scholarly journals True versus perturbed forest inventory plot locations for modeling: a simulation study

2006 ◽  
Vol 36 (3) ◽  
pp. 801-807 ◽  
Author(s):  
John W Coulston ◽  
Kurt H Riitters ◽  
Ronald E McRoberts ◽  
Greg A Reams ◽  
William D Smith
Keyword(s):  
Linear Regression ◽  
Mean Square Error ◽  
Forest Inventory ◽  
Regression Models ◽  
Model Development ◽  
Forest Health ◽  
Mean Square ◽  
Linear Regression Models ◽  
Coarse Spatial Resolution ◽  
The Difference

USDA Forest Service Forest Inventory and Analysis plot information is widely used for timber inventories, forest health assessments, and environmental risk analyses. With few exceptions, true plot locations are not revealed; the plot coordinates are manipulated to obscure the location of field plots and thereby preserve plot integrity. The influence of perturbed plot locations on the development and accuracy of statistical models is unknown. We tested the hypothesis that the influence is related to the spatial structure of the data used in the models. For ordinary kriging we examined the difference in mean square error based on true and perturbed plot locations across a range of spatial autocorrelations. We also examined the difference in mean square error for regression models developed with true and perturbed plot locations across a range of spatial autocorrelations and spatial resolutions. Perturbing plot locations did not significantly influence the accuracy of kriging estimates, but in some situations linear regression model development and accuracy were significantly influenced. Unless the independent variable has high spatial autocorrelation, only coarse spatial resolution data should be used to develop linear regression models.

scholarly journals Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

2021 ◽  
Author(s):  
Reza Norouzi ◽  
Parveen Sihag ◽  
Rasoul Daneshfaraz ◽  
John Abraham ◽  
Vadoud Hasannia
Keyword(s):  
Energy Dissipation ◽  
Linear Regression ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Discharge Rate ◽  
Relative Energy ◽  
Mean Square ◽  
Linear Regression Models ◽  
Soft Computing Techniques

Abstract This study was designed to evaluate the ability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, LR, and MLR to predict the relative energy dissipation(ΔE/Eu) for vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation. In the experiments, the discharge rate, drop height, and porosity of the screens were varied. Parameters yc/h, yd/yc, and p were input variables, and ΔE/Eu was the output variable. The efficiencies of the models were compared using the following metrics: correlation coefficient (CC), mean absolute error (MAE), root-mean-square error (RMSE), Normalized root mean square error (NRMSE) and Nash–Sutcliffe model efficiency (NSE). Results indicate that the performance of the ANFIS_gbellmf based model with a CC value of 0.9953, RMSE value of 0.0069, MAE value of 0.0042, NRMSE value as 0.0092 and NSE value as 0.9895 was superior to other applied models. Also, a linear regression yielded CC = 0.9933, RMSE = 0.0083, and MAE = 0.0067. This linear model outperformed multiple linear regression models. Results from a sensitivity study suggest that yc/h is the most effective parameter for predicting ΔE/Eu.

scholarly journals Influence of year-on-year performance on final degree classification in a chiropractic master's degree program

2016 ◽  
Vol 30 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Philip Dewhurst ◽  
Jacqueline Rix ◽  
David Newell
Keyword(s):  
Logistic Regression ◽  
Problem Solving ◽  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
The Difference ◽  
Degree Classification ◽  
Grade Performance ◽  
Final Degree

Objective: We explored if any predictors of success could be identified from end-of-year grades in a chiropractic master's program and whether these grades could predict final-year grade performance and year-on-year performance. Methods: End-of-year average grades and module grades for a single cohort of students covering all academic results for years 1–4 of the 2013 graduating class were used for this analysis. Analysis consisted of within-year correlations of module grades with end-of-year average grades, linear regression models for continuous data, and logistic regression models for predicting final degree classifications. Results: In year 1, 140 students were enrolled; 85.7% of students completed the program 4 years later. End-of-year average grades for years 1–3 were correlated (Pearson r values ranging from .75 to .87), but the end-of-year grades for years 1–3 were poorly correlated with clinic internship performance. In linear regression, several modules were predictive of end-of-year average grades for each year. For year 1, logistic regression showed that the modules Physiology and Pharmacology and Investigative Imaging were predictive of year 1 performance (odds ratio [OR] = 1.15 and 0.9, respectively). In year 3, the modules Anatomy and Histopathology 3 and Problem Solving were predictors of the difference between a pass/merit or distinction final degree classification (OR = 1.06 and 1.12, respectively). Conclusion: Early academic performance is weakly correlated with final-year clinic internship performance. The modules of Anatomy and Histopathology year 3 and Problem Solving year 3 emerged more consistently than other modules as being associated with final-year classifications.

A Study of the Atmospheric Weighting Mean Temperature in Northwest Plateau of China

2011 ◽  
Vol 137 ◽  
pp. 291-296
Author(s):  
Jing Jiang Zhang ◽  
Yan Li Chu ◽  
Ji Qin Zhong
Keyword(s):  
Water Vapor ◽  
Linear Regression ◽  
Inner Mongolia ◽  
Regression Models ◽  
Precipitable Water ◽  
Linear Regression Models ◽  
Mean Temperature ◽  
Tm Model ◽  
The Difference ◽  
Data Collections

The data from 11 meteorological radiosonde stations in 5 provinces including Shanxi, Shaanxi, Ningxia, Inner Mongolia and Hebei are divided into 9 different data collections which are used to deduce the linear regression models of atmospheric weighting mean temperature (Tm) for Ground-based GPS precipitable water vapor (PWV) retrieval. These 9 models, together with Bevis model, are used to retrieve the GPS PWV at station BGTY. In comparison with the correlations between the ground-based GPS PWV and radiosonde PWV at this station, the difference between these 10 different models of Tm is analyzed. The results show that the Bevis model of Tm can be used to retrieve the GPS PWV of the regions mentioned above. At the same time, the Tm model computed from the radiosonde measurements of specific regions and seasons can provide more accurate GPS PWV than the Bevis model.

scholarly journals Model development for the estimation of urban air temperature based on surface temperature and NDVI

2020 ◽  
Vol 54 (3) ◽  
pp. 29-40 ◽  
Author(s):  
Y Guo ◽  
Tamás Gál ◽  
Guohang Tian ◽  
János Unger
Keyword(s):  
Linear Regression ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Regression Models ◽  
Pearson Correlation ◽  
Model Development ◽  
Urban Air ◽  
Landsat 8 ◽  
Linear Regression Models

Predictive models for urban air temperature (Tair) were developed by using urban land surface temperature (LST) retrieved from Landsat-8 and MODIS data, NDVI retrieved from Landsat-8 data and Tair measured by 24 climatological stations in Szeged. The investigation focused on summer period (June−September) during 2016−2019 in Szeged. The relationship between Tair and LST was analyzed by calculating Pearson correlation coefficient, root-mean-square error and mean-absolute error using the data of 2017−2019, then unary (LST) and binary (LST and NDVI) linear regression models were developed for estimating Tair. The data in 2016 were used to validate the accuracy of the models. Correlation analysis indicated that there were strong correlations during the nighttime and relatively weaker ones during the daytime. The errors between Tair and LSTMODIS-Night was the smallest, followed by LSTMODIS-Day and LSTLandsat-8 respectively. The validation results showed that all models could perform well, especially during nighttime with an error of less than 1.5o. However, the addition of NDVI into the linear regression models did not significantly improve the accuracy of the models, and even had a negative effect. Finally, the influencing factors and temporal and spatial variability of the correlation between Tair and LST were analyzed. LSTLandsat-8 had a larger original error with Tair, but the regression model based on Landsat-8 had a stronger ability to reduce errors.

FUZZY LINEAR REGRESSION ANALYSIS FROM THE POINT OF VIEW RISK

2004 ◽  
Vol 12 (05) ◽  
pp. 635-649 ◽  
Author(s):  
MOHAMMAD MODARRES ◽  
EBRAHIM NASRABADI ◽  
MOHAMMAD MEHDI NASRABADI
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Fuzzy Numbers ◽  
Linear Regression Analysis ◽  
Point Of View ◽  
Risk Averse ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression ◽  
Risk Seeking ◽  
The Difference

In this paper, fuzzy linear regression models with fuzzy/crisp output, fuzzy/crisp input are considered. In this regard, we define risk-neutral, risk-averse and risk-seeking fuzzy linear regression models. In order to do that, two equality indices are applied to express the degree of equality between a pair of fuzzy numbers. We also develop three mathematical models to obtain the parameters of fuzzy linear regression models. Minimizing the difference between the total spread of the observed and estimated values is the objective of these models. The advantage of our proposed models is the simplicity in programming and computation.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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