Multilinear Regression Model to Predict Correlation Between IT Graduate Attributes for Employability Using R

2020 ◽  
pp. 177-184
Author(s):  
Ankita Chopra ◽  
Madan Lal Saini
Keyword(s):  
Regression Model ◽  
Multilinear Regression ◽  
Graduate Attributes

scholarly journals Modelling Timbral Hardness

2019 ◽  
Vol 9 (3) ◽  
pp. 466 ◽  
Author(s):  
Andy Pearce ◽  
Tim Brookes ◽  
Russell Mason
Keyword(s):  
Regression Model ◽  
Sound Source ◽  
Rank Order ◽  
Automatic Generation ◽  
Training Data ◽  
Training Dataset ◽  
Sound Effect ◽  
Multilinear Regression ◽  
Perceptual Model ◽  
Search Results

Hardness is the most commonly searched timbral attribute within freesound.org, a commonly used online sound effect repository. A perceptual model of hardness was developed to enable the automatic generation of metadata to facilitate hardness-based filtering or sorting of search results. A training dataset was collected of 202 stimuli with 32 sound source types, and perceived hardness was assessed by a panel of listeners. A multilinear regression model was developed on six features: maximum bandwidth, attack centroid, midband level, percussive-to-harmonic ratio, onset strength, and log attack time. This model predicted the hardness of the training data with R 2 = 0.76. It predicted hardness within a new dataset with R 2 = 0.57, and predicted the rank order of individual sources perfectly, after accounting for the subjective variance of the ratings. Its performance exceeded that of human listeners.

scholarly journals An analysis of the economic performances of the retail companies in Romania

2020 ◽  
Vol 15 (1) ◽  
pp. 125-133 ◽  
Author(s):  
Mihail Busu ◽  
Madalina Vanesa Vargas ◽  
Ioan Alexandru Gherasim
Keyword(s):  
Regression Model ◽  
Econometric Model ◽  
The Other ◽  
Economic Factors ◽  
Multilinear Regression ◽  
Statistical Software ◽  
Model Based ◽  
Fixed Assets ◽  
Economic Studies

AbstractBased on the finding of the economic studies on the analysis of the performances of the companies from retails sector, this paper aims of analyzing the economic factors which are the basis of economic performances of the new companies from the retails sector of Romania. Starting with an econometric model based on current assets, fixed assets and number of employees, three research hypotheses were tested and validated through a multilinear regression model analyzed with the OLS method with the use of statistical software SPSS 23. The conclusions of the paper are in line with the other researches in the area and underline that the economic performances of the selected companies are determined by the current and fixed assets, as well as the number of employees.

scholarly journals Forecast for the Cameroon’s Residential Electricity Demand Based on the Multilinear Regression Model

2020 ◽  
Vol 12 (05) ◽  
pp. 182-192 ◽  
Author(s):  
Serge Guefano ◽  
Jean Gaston Tamba ◽  
Louis Monkam ◽  
Beguide Bonoma
Keyword(s):  
Regression Model ◽  
Electricity Demand ◽  
Multilinear Regression ◽  
Residential Electricity

scholarly journals Sustainability of Circular Economy Indicators and Their Impact on Economic Growth of the European Union

2019 ◽  
Vol 11 (19) ◽  
pp. 5481 ◽  
Author(s):  
Mihail Busu ◽  
Carmen Lenuta Trica
Keyword(s):  
Economic Growth ◽  
European Union ◽  
Regression Model ◽  
Circular Economy ◽  
Time Frame ◽  
Environmental Indicators ◽  
Environmental Taxes ◽  
Recycling Rate ◽  
The European Union ◽  
Multilinear Regression

In this paper, we develop a methodology for studying the sustainability of the circular economy model, based on environmental indicators, and its impact on European Union (EU) economic growth. In open-end systems, waste is converted back to materials and objects through recycling; hence, a linear economy is transformed into a circular economy (CE). Environmental factors support the argument for the sustainable implementation of a circular economy. The main objective of this paper is to analyze the sustainability of the CE indicators and to elaborate a multilinear regression model with panel data for determining the dependency of the main CE factors on EU economic growth. Starting with the model of economic growth based on circular material use rate, recycling rate of municipal waste (RRMW), trade in recycling materials, labor productivity, environmental taxes, and resource productivity as independent variables, six statistical hypotheses were validated through a multiple regression model with the use of the statistical software EViews 11. The research study was conducted for 27 EU countries, and the data was collected from the European Union Statistical Office (EUROSTAT), during the time frame 2010 to 2017. Based on econometric modeling, the paper highlights that circular economy generates sustainable economic growth across the EU.

Autonomic control of ventricular refractoriness

1981 ◽  
Vol 241 (6) ◽  
pp. H878-H882 ◽  
Author(s):  
S. Nattel ◽  
D. E. Euler ◽  
J. F. Spear ◽  
E. N. Moore
Keyword(s):  
Regression Model ◽  
Right Ventricular ◽  
Nerve Stimulation ◽  
Refractory Period ◽  
Regional Variation ◽  
Vagal Stimulation ◽  
Left Ventricular ◽  
Sympathetic Stimulation ◽  
Multilinear Regression

The effects of vagal and sympathetic stimulation on canine ventricular refractoriness were studied in vivo. Sympathetic stimulation reduced the left ventricular refractory period to an extent linearly related to the logarithm of nerve stimulation frequency. Vagal stimulation had no effect in the absence of sympathetic stimulation but produced a frequency-dependent attenuation of sympathetic effects when the two systems were stimulated simultaneously. The effects of combined vagal and sympathetic stimulation were best described by a multilinear regression model using the logarithm of vagal and sympathetic frequency as covariates. The magnitude of vagal attenuation of sympathetic effects did not show any regional variation at the five widely spaced sites (2 right ventricular, 3 left ventricular) studied.

scholarly journals Predictors of disability attributed to symptoms of increased interrecti distance in women after childbirth: an observational study

2021 ◽  
Author(s):  
Martin Eriksson Crommert ◽  
Ida Flink ◽  
Catharina Gustavsson
Keyword(s):  
Observational Study ◽  
Regression Model ◽  
Emotional Distress ◽  
Psychological Factors ◽  
Regression Coefficient ◽  
Activity Level ◽  
Outcome Variable ◽  
Fear Avoidance ◽  
Multilinear Regression ◽  
Fear Avoidance Beliefs

Abstract Objective The purpose of this study was to investigate how various physical and psychological factors are linked to disability attributed to symptoms from increased interrecti distance (IRD) in women after childbirth. Methods In this cross-sectional observational study, 141 women with an IRD of at least 2 finger widths and whose youngest child was between the ages of 1 and 8 years participated. A multilinear regression model was performed with disability as the outcome variable and fear-avoidance beliefs, emotional distress, body mass index, lumbopelvic pain, IRD, and physical activity level as predictor variables. Results The regression model accounted for 60% (R2 = 0.604, adjusted R2 = 0.586) of the variance in disability (F6,132 = 33.5). The 2 strongest predictors were lumbopelvic pain with a regression coefficient of 1.4 (95% CI = 1.017 to 1.877) and fear avoidance with a regression coefficient of 0.421 (95% CI = 0.287 to 0.555). The actual IRD, with a regression coefficient of −0.133 (95% CI = −1.154 to 0.888), did not contribute significantly to the variation in disability. Conclusion Disability attributed to symptoms from an increased IRD is explained primarily by the level of lumbopelvic pain but also by the degree of fear-avoidance beliefs and emotional distress. Impact This study highlights pain intensity and psychological factors as crucial factors for understanding disability attributed to increased IRD.

scholarly journals Multilinear Regression Model to Estimate Mud Weight

2021 ◽  
Vol 5 (1) ◽  
pp. 1-12
Author(s):  
J. O. Oloro ◽  
T. E. Akhihiero
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Regression Equation ◽  
T Test ◽  
Multilinear Regression ◽  
F Test ◽  
Regression Parameters ◽  
Regression Techniques ◽  
Using Data

Estimation of mud weight poses a serious challenge to mud industries. In this study, a model was developed to tackle the problem of estimation of mud weight using multilinear regression techniques. The model was developed using data obtained from production records. The data include mud weight, water and other chemicals (materials) for nine different samples. The data were analysed to establish linearity and the data was substituted into the multiple regression to form a matrix with nine unknown regression parameters which was substituted into the regression equation to form the model. T-test and F –test was used to validate the model. Results from the test suggest that the developed model was reliable. The model was used to estimate mud weight for four samples and the results are reliable. The effect of each variable was also considered and results also show that each of the variables affects the mud weight.

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