scholarly journals Associating Use of Digital Technology and Self-Reported Health Problems among College Going Students in Delhi-NCR, India

2020 ◽  
Vol 4 (2) ◽  
pp. 44-48
Author(s):  
Sheetal Bawnoo Handoo ◽  
Richa Rathor
Keyword(s):  
College Students ◽  
Linear Regression ◽  
Digital Media ◽  
Digital Technology ◽  
Statistical Tests ◽  
Multiple Linear Regression Model ◽  
Health Problems ◽  
College Going ◽  
Validated Questionnaire ◽  
Reported Health

INTRODUCTION: The increased use of digital media among college students has the tendency to cause various health problems based on the duration and medium used. AIM: To assess the use of digital technology and self- reported health problems among college going students in Delhi-NCR, IndiaMATERIALS AND METHODS: Data was collected using a pre-tested and pre-validated questionnaire which was divided into three sections. The first section contained seven questions regarding demographic details, the second contained three questions regarding the device used, hours spent and the type of media assessed, while the third contained twelve questions regarding self-reported adverse events while accessing digital media. Statistical tests involved the Shapiro-Wilk test, Independent samples t-test, multivariate linear regression and the Pearson’s correlation coefficient. The analysis was done using SPSS version 19.0.RESULTS: Responses of 717 students were included in the final analysis. Most of the students were between 17-19 years (53.9%), the primary device used was smartphone (91.8%). Most students used their device for >1-4 hours (34.6%). The most common self-reported symptom was back and/or neck pain (18.4%) followed by sleep issues/ insomnia (17.7%) and headache (17.3). Multiple linear regression model revealed that good knowledge scores were significantly associated with age(p = 0.04) and the duration of device used (p = 0.02). A positive, linear, great strength of association (r: +0.747) and a significant relationship (p = 0.037) was found between self-reported health problems and the hours of device usage. CONCLUSION: It is advised that college students be advised regarding the ill effects of digital medium without taking proper precautions.

scholarly journals Application of Chosen Methods of Robust Estimation: Baarda’s and Huber’s in Search for Outliers in the Real Estate Market Modeling

2018 ◽  
Vol 18 (1) ◽  
pp. 27-38
Author(s):  
Beata Śpiewak
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Robust Estimation ◽  
Statistical Tests ◽  
Multiple Linear Regression Model ◽  
Real Estate Market ◽  
Unit Price ◽  
Active Method ◽  
The Real Estate ◽  
The Real Estate Market

Abstract In the article, an attempt was made to compile a dataset which was devoid of outliers, on the example of Cracovian apartment market. Robust estimation was the tool which was used, but only its two methods were considered: Baarda’s and Huber’s. Huber’s method belongs to the so-called active methods which means that it allows to eliminate gross errors during the estimation of the parameters of multiple linear regression where a unit price is called a dependent variable or forecasted one. Baarda’s method is a passive method which is based on statistical tests and allows, after determining the parameters of a multiple linear regression model, to indicate the observations which may be burdened with gross errors. Thus both mentioned algorithms differ from each other substantially. In this publication, Baarda’s and Huber’s methods were compared in the context of their effectiveness for the analyzed dataset, and as tools of preparing the data for further analysis. The results showed that Baarda’s method is more appropriate for the analyzed dataset than Huber’s algorithm, but it does not mean that the active method is worse.

scholarly journals Knowledge, perceptions and practices of menstrual hygiene among degree college students in Guntur city of Andhra Pradesh, India

2018 ◽  
Vol 7 (10) ◽  
pp. 4109 ◽  
Author(s):  
Christina Patricia Balla ◽  
Samson Sanjeeva Rao Nallapu
Keyword(s):  
College Students ◽  
Statistical Tests ◽  
Good Knowledge ◽  
Chi Square ◽  
Menstrual Hygiene ◽  
Adverse Health Outcomes ◽  
College Going ◽  
Associated Symptoms ◽  
Sanitary Products ◽  
Self Administered Questionnaire

Background: The unmet menstrual hygiene needs of young girls in India restrict their mobility and daily activities. Taboos and socio-cultural restrictions contribute to poor knowledge and practices leading to adverse health outcomes. This study is set to look at the knowledge, perceptions and practices concerning menstruation among college going girls.Methods: This study was conducted with 254-degree college students from a women’s degree college in Guntur city. After assuring adequate confidentiality, a self-administered questionnaire was administered to each participant. The information obtained was entered and analysed in MS excel. Important findings were subjected to statistical tests like Chi square and Z test for significance testing at 5% LOS.Results: Mother’s education was significantly related to the girls’ knowledge about menses (2 16.6, p 0.00002). A positive perception of menses was associated with good knowledge about it. (p < 0.00001).  Complaints related to menses were also associated with good knowledge scores (2 9.8, p 0.002). Absenteeism during periods was 81.5%, the causes being pain 60.4%, heavy bleeding 31.4%, both pain and heavy bleeding 4.8% and nausea 3.4%.Conclusions: The associated symptoms of menstruation need to be addressed in schools and colleges and in their respective homes. Ensuring availability of sanitary products, water, privacy and appropriate waste disposal in all public services and institutions can address the challenges.

scholarly journals EVALUATION OF MULTIPLE LINEAR REGRESSION MODEL TO OBTAIN DBH OF TREES USING DATA FROM A LIGHTWEIGHT LASER SCANNING SYSTEM ON-BOARD A UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 449-454 ◽  
Author(s):  
M. V. Machado ◽  
A. M. G. Tommaselli ◽  
V. M. Tachibana ◽  
R. P. Martins-Neto ◽  
M. B. Campos
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Statistical Tests ◽  
Low Cost ◽  
Multiple Linear Regression Model ◽  
Urban Trees ◽  
Scanning System ◽  
Linear Regression Method

<p><strong>Abstract.</strong> Vegetation mapping requires information about trees and underlying vegetation to ensure proper management of the urban and forest environments. This information can be obtained using remote sensors. For instance, lightweight systems composed of Unmanned Aerial Vehicles (UAVs) as a platform, low-cost laser units and the recent miniaturized navigation sensors (positioning and orientation) have become a very feasible and flexible alternative. Low-cost UAV-ALS systems usually provide centimetric accuracy in altimetry, according to flight data configuration and quality of observations. This paper presents a feasibility study of a lightweight ALS system on-board a UAV to estimate the diameters at breast height (DBH) of urban trees using LiDAR data and linear regression model. A mathematical model correlating the crown diameter and height of the tree to estimate the DBH was developed based on a linear regression with stepwise method. The stepwise linear regression method enables the addition and the removal of predictor variables through statistical tests. The tree samples were separated in two classes (A and B), according to the diametric distribution. These sample classes were used to define two linear regression models. The regression models that best fit the samples achieved an R<sup>2</sup> adj value above 94% for class A and B, which demonstrates the closeness between the samples and the developed mathematical models. The quality control of the proposed regression models was performed comparing the DBH values estimated and directly measured (reference). DBH of the trees were estimated with an average discrepancy of 8.7&amp;thinsp;cm.</p>

scholarly journals Effects of Highway Landscapes on Drivers’ Eye Movement Behavior and Emergency Reaction Time: A Driving Simulator Study

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Fengxiang Guo ◽  
Mingyuan Li ◽  
Yao Chen ◽  
Jian Xiong ◽  
Jaeyoung Lee
Keyword(s):  
Reaction Time ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Eye Movement ◽  
Driving Simulator ◽  
Statistical Tests ◽  
Descriptive Analysis ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Movement Behavior

This study aims at investigating the effects of highway landscapes and alignments on drivers’ eye movement behavior and emergency reaction time, based on a driving simulator experiment. In this study, four simulation scenarios are evaluated including open space, semiopen space, semiclosed space, and enclosed space landscapes on highways in Yunnan Province, China. Twenty-four experienced drivers participated in a 6-kilometer driving experiment in each landscape scenario. Each subject was required to drive at 80 km/h in the scenarios and the driving behavior data were collected. Three different data analysis methods were employed: (1) descriptive analysis of the characteristics of drivers’ visual fixation area; (2) statistical tests of emergency reaction time with drivers’ demographic characteristics, highway landscapes, and alignments; and (3) multiple linear regression analysis of emergency reaction time, highway landscapes, and alignments. The results show that emergency reaction time is significantly influenced by highway landscapes and alignments, and the multiple linear regression model built in this experiment could accurately predict drivers’ emergency reaction time in different highway landscapes and alignments.

TYPE A-B BEHAVIOR AND SELF-REPORTED HEALTH PROBLEMS

1990 ◽  
Vol 67 (7) ◽  
pp. 960 ◽  
Author(s):  
DONNA M. DAVILLA
Keyword(s):  
Type A ◽  
Health Problems ◽  
Reported Health

Folate Nutritional Status among Psoriasis Patients not Exposed to Antifolate Drug

2020 ◽  
Vol 16 (4) ◽  
pp. 543-553
Author(s):  
Luciana Y. Tomita ◽  
Andréia C. da Costa ◽  
Solange Andreoni ◽  
Luiza K.M. Oyafuso ◽  
Vânia D’Almeida ◽  
...  
Keyword(s):  
Folic Acid ◽  
Linear Regression ◽  
Multiple Linear Regression Model ◽  
Folate Intake ◽  
Serum Folate ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Folic Acid Fortification ◽  
Plasma Folate ◽  
Psoriasis Severity

Background: Folic acid fortification program has been established to prevent tube defects. However, concern has been raised among patients using anti-folate drug, i.e. psoriatic patients, a common, chronic, autoimmune inflammatory skin disease associated with obesity and smoking. Objective: To investigate dietary and circulating folate, vitamin B12 (B12) and homocysteine (hcy) in psoriatic subjects exposed to the national mandatory folic acid fortification program. Methods: Cross-sectional study using the Food Frequency Questionnaire, plasma folate, B12, hcy and psoriasis severity using the Psoriasis Area and Severity Index score. Median, interquartile ranges (IQRs) and linear regression models were conducted to investigate factors associated with plasma folate, B12 and hcy. Results: 82 (73%) mild psoriasis, 18 (16%) moderate and 12 (11%) severe psoriasis. 58% female, 61% non-white, 31% former smokers, and 20% current smokers. Median (IQRs) were 51 (40, 60) years. Only 32% reached the Estimated Average Requirement of folate intake. Folate and B12 deficiencies were observed in 9% and 6% of the blood sample respectively, but hyperhomocysteinaemia in 21%. Severity of psoriasis was negatively correlated with folate and B12 concentrations. In a multiple linear regression model, folate intake contributed positively to 14% of serum folate, and negative predictors were psoriasis severity, smoking habits and saturated fatty acid explaining 29% of circulating folate. Conclusion: Only one third reached dietary intake of folate, but deficiencies of folate and B12 were low. Psoriasis severity was negatively correlated with circulating folate and B12. Stopping smoking and a folate rich diet may be important targets for managing psoriasis.

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.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

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