scholarly journals 525 Automated Continuous Instrument Tracking in Laparoscopic Box Trainers Predicts Performance at Assessment: A Prospective Cohort Study in Core Surgical Trainees

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
K Shivank ◽  
R Ilin ◽  
K Walker ◽  
P Brennan
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Video Assessment ◽  
Box Trainers ◽  
Basic Task ◽  
Surgical Trainees ◽  
Instrument Tracking ◽  
Highly Correlated

Abstract Introduction Box-trainers enable deliberate practice of laparoscopic skills and can be equipped with instrument tracking metrics to provide feedback. However, the relationship between metrics, assessment outcomes and practice are unclear. Method Core surgical trainees were provided take-home box trainers with SurgTrac™ instrument tracking metrics for practice (eoSurgical Ltd., Scotland, UK). Practice was incentivised by certification and video assessment of a basic task, peg capping. Assessment was by consultant general surgeons, using objective structured assessment of technical skills (OSATS). The following metrics were analysed: task duration, distance moved by instruments, average instrument speed, average instrument acceleration, left- or right-handedness and instrument smoothness. Metrics were correlated to OSATS scores using regression analysis. Threshold for significance was p = 0.05. Results During the study period, there were 1639 peg capping performances by 85 trainees; 103 video recordings were submitted for assessment. All metrics were significantly associated with OSATS score, except instrument acceleration. The multiple linear regression model was highly correlated with actual scores (Pearson correlation 0.658; p < 0.001). Practice was positively correlated with regression model predicted OSATS score (regression analysis; ANOVA p < 0.001). Conclusions Instrument tracking metrics reliably predict OSATS performance and practice improves predicted score. Trainees can use metrics in unsupervised practice and gauge whether they are improving as expected.

scholarly journals The Effect of Phase on Paper Thickness Optimization in the Paper Recycling Process Using Recycling Machine

2020 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Fauzhia Rahmasari
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Thickness Optimization ◽  
Recycling Process ◽  
Paper Recycling ◽  
Independent Variables ◽  
The Relationship

AbstractEfforts to manage the recycling of paper waste into new paper have been carried out in recent times. It takes a tool or machine that is able to effectively and efficiently recycle used paper into new paper. There are several factors that affect the effectiveness of paper recycling machines, one of which is the paper thickness. One method that can be used to analyze the factors that influence paper thickness in the paper production process using a paper recycling machine is regression analysis. Regression analysis is data analysis techniques in statistics that is used to examine the relationship between several independent variables and dependent variable. However, if we want to examine the relationship or effect of two or more independent variables on a dependent variable, the regression model used is a multiple linear regression model. This study purposes are to analyze the factors that influence paper thickness using a paper recycling machine using multiple linear regression and to inform the modeling about that. The results showed that the factors that affect the paper thickness optimization are destruction and press phase. AbstractUpaya pengelolaan daur ulang sampah kertas menjadi kertas baru telah banyak dilakukan pada jaman sekarang. Dibutuhkan suatu alat atau mesin yang mampu secara efektif dan efisien dalam mendaur ulang kertas bekas menjadi kertas baru. Terdapat beberapa faktor yang mempengaruhi tingkat efektifitas mesin daur ulang kertas diantaranya adalah ketebalan kertas. Salah satu metode yang dapat digunakan untuk menganalisis faktor-faktor yang mempengaruhi ketebalan kertas pada proses produksi kertas menggunakan mesin daur ulang kertas adalah analisis regresi. Analisis regresi merupakan teknik analisis data dalam statistika yang digunakan untuk mengkaji hubungan antara beberapa variabel bebas dengan variabel tidak bebas. Namun, jika ingin mengkaji hubungan atau pengaruh dua atau lebih variabel bebas terhadap satu variabel tidak bebas, maka model regresi yang digunakan adalah model regresi linier berganda. Tujuan dalam penelitian ini yaitu menganalisis faktor-faktor yang mempengaruhi ketebalan kertas menggunakan mesin daur ulang kertas menggunakan regresi linier berganda serta memberikan informasi pemodelan mengenai hal tersebut. Hasil penelitian menunjukkan bahwa faktor yang mempengaruhi keoptimalan ketebalan kertas adalah fase penghancuran dan pemadatan kertas

scholarly journals Regression Models for Predictions of COVID-19 New Cases and New Deaths Based on May/June Data in Ethiopia

2020 ◽  
Author(s):  
Alemayehu Siffir Argawu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Laboratory Tests ◽  
Addis Ababa ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
African Countries ◽  
Predicted Values

As the 15 of June 2020, we have 7,984,067 total COVID-19 cases, globally and 435,181 total deaths. Ethiopia was ranked 2nd and 15th in the table by 176 new cases and by 3,521 total new cases from African countries. Then, this study aimed to predict COVID-19 new cases and new deaths based on May/June data in Ethiopia using regression model. In this study, I used Pearson correlation analysis and the linear regression model to predict COVID-19 new cases and new deaths based on the available data from 12th May to 10th June 2020 in Ethiopia. There was a significant positive correlation between COVID-19 new cases and new deaths with different related variables. In the regression models, the simple linear regression model was a better fit the data of COVID-19 new cases and new deaths than as compared with quadratic and cubic regression models. In the multiple linear regression model, variables such as the number of days, the number of new laboratory tests, and the number of new cases from AA city significantly predicted the COVID-19 new cases. In this model, the number of days and new recoveries significantly predicted new deaths of COVID-19. The number of days, daily laboratory tests, and new cases from Addis Ababa city significantly predicted new COVID-19 cases, and the number of days and new recoveries significantly predicted new deaths from COVID-19. According to this analysis, if strong preventions and action are not taken in the country, the predicted values of COVID-19 new cases and new deaths will be 590 and 12 after two months (after 9th of August) from now, respectively. The researcher recommended that the Ethiopia government, Ministry of Health and Addis Ababa city administrative should give more awareness and protections for societies, and they should also open more COVID-19 laboratory testing centers. Generally, the obtained results of this study may help Ethiopian decision-makers put short-term future plans to face this epidemic.

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.

Analysis of variations in and correlations between selected physical parameters of common beech (Fagus silvatica L.) nuts

2017 ◽  
Vol 1 (21) ◽  
pp. 49-63
Author(s):  
Zdzisław Kaliniewicz ◽  
Piotr Markowski ◽  
Andrzej Anders ◽  
Paweł Tylek ◽  
Zbigniew Krzysiak ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Cross Sections ◽  
Linear Regression Analysis ◽  
Physical Parameters ◽  
Common Beech ◽  
Mesh Screen ◽  
Before And After ◽  
Fagus Silvatica ◽  
Highly Correlated

The basic dimensions and the mass of common beech nuts and seeds from five nut batches, harvested from tree stands in northern Poland, were determined. Environmental conditions had a greater influence on seed plumpness than the age of tree stands. The results of measurements were analyzed statistically by analysis of variance, correlation analysis and linear regression analysis. Despite differences in their plumpness, nuts were characterized by nearly identical cross-sections which resembled an equilateral triangle. The thickness of nuts and seeds was highly correlated with their mass, and this information can facilitate seed husking and separation into mass categories. Before and after husking, seeds should be separated with the use of a mesh screen with longitudinal openings. Medium-sized (most numerous) seeds were separated into the following plumpness categories using a screen separator with ≠6 mm and ≠7 mm openings: 84% of moderately plump seeds, 3% of seeds with reduced plumpness, and 13% of plump seeds.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

scholarly journals Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

2021 ◽  
pp. 1-12
Author(s):  
Pere Oller ◽  
Cristina Baeza ◽  
Glòria Furdada
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Extreme Values ◽  
Multiple Linear Regression Model ◽  
Present Knowledge ◽  
Snow Avalanches ◽  
Independent Variables ◽  
Horizontal Length ◽  
Length And Area ◽  
Runout Modelling

Abstract A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

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