scholarly journals Plotting partial correlation and regression in ecological studies

Web Ecology ◽  
2008 ◽  
Vol 8 (1) ◽  
pp. 35-46 ◽  
Author(s):  
J. Moya-Laraño ◽  
G. Corcobado
Keyword(s):  
Linear Model ◽  
Multiple Regression ◽  
Explanatory Variable ◽  
Regression Line ◽  
Multivariate Techniques ◽  
Least Squares Regression ◽  
Ecological Studies ◽  
Partial Regression ◽  
Explanatory Variables ◽  
Independent Variable

Abstract. Multiple regression, the General linear model (GLM) and the Generalized linear model (GLZ) are widely used in ecology. The widespread use of graphs that include fitted regression lines to document patterns in simple linear regression can be easily extended to these multivariate techniques in plots that show the partial relationship of the dependent variable with each independent variable. However, the latter procedure is not nearly as widely used in ecological studies. In fact, a brief review of the recent ecological literature showed that in ca. 20% of the papers the results of multiple regression are displayed by plotting the dependent variable against the raw values of the independent variable. This latter procedure may be misleading because the value of the partial slope may change in magnitude and even in sign relative to the slope obtained in simple least-squares regression. Plots of partial relationships should be used in these situations. Using numerical simulations and real data we show how displaying plots of partial relationships may also be useful for: 1) visualizing the true scatter of points around the partial regression line, and 2) identifying influential observations and non-linear patterns more efficiently than using plots of residuals vs. fitted values. With the aim to help in the assessment of data quality, we show how partial residual plots (residuals from overall model + predicted values from the explanatory variable vs. the explanatory variable) should only be used in restricted situations, and how partial regression plots (residuals of Y on the remaining explanatory variables vs. residuals of the target explanatory variable on the remaining explanatory variables) should be the ones displayed in publications because they accurately reflect the scatter of partial correlations. Similarly, these partial plots can be applied to visualize the effect of continuous variables in GLM and GLZ for normal distributions and identity link functions.

Analysis of variance (ANOVA).

2021 ◽  
pp. 155-165
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Linear Model ◽  
Analysis Of Variance ◽  
Explanatory Variable ◽  
T Test ◽  
Specific Function ◽  
Real Numbers ◽  
Response Variable ◽  
Explanatory Variables ◽  
Student’S T ◽  
One Way Anova

Abstract Analysis of variance is used to analyze the differences between group means in a sample, when the response variable is numeric (real numbers) and the explanatory variable(s) are all categorical. Each explanatory variable may have two or more factor levels, but if there is only one explanatory variable and it has only two factor levels, one should use Student's t-test and the result will be identical. Basically an ANOVA fits an intercept and slopes for one or more of the categorical explanatory variables. ANOVA is usually performed using the linear model function lm, or the more specific function aov, but there is a special function oneway.test when there is only a single explanatory variable. For a one-way ANOVA the non-parametric equivalent (if variance assumptions are not met) is the kruskal.test.

Analysis of variance (ANOVA).

2021 ◽  
pp. 155-165
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Linear Model ◽  
Analysis Of Variance ◽  
Explanatory Variable ◽  
T Test ◽  
Specific Function ◽  
Real Numbers ◽  
Response Variable ◽  
Explanatory Variables ◽  
Student’S T ◽  
One Way Anova

Abstract Analysis of variance is used to analyze the differences between group means in a sample, when the response variable is numeric (real numbers) and the explanatory variable(s) are all categorical. Each explanatory variable may have two or more factor levels, but if there is only one explanatory variable and it has only two factor levels, one should use Student's t-test and the result will be identical. Basically an ANOVA fits an intercept and slopes for one or more of the categorical explanatory variables. ANOVA is usually performed using the linear model function lm, or the more specific function aov, but there is a special function oneway.test when there is only a single explanatory variable. For a one-way ANOVA the non-parametric equivalent (if variance assumptions are not met) is the kruskal.test.

Improved Comparison of ILI Data and Field Excavations

2012 ◽  
Author(s):  
William V. Harper ◽  
David J. Stucki ◽  
Thomas A. Bubenik ◽  
Clifford J. Maier ◽  
David A. R. Shanks ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Regression Line ◽  
Cost Savings ◽  
Major Axis ◽  
Least Squares Regression ◽  
Text Book ◽  
Probability Of Exceedance ◽  
Management Plans ◽  
Independent Variable ◽  
Reduced Major Axis

The importance of comparing in-line inspection (ILI) calls to excavation data should not be underestimated. Neither should it be undertaken without a solid understanding of the methodologies being employed. Such a comparison is not only a key part of assessing how well the tool performed, but also for an API 1163 evaluation and any subsequent use of the ILI data. The development of unity (1-1) plots and the associated regression analysis are commonly used to provide the basis for predicting the likelihood of leaks or failures from unexcavated ILI calls. Combining such analysis with statistically active corrosion methods into perhaps a probability of exceedance (POE) study helps develop an integrity maintenance plan for the years ahead. The theoretical underpinnings of standard regression analysis are based on the assumption that the independent variable (often thought of as x) is measured without error as a design variable. The dependent variable (often labeled y) is modeled as having uncertainty or error. Pipeline companies may run their regressions differently, but ILI to field excavation regressions often use the ILI depth as the x variable and field depth as the y variable. This is especially the case in which a probability of exceedance analysis is desired involving transforming ILI calls to predicted depths for a comparison to a threshold of interest such as 80% wall thickness. However, in ILI to field depth regressions, both the measured depths can have error. Thus, the underlying least squares regression assumptions are violated. Often one common result is a regression line that has a slope much less than the ideal 1-1 relationship. Reduced Major Axis (RMA) Regression is specifically formulated to handle errors in both the x and y variables. It is not commonly found in the standard literature but has a long pedigree including the 1995 text book Biometry by Sokal and Rohlf in which it appears under the title of Model II regression. In this paper we demonstrate the potential improvements brought about by RMA regression. Building on a solid comparison between ILI data and excavations provides the foundation for more accurate predictions and management plans that reliably provide longer range planning. This may also result in cost savings as the time between ILI runs might be lengthened due to a better analysis of such important data.

Japanese R&D Profitability

2018 ◽  
Vol 8 (2) ◽  
pp. 16-29 ◽  
Author(s):  
Hirokazu Yamada ◽  
Yuji Nakayama
Keyword(s):  
Explanatory Variable ◽  
Value Added ◽  
Production Functions ◽  
Decision Making Process ◽  
Regression Estimation ◽  
Least Squares Regression ◽  
Managerial Decision ◽  
Explanatory Variables ◽  
Error Terms ◽  
Time Lagged

This article examines the contribution to profit from research and development (R&D) using industry-level accounting panel data for eight industries in Japan from 1986 to 2012. Problematically, simple least-squares regression estimation of production functions, where the authors specify sales or value-added as the explained variable and investment in R&D as the explanatory variable, involve endogeneity. Two possible ways of addressing this are the instrumental variables method and another that utilizes the orthogonality between error terms and appropriately time-lagged explanatory variables. The authors compare how both these methods eliminate endogeneity in the estimated production function and thus improve the accuracy of estimates of the rate of return on R&D. These findings thus contribute to the managerial decision-making process on R&D strategy by providing insights into the precise contribution of firm R&D to profit.

scholarly journals Determinants of Differentiation of Cost of Risk (CoR) among Polish Banks during COVID-19 Pandemic

2021 ◽  
Vol 14 (3) ◽  
pp. 110
Author(s):  
Zbigniew Korzeb ◽  
Paweł Niedziółka
Keyword(s):  
Credit Risk ◽  
Regression Model ◽  
Multiple Regression ◽  
Commercial Banks ◽  
Explanatory Variable ◽  
Structural Diversity ◽  
Multiple Regression Model ◽  
Explanatory Variables ◽  
Return On Capital ◽  
The Cost

The aim of the paper is to assess the evolution of the cost of credit risk (CoR) of Polish banks as a result of the COVID-19 pandemic in the first three quarters of 2020 as well as its microeconomic determinants. We analysed the structural diversity of the sample of the 13 largest Polish commercial banks in terms of the evolution of their CoR. For this purpose, a diagraphic method of Jan Czekanowski was used. It allowed us to distinguish two groups of banks displaying features characteristic of multi-object structures and three groups consisting of individual banks characterized by atypical CoR developments, significantly different from the structures of objects classified to the first and second groups. In the second part of the research, in order to identify the determinants of the observed trends, a multiple regression model was used in which the explanatory variable was the dynamics of CoR in the first three quarters of 2020. The parameters of return on capital (ROE) at the end of 2019, Non-Performing Loans (NPLs) at the end of 2019 and the dynamics of write-offs in the period 2017–2019 proved to be important explanatory variables.

scholarly journals A Model of Stress Change under the First COVID-19 Pandemic among the General Public in Japanese Major Cities and Rural Areas

2021 ◽  
Vol 13 (3) ◽  
pp. 1207
Author(s):  
Misato Uehara ◽  
Makoto Fujii ◽  
Kazuki Kobayashi
Keyword(s):  
Rural Areas ◽  
Healthcare Workers ◽  
Explanatory Variable ◽  
Large City ◽  
Stress Change ◽  
Multiple Factor Analysis ◽  
Explanatory Variables ◽  
Private Research ◽  
The Difference ◽  
The Relationship

Research on stress related to the COVID-19 pandemic has been dominated by the cases of healthcare workers, students, patients, and their stress during the COVID-19 pandemic. This study examined the relationship between the amount of stress change under the COVID-19 pandemic and demographic factors (age, sex, occupation, etc.) in residents of a large city and a rural area of Japan. A total of 1331 valid responses were received in June 2020 from residents of Tokyo, Osaka, and Nagano registered with a private research firm. We were able to identify 15 statistically significant variables out of 36 explanatory variables, which explained the significant increase in stress compared to the pre-pandemic period. Multiple-factor analysis showed that the relationship with people is a more significant explanatory variable for the level of increase in stress than the difference in environment between big cities (Tokyo, Osaka) and rural areas (Nagano), the type of housing, and the decrease in income compared to the pre-pandemic period.

International Real Estate Review

2006 ◽  
Vol 9 (1) ◽  
pp. 112-131
Author(s):  
Steven Plaut ◽  
◽  
Egita Uzulena ◽  
Keyword(s):  
Architectural Design ◽  
Explanatory Variable ◽  
Housing Stock ◽  
Housing Markets ◽  
Hedonic Pricing ◽  
Housing Values ◽  
Pricing Models ◽  
Explanatory Variables ◽  
Housing Value ◽  
Physical Features

Architectural design has generally not been included in estimations of hedonic pricing models and the reason is no doubt the difficulty in capturing it in a usable measurement variable. It is usually too idiosyncratic and heterogeneous to “sum up” easily and introduce as an explanatory variable. However, in some housing markets, architectural design consists of a limited number of standardized “prototypes”, which can then be used as explanatory variables in hedonic estimations. Such is the case for Riga, Latvia, where almost the entire housing stock fits into about a score of fairly standardized architectural design types. This paper is an empirical analysis of the Riga housing market, which only became a “market” in a meaningful sense after the collapse of the Soviet regime in Latvia. The paper analyzes a set of about 3500 transactions, all from recent years. We estimate the elasticity of housing value with respect to size of housing units and some other physical features, and the value of the different architectural designs, controlling for location. This is one of the first hedonic or microeconomic analyses of housing values in any post-Soviet transitional economy.

scholarly journals Improving estimations in quantile regression model with autoregressive errors

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 97-107 ◽  
Author(s):  
Bahadır Yuzbasi ◽  
Yasin Asar ◽  
Samil Sik ◽  
Ahmet Demiralp
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Simulation Experiment ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
White Noise Process ◽  
Respiratory Mortality ◽  
Explanatory Variables ◽  
Quantile Regression Model ◽  
Regression Approach

An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.

scholarly journals What Makes an Individual Invest in Stocks?

2019 ◽  
Vol 1 (2) ◽  
pp. 37
Author(s):  
Candy Candy
Keyword(s):  
Multiple Regression ◽  
Quantitative Data ◽  
Subjective Norms ◽  
Behavioural Control ◽  
Analysis Method ◽  
Perceived Behavioural Control ◽  
Behavioral Biases ◽  
Independent Variable

The purpose of this research is to examine the factors that influence intention to invest in stocks of an individual. This research are using quantitative data with 395 questionnaires collected from Batam residents who as sample. Questionnaire consists indicators for independent variable that included attitude, perceived behavioural control, subjective norms, past behavioral biases and intention to invest. Multiple regression used as the analysis method and result showed that all independent variable have a positive significant effect on intention to invest. It means those factors can motivated an individual to invest in stocks market.

scholarly journals Pengaruh Kepemimpinan, Motivasi Kerja, dan Kepuasan Kerja Terhadap Kinerja Personil Polisi Bidokkes Polda Kalimantan Selatan

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Supriadi Noor ◽  
Titien Agustina
Keyword(s):  
Job Satisfaction ◽  
Multiple Regression ◽  
Work Motivation ◽  
Employee Performance ◽  
The South ◽  
Additional Information ◽  
Leadership Motivation ◽  
Analysis Technique ◽  
Dependent Variables ◽  
Independent Variable

The purpose of this study is to analyze the influence of motivational leadership and job satisfaction on the performance of South Kalimantan Police Biddokes personnel. The benefits obtained from this study are providing input or additional information that is meaningful to organizations, companies and further research on leadership, motivation, and job satisfaction with employee performance as a reference for further research.This research variable consists of indentpent variables and dependent variables. The independent variable consists of leadership, motivation and job satisfaction. Whereas the dependent variable consists of employee performance. The analysis technique used is multiple regression (multiple regression) with the help of SPSS 20.0 software.The results of the Leadership, Work Motivation, and Job Satisfaction research of the South Kalimantan Police of Biddokkes went well. Leadership, work motivation, and job satisfaction have a partial effect on the performance of the South Kalimantan Regional Police Biddokkes. Leadership, work motivation, and job satisfaction simultaneously influence the performance of the South Kalimantan Police Biddokkes. Leadership has a dominant effect on the performance of the South Kalimantan Regional Police Biddokkes compared to work motivation and job satisfaction.

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