scholarly journals Regression Pluviometry- Morphometry

2021 ◽  
Vol 5 (1) ◽  
pp. 1-6
Author(s):  
Saidi s ◽  
Keyword(s):  
Linear Regression ◽  
Linear Model ◽  
Local Topography ◽  
Method Analysis

The idea based on AURELHY method (Analysis Using Relief for Hydrometeology) which consists of treating or processing the local topography by means of a linear regression, followed by analysis viakriging residuals of the linear model for this cartographic approach.

scholarly journals Pengaruh Religiositas terhadap Keputusan Pembelian Pakaian Muslimah

2021 ◽  
Vol 4 (1) ◽  
pp. 14-24
Author(s):  
Dessy Trimulyani ◽  
Hendro Lisa ◽  
Ferdinan Ferdinan
Keyword(s):  
Linear Regression ◽  
Quantitative Research ◽  
Muslim Women ◽  
Regression Method ◽  
Linear Regression Method ◽  
Simple Linear Regression ◽  
Method Analysis

Starting from the phenomenon occurred especially among Muslim women who were very concerned with fashion issues. Many of them did not know the function of the clothes they wear, and how Islam arranges the clothes. The purpose of this study was to determine the influence of religiosity on the decision of purchasing Muslim clothes in Tembilahan District. This was quantitative research by simple linear regression method analysis. The results showed that the T count was bigger than the T table. The T table was 12,763>2,024 and sig. 0.000 <0.05 means Ha was accepted, and Ho was rejected. The contribution of religiosity to the decision toward purchasing Muslim clothing by looking at the results of the R Square was 81.1%, while the remaining 18.9% influenced by other factors outside of the researcher's discussion.

scholarly journals Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

2020 ◽  
Author(s):  
Haoua Tall ◽  
Issaka Yaméogo ◽  
Ryan Novak ◽  
Lionel L Ouedraogo ◽  
Ousmane Ouedraogo ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Linear Regression ◽  
Regression Model ◽  
Burkina Faso ◽  
Linear Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Climate Factors ◽  
Multivariate Linear Model

Abstract Background: Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors. Aims: The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district. Data and methods: The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district. Results: The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou. Conclusion: The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.

Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

2020 ◽  
Author(s):  
Haoua Tall ◽  
Issaka Yaméogo ◽  
Ryan Novak ◽  
Lionel L Ouedraogo ◽  
Ousmane Ouedraogo ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Linear Regression ◽  
Regression Model ◽  
Burkina Faso ◽  
Linear Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Climate Factors ◽  
Multivariate Linear Model

Abstract Background Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors.Aims The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district.Data and methods The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district.Results The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou.Conclusion The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.

scholarly journals Simplified large African carnivore density estimators from track indices

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2662 ◽  
Author(s):  
Christiaan W. Winterbach ◽  
Sam M. Ferreira ◽  
Paul J. Funston ◽  
Michael J. Somers
Keyword(s):  
Linear Regression ◽  
Confidence Interval ◽  
Population Size ◽  
Linear Model ◽  
Standard Error ◽  
Linear Models ◽  
Information Criteria ◽  
Large Carnivores ◽  
Simple Linear Regression ◽  
Akaike Information Criteria

BackgroundThe range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices.MethodsWe did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear modely = αx + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models.ResultsThe Lion on Clay and Low Density on Sand models with intercept were not significant (P > 0.05). The other four models with intercept and the six models thorough origin were all significant (P < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support.DiscussionOur results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formulaobserved track density = 3.26 × carnivore densitycan be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km2or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

Prediction

2021 ◽  
pp. 85-96
Author(s):  
Andy Hector
Keyword(s):  
Linear Regression ◽  
Linear Model ◽  
Wood Density ◽  
The Relationship

In this chapter we use linear regression to model the relationship between wood density and timber hardness. Our first goal is to estimate the coefficients of the linear model—the regression intercept and slope. The aim of this chapter is to put those coefficients to work in predicting timber hardness from wood density.

On one twoepoch linear model with the nuisance parameters

2008 ◽  
Vol 58 (1) ◽  
Author(s):  
Karel Hron
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Model ◽  
Linear Regression Model ◽  
Mean Value ◽  
Nuisance Parameters ◽  
Variable Parameters ◽  
Linear Estimators ◽  
Theoretical Results

AbstractThe optimum linear estimators of the useful mean value parameters within a linear regression model with the stable and variable parameters and with the nuisance parameters are derived including their characteristics of accuracy. Some verification of theoretical results is presented.

scholarly journals EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES

2019 ◽  
Vol 25 (1) ◽  
pp. 30-35
Author(s):  
HANANE FIKRI ◽  
TAOUFIQ FECHTALI ◽  
MOHAMED MAMOUMI
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Model ◽  
Model Prediction ◽  
Organophosphorus Pesticides ◽  
Lethal Dose ◽  
Log P ◽  
Organophosphorous Pesticides ◽  
Phase Prediction

Structure-Toxicity Relationships have been studied for a set of 42 organophosphorous pesticides (OPs) through multiple linear regression (MLR) and artificial neural networks (ANN). A model with three descriptors, including: total lipophilicity [log (P)], widths radicals R1 [(LR1)] and R2 [(LR2)] has achieved good results in phase Training and phase prediction of toxicity [log LD50 (lethal dose 50, Oral rat)]. The linear model (MLR: n=40, r²=0.86, s=40 and q2 = 0.66) and non-linear model with a configuration [3-6-1] (ANN: r²=0.95, s=0.73 and q2 = 0.17) have proved very successful and complementary. The selected descriptors indicate the importance of lipophilicity and widths radicals R1 and R2 in the contribution of the toxicity of pesticides derived from OPs used in this study. This information is relevant for the design of a new model of non-toxic pesticides OPs.

scholarly journals Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia

2021 ◽  
Author(s):  
Ratih Oktri Nanda ◽  
Aldilas Achmad Nursetyo ◽  
Aditya Lia Ramadona ◽  
Muhammad Ali Imron ◽  
Anis Fuad ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Human Mobility ◽  
Management Strategies ◽  
Principal Component ◽  
Outbreak Management ◽  
Community Mobility

Background Human mobility could act as a vector to facilitate the spread of infectious diseases. In response to the COVID-19 pandemic, Google Community Mobility Reports (CMR) provide the necessary data to explore community mobility further. Therefore, we aimed to examine the relationship between community mobility on COVID-19 dynamics in Jakarta, Indonesia. Methods We utilized the mobility data from Google from February 15 to December 31, 2020. We explored several statistical models to estimate the COVID-19 dynamics in Jakarta. Model 1 was a Poisson Regression Generalized Linear Model (GLM), Model 2 was a Negative Binomial Regression Generalized Linear Model (GLM), and Model 3 was a Multiple Linear Regression (MLR). Results We found that Multiple Linear Regression (MLR) with some adjustments using Principal Component Analysis (PCA) was the best fit model. It explained 52% of COVID-19 cases in Jakarta (R-Square: 0.52, p<0.05). All mobility variables were significant predictors of COVID-19 cases (p<0.05). More precisely, about 1% change in grocery and pharmacy would contribute to a 4.12% increase of the COVID-19 cases in Jakarta. Retails and recreations, workplaces, transit stations, and parks would result in 3.11%, 2.56%, 2.26%, and 1.93% of more COVID-19 cases, respectively. Conclusion Our study indicates that increased mobility contributes to increased COVID-19 cases. This finding will be beneficial to assist policymakers to have better outbreak management strategies, to anticipate increased COVID-19 cases in the future at certain public places and during seasonal events such as annual religious holidays or other long holidays in particular.

scholarly journals Generalized Vertical Components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244478
Author(s):  
Martino Pesaresi ◽  
Christina Corbane ◽  
Chao Ren ◽  
Ng Edward
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Linear Model ◽  
Vertical Component ◽  
Urban Morphology ◽  
Multivariate Linear Model ◽  
Building Height ◽  
Multi Scale ◽  
Digital Elevation ◽  
Regression Techniques

The estimation of the vertical components of built-up areas from free Digital Elevation Model (DEM) global data filtered by multi-scale convolutional, morphological and textural transforms are generalized at the spatial resolution of 250 meters using linear least-squares regression techniques. Six test cases were selected: Hong Kong, London, New York, San Francisco, Sao Paulo, and Toronto. Five global DEM and two DEM composites are evaluated in terms of 60 combinations of linear, morphological and textural filtering and different generalization techniques. Four generalized vertical components estimates of built-up areas are introduced: the Average Gross Building Height (AGBH), the Average Net Building Height (ANBH), the Standard Deviation of Gross Building Height (SGBH), and the Standard Deviation of Net Building Height (SNBH). The study shows that the best estimation of the net GVC of built-up areas given by the ANBH and SNBH, always contains a greater error than their corresponding gross GVC estimation given by the AGBH and SGBH, both in terms of mean and standard deviation. Among the sources evaluated in this study, the best DEM source for estimating the GVC of built-up areas with univariate linear regression techniques is a composite of the 1-arcsec Shuttle Radar Topography Mission (SRTM30) and the Advanced Land Observing Satellite (ALOS) World 3D–30 m (AW3D30) using the union operator (CMP_SRTM30-AW3D30_U). A multivariate linear model was developed using 16 satellite features extracted from the CMP_SRTM30-AW3D30_U enriched by other land cover sources, to estimate the gross GVC. A RMSE of 2.40 m and 3.25 m was obtained for the AGBH and the SGBH, respectively. A similar multivariate linear model was developed to estimate the net GVC. A RMSE of 6.63 m and 4.38 m was obtained for the ANBH and the SNBH, respectively. The main limiting factors on the use of the available global DEMs for estimating the GVC of built-up areas are two. First, the horizontal resolution of these sources (circa 30 and 90 meters) corresponds to a sampling size that is larger than the expected average horizontal size of built-up structures as detected from nadir-angle Earth Observation (EO) data, producing more reliable estimates for gross vertical components than for net vertical component of built-up areas. Second, post-production processing targeting Digital Terrain Model specifications may purposely filter out the information on the vertical component of built-up areas that are contained in the global DEMs. Under the limitations of the study presented here, these results show a potential for using global DEM sources in order to derive statistically generalized parameters describing the vertical characteristics of built-up areas, at the scale of 250x250 meters. However, estimates need to be evaluated in terms of the specific requirements of target applications such as spatial population modelling, urban morphology, climate studies and so on.

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