from the position of the direction of their influence: acceleration or deceleration of inflation, as well as from the point of view of their degree of influence on the inflation rate. At the same time, the influence of the coronavirus pandemic and fluctuations in the oil market on the economic parameters in the Russian Federation in 2020, on the price level of individual food and non-food products was examined. The analyzed indicator of the inflation rate is defined as an indicator of the state of the economic situation in the country, it is revealed that this variable will be dependent. Using the methods of correlation and regression analysis, a mathematical expression is found in the form of a regression model and its adequacy and statistical significance are evaluated. The coefficient of pair correlation, which characterizes the degree of statistical dependence between two variables, without taking into account the influence of other variables, was adopted as the main indicator characterizing the relationship between the analyzed variables. As a result of the calculations, a model of multiple linear regression of the inflation rate was built, the average monthly nominal accrued wages of the employees of the organizations were approximated, and approximation equations were obtained, which made it possible to build an inflation rate forecast for the Southern Federal District for 2019-2020.

scholarly journals Impact of Socio Economics Variables on Poverty: An Empirical Analysis for District Chitral of KPK Pakistan

2017 ◽  
Vol 8 (3) ◽  
pp. 46-53
Author(s):  
Faiz Muhammad ◽  
Amjad Ali
Keyword(s):  
Regression Analysis ◽  
Negative Impact ◽  
Positive Impact ◽  
Household Head ◽  
Sampling Technique ◽  
Multidimensional Poverty ◽  
Poverty Index ◽  
Multidimensional Poverty Index ◽  
Household Poverty ◽  
The Impact

This study investigates the impact of socioeconomic variables on household poverty in Chitral valley, the largest district of Khyber Pakhtunkhwa Province of Pakistan. The household poverty index has been constructed while calculating multidimensional poverty index for each household. For this purpose, a representative sample of 252 households has been surveyed while distributing a questionnaire to each household. The data have been collected through stratified sampling technique and the collected data then analyzed while applying descriptive statistical tools and regression techniques. The regression analysis was done while taking explanatory variables as income of the household, the gender of household head, lives stock population of household, age of household head and dependence ratio of the household. Results of the regression analysis show that lives stock population and income of household have significant negative impact on household poverty. The results further reveal that dependency ratio has also significant positive impact on household poverty. Different diagnostics tests have also been applied in order to test the assumptions of the linear regression model and the results of all the diagnostics show the absence of econometric problems in the estimated model. 

scholarly journals The Impact of an Automated Patient Digital Engagement Platform on Revisit Reduction

Iproceedings ◽  
10.2196/15091 ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. e15091
Author(s):  
Adam Beck ◽  
Caroline Robinson
Keyword(s):  
Emergency Department ◽  
Patient Engagement ◽  
Information Exchange ◽  
Negative Impact ◽  
Statistical Significance ◽  
General Medicine ◽  
Inpatient Setting ◽  
Hospital Inpatient ◽  
Digital Platform ◽  
The Impact

Background Revisits within 30 days to an emergency department (ED), observation care unit, or inpatient setting following patient discharge continues to be a challenge, especially in urban settings. In addition to the consequences for the patient, these revisits have a negative impact on a health system’s finances in a value based care or global budget environment. Objective The objective was to evaluate the effectiveness of a customized automated digital patient engagement application (GetWell Loop) to prevent 30-day revisits after home discharge from an ED or hospital inpatient setting. Methods The LifeBridge Health Innovation Team collaborated with the GetWell Network to customize their patient engagement platform (GetWell Loop) with automated check-in questions and resources. An application link was emailed to adult patients discharged home from the ED. A retrospective study of ED visits for patients treated for general medicine and cardiology conditions (accounting for 24% of our adult ED discharges) between August 1, 2018, and December 31, 2018, was conducted using CRISP, Maryland’s state-designated health information exchange. We used this database to identify the index visits that experienced an emergency department visit, inpatient admission, or observation stay at any Maryland facility within 30 days of discharge. We also used data within GetWell Loop to track patient activation and engagement. The primary endpoint was a comparison of ED patients that experienced a 30-day revisit and who did or did not activate their GetWell Loop account. Secondary end points included overall activation rate and the rate of engagement as measured by the number of logins, alerts, and comments generated by patients through the platform. Statistical significance was calculated using the Fisher’s exact test with a P<.05. Results ED discharges who were treated for general medicine conditions (n=787) and activated their GetWell Loop account experienced a 30-day revisit rate of 18.9% compared to 25.2% who did not activate their account (P=.06). For patients treated for cardiology conditions (n=722), 10.5% of patients who activated their GetWell account experienced a 30-day revisit compared to 17.4% not activating their account (P=.02). During the course of this study, 26% of patients receiving an invite to use the digital platform activated their account (n=1652) logged in a total of 4006 times, generated 734 alerts, and submitted 297 open ended comments/questions. Conclusions These results indicate the potential value of digital health platforms to improve 30-day revisit rates. The strongest impact was observed amongst cardiology patients where the revisit rate is 39.8% lower for patients using GetWell Loop compared to general medicine patients where the relative difference is 25.2%. The results also indicate patients are willing to utilize a digital platform postdischarge to proactively engage in their own care. We attempted to control for potential selection bias that may impact this analysis given patient adoption and use of a digital platform by looking for differences in the subpopulations who did and did not activate the platform. LifeBridge Health is proving healthcare systems can leverage automated mobile platforms to successfully impact clinical outcomes at scale without compromising customer service and patient experience.

A Proposed Methodology for the Assessment of Glaucoma Awareness in Greece: Introduction of the EIT-8G Scale

2011 ◽  
Vol 22 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Georgios Labiris ◽  
Andreas Katsanos ◽  
Michael Fanariotis ◽  
Anna Koutsogianni ◽  
Athanassios Giarmoukakis ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Family History ◽  
Logistic Regression Analysis ◽  
Rasch Analysis ◽  
Statistical Significance ◽  
Multivariate Logistic Regression Analysis ◽  
Control Group ◽  
Valid Instrument ◽  
The Impact

Purpose To develop a reliable and practical questionnaire for glaucoma awareness and evaluate the impact of potential determinants. Methods Patients with primary open-angle, pigmentary, and exfoliation glaucoma, as well as healthy controls, were recruited. The instrument included questions about demographic characteristics, as well as 8 questions assessing the participant's familiarity with glaucoma. Rasch analysis was used for the validation of the questionnaire. The effect of demographics as potential determinants of awareness was examined with a multivariate logistic regression analysis. Bonferroni-corrected statistical significance was tested with the Mann-Whitney U test or one-way analysis of variance. Association between demographics and questionnaire scores was examined with Spearman correlation. Results As indicated by power analysis, responses from 175 patients (mean age 65.5 years) and 314 controls (mean age 43.3 years) were analyzed. Rasch analysis indicated no multidimensionality and good item-person targeting. Mean ± SD awareness scores for the glaucoma and control groups were 4.43±2.10 and 4.20±2.11, respectively (p=0.207). Sex and residence were not predictors of disease awareness, whereas educational level was only a determinant in the control group (p<0.001). Income was a predictor only for patients (r=0.357, p<0.001), whereas family history was predictive for both groups (p<0.001). Logistic regression analysis revealed that only family history was associated with increased awareness (χ2=4.61, p=0.03, odds ratio 1.98). Conclusions This study introduces a practical and valid instrument for the assessment of glaucoma awareness.

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