scholarly journals Extreme and Inconsistent: A Case-Oriented Regression Analysis of Health, Inequality, and Poverty

2020 ◽  
Vol 6 ◽  
pp. 237802312090606
Author(s):  
Simone Rambotti ◽  
Ronald L. Breiger
Keyword(s):  
Regression Analysis ◽  
Income Inequality ◽  
Comparative Research ◽  
Regression Models ◽  
Ordinary Least Squares ◽  
Multiple Model ◽  
Least Squares Regression ◽  
Coefficient Estimates ◽  
Regression Methods ◽  
Inside Out

A methodological paradox characterizes macro-comparative research: it routinely violates the assumptions underlying its dominant method, multiple regression analysis. Comparative researchers have substantive interest in cases, but cases are largely rendered invisible in regression analysis. Researchers seldom recognize the mismatch between the goals of macro-comparative research and the demands of regression methods, and sometimes they end up engaging in strenuous disputes over particular variable effects. A good example is the controversial relationship between income inequality and health. Here, the authors offer an innovative method that combines variable-oriented and case-oriented approaches by turning ordinary least squares regression models “inside out.” The authors estimate case-specific contributions to regression coefficient estimates. They reanalyze data on income inequality, poverty, and life expectancy across 20 affluent countries. Multiple model specifications are dependent primarily on two countries with values on the outcome that are extreme in magnitude and inconsistent with conventional theoretical expectations.

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

Living Arrangements and Sleep-Related Outcomes Among Older Adults in China

2019 ◽  
Vol 91 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Yen-Han Lee ◽  
Yen-Chang Chang ◽  
Timothy Chiang ◽  
Ching-Ti Liu ◽  
Mack Shelley
Keyword(s):  
Older Adults ◽  
Living Arrangements ◽  
Odds Ratio ◽  
Regression Models ◽  
Family Members ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Quality Of Sleep ◽  
Nationally Representative

It has been discussed previously that older adults’ living arrangements are associated with mortality. This study investigated the relationships between older adults’ living arrangements and sleep-related outcomes in China. The nationally representative sample included 4,731 participants who participated on two different occasions, with a total of 9,462 observations (2012 and 2014 waves). Panel logistic regression and panel ordinary least squares regression models were estimated with outcomes of sleep quality and average hours of sleep daily, respectively. Approximately 62% of individuals reported good quality of sleep. We observed that older adults who lived with family members had 17% greater odds of reporting good quality of sleep (adjusted odds ratio = 1.17, 95% confidence interval [1.03, 1.34], p < .05) and reported longer sleep duration daily (β = .334, standard error = .069, p < .01), compared with those who lived alone. Social support is needed to strengthen the residential relationship, especially with family members.

Reconciling temporal conflicts in innovation ambidexterity: the role of TMT temporal leadership

2020 ◽  
Vol 24 (8) ◽  
pp. 1899-1920
Author(s):  
Jiawen Chen ◽  
Linlin Liu
Keyword(s):  
Regression Models ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Exploration And Exploitation ◽  
Temporal Perspective ◽  
Content Type ◽  
Temporal Aspects ◽  
Positive Effect ◽  
Practical Implications

Purpose This study aims to extend the temporal perspective on ambidexterity by investigating how and under what conditions top management team (TMT) temporal leadership improves innovation ambidexterity. Design/methodology/approach Using a questionnaire survey, data were collected from 165 small- and medium-sized enterprises in China. Ordinary least squares regression models were applied to test the hypotheses. Findings The findings show that TMT temporal leadership has a positive effect on innovation ambidexterity and temporal conflict mediates this relationship. Market dynamism and institutional support moderate the indirect effect of TMT temporal leadership on innovation ambidexterity. Practical implications Managers wishing to promote exploration and exploitation simultaneously should pay attention to the temporal aspects of their innovation strategy and improve their temporal leadership activities. Originality/value This study highlights the temporal conflicts in ambidexterity and clarifies the enabling role of TMT temporal leadership. It contributes new insights to the research on organizational ambidexterity and strategic leadership.

Explaining procedural justice during police-suspect encounters

2017 ◽  
Vol 40 (3) ◽  
pp. 574-586 ◽  
Author(s):  
John D. McCluskey ◽  
Michael Reisig
Keyword(s):  
Police Officers ◽  
Regression Models ◽  
Ordinary Least Squares ◽  
Situational Factors ◽  
Self Control ◽  
Social Characteristics ◽  
Observation Data ◽  
Least Squares Regression ◽  
Content Type ◽  
Police Encounters

Purpose The purpose of this paper is to develop and test a series of hypotheses regarding the use of procedurally just policing during suspect encounters. Design/methodology/approach Systematic social observation data from police encounters with suspects are used (N=939). Ordinary least-squares regression models are estimated to evaluate the effects of four variable clusters (i.e. suspect self-presentation, situational factors, suspect social characteristics, and officer characteristics) on procedurally just policing practices. Findings Results from the regression models show that the most salient predictors of police officers exercising authority in a procedurally just manner include the level of self-control displayed by suspects, the number of citizen onlookers, whether the encounter involved a traffic problem, the race/ethnicity of suspects, and suspects’ social status. Research limitations/implications This study focused only on police-suspects encounters where compliance requests were made. While the size of the sample is relatively large, the results from this study do not generalize to all types of police encounters with members of the public. Originality/value This research adds to an emerging body of research focused on predicting procedurally just practices in police encounters. The findings support increased attention to theories that explain police-citizens interactions, and also indicate that further consideration to the measurement of police behavior is warranted.

A Calibration Tutorial for Spectral Data. Part 1: Data Pretreatment and Principal Component Regression Using Matlab

1996 ◽  
Vol 4 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Paul Geladi ◽  
Harald Martens
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Regression Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Statistical Parameters ◽  
Calibration Samples

Regression and calibration play an important role in analytical chemistry. All analytical instrumentation is dependent on a calibration that uses some regression model for a set of calibration samples. The ordinary least squares (OLS) method of building a multivariate linear regression (MLR) model has strict limitations. Therefore, biased or regularised regression models have been introduced. Some selected ones are ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS or PLSR). Also, artificial neural networks (ANN) based on back-propagation can be used as regression models. In order to understand regression models more is needed than just a set of statistical parameters. A deeper understanding of the underlying chemistry and physics is always equally important. For spectral data this means that a basic understanding of spectra and their errors is useful and that spectral representation should be included in judging the usefulness of the data treatment. A “constructed” spectrometric example is introduced. It consists of real spectrometric measurements in the range 408–1176 nm for 26 calibration samples and 10 test samples. The main response variable is litmus concentration, but other constituents such as bromocresolgreen and ZnO are added as interferents and also the pH is changed. The example is introduced as a tutorial. All calculations are shown in detail in Matlab. This makes it easy for the reader to follow and understand the calculations. It also makes the calculations completely traceable. The raw data are available as a file. In Part 1, the emphasis is on pretreatment of the data and on visualisation in different stages of the calculations. Part 1 ends with principal component regression calculations. Partial least squares calculations and some ANN results are presented in Part 2.

scholarly journals PENGARUH DANA ALOKASI KHUSUS DAN PENDAPATAN ASLI DAERAH TERHADAP BELANJA MODAL STUDI KASUS PADA KABUPATEN BOLAANG MONGONDOW TAHUN 2004-2013

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Mohammad Bayu Moha ◽  
Anderson Guntur Kumenaung ◽  
Debby Christina Rotinsulu
Keyword(s):  
Regression Analysis ◽  
Capital Expenditure ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Economic Resources ◽  
Capital Expenditures ◽  
Test Results ◽  
Least Squares Regression ◽  
Ordinary Least Square ◽  
Local Revenue

Abstrak Pendapatan Asli Daerah (PAD)  merupakan salah satu komponen pendapatan utama pemerintah daerah dalam menunjang anggaran rumah tangganya, semakin tinggi tingkat pendapatan yang dimiliki oleh daerah tentu akan semakin tinggi pula tingkatan kemandiriannya dan bisa memaksimalkan pengalokasian anggaran untuk pembangunan sektor-sektor unggulan. Sedangkan Dana Alokasi Khusus (DAK) menjadi sumber pendapatan daerah yang bisa menambah asset local dan secara agreggat menambah pendapatan melalui peningkatan sumber-sumber perekonomian yang dimiliki. Dalam penelitian ini digunakan Ordinary least square dengan analisis regresi berganda dan mendapatkan hasil uji t dan uji f menunjukan bahwa PAD berpengaruh positif dan signifikan terhadap belanja modal sedangkan DAK tidak memberi pengaruh yang signifikan, namun melalui uji R Square didapatkan hasil 82,7 hal ini berarti secara bersama-sama pengaruh PAD dan DAU terhadap belanja modal adalah 82,7 % (persen) sedangkan sisanya dipengaruhi variable lain. Kata kunci : Pendapatan Asli Daerah (PAD), Dana Alokasi Khusus (DAK), Belanja Modal   Abstract Local Revenue  is one of the major revenue components of the local government in supporting the household budget, the higher the level of income that is owned by the region of course the higher the level of independence and can maximize the budget allocation for the development of leading sectors. While the Special Allocation Fund became a source of local revenue that can increase local assets and collectively increase revenue through increased economic resources owned. This study used the Ordinary least squares regression analysis and obtain test results and test t f showed that PAD positive and significant impact on capital expenditures, while DAK does not give a significant influence, but through R Square test showed 82.7 this means  collectively influence of PAD and DAU towards capital expenditure was 82.7% (percent) while the rest influenced other variables. Keywords: Local Revenue,  the Special Allocation Fund, Capital Expenditure  

scholarly journals How discounts impact IPOs valuation performance and underpricing? A confrontation between warranted and unwarranted discounts

2021 ◽  
Vol 3 (1) ◽  
pp. 1-14
Author(s):  
Douaa Tizniti ◽  
◽  
Mohammed Rachid Aasri ◽  
Keyword(s):  
Regression Analysis ◽  
Market Efficiency ◽  
Small Sample Size ◽  
Intrinsic Value ◽  
Fair Value ◽  
Ordinary Least Squares ◽  
Small Sample ◽  
Least Squares Regression ◽  
Final Offer ◽  
Performance Results

Purpose: We investigated the different impacts warranted and unwarranted discounts have on IPOs valuation performance and underpricing. Research methodology: We used multivariate ordinary least squares regression analysis to examine discounts’ determinants, and their impacts on valuation errors and underpricing. We also used bias and accuracy errors to examine valuation performance. Results: We find both final offer price accuracy errors and underpricing negatively related to warranted discounts and positively related to unwarranted discounts. Additionally, warranted discounts are positively related to fair value estimate bias errors, contrarily to unwarranted discounts. Limitations: The relatively small sample size represents our study’s main limitation. Contribution: Unwarranted discounts allow assessing by issuers' underpricing level and underwriters’ sub-optimal efforts and investors' positive returns. Whereas warranted discounts allow issuers to avoid overpricing IPOs and communicate their intrinsic value, investors assess their negative returns, and underwriters reveal their superior qualitative valuation. Regulators can increase after-market efficiency and protect investors by implementing unwarranted discounts’ constraints and warranted discounts’ thresholds.

The Size of Government in Majoritarian and Consensus Democracies

2004 ◽  
Vol 37 (3) ◽  
pp. 340-359 ◽  
Author(s):  
Margit Tavits
Keyword(s):  
Regression Analysis ◽  
Political Institutions ◽  
Ordinary Least Squares ◽  
Partisan Politics ◽  
Systematic Effect ◽  
Government Revenue ◽  
Least Squares Regression ◽  
Size Of Government ◽  
Considerable Evidence ◽  
Institutional Effects

This article looks at the effect of democratic institutions on the size of government. With the help of the ordinary least squares regression analysis of data from the Organization for Economic Cooperation and Development (OECD) countries from 1974 to 1995, the study provides considerable evidence that the variance in the type of democracy, measured by the Lijphart index of majoritarian/consensus political institutions, has a systematic effect on the variance in the size of government, measured both by total government outlays as well as total government revenue as a percentage of gross domestic product. The article further argues that such institutional effects on the size of government are strengthened by partisan politics. More specifically, the analysis demonstrates the presence of the multiplicative interaction effect of the mutually reinforcing nature between the institutional structure and partisan composition of government in their association with the size of government.

scholarly journals NIR-chemometric approaches for evaluating carbonization characteristics of hydrothermally carbonized lignin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung-Wook Hwang ◽  
Un Taek Hwang ◽  
Kyeyoung Jo ◽  
Taekyeong Lee ◽  
Jinseok Park ◽  
...  
Keyword(s):  
Least Squares ◽  
Infrared Spectra ◽  
Regression Models ◽  
Near Infrared ◽  
Prediction Models ◽  
Hydrothermal Carbonization ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Near Infrared Spectra ◽  
Mean Square Errors

AbstractThe aim of this study is to establish prediction models for the non-destructive evaluation of the carbonization characteristics of lignin-derived hydrochars as a carbon material in real time. Hydrochars are produced via the hydrothermal carbonization of kraft lignins for 1–5 h in the temperature range of 175–250 °C, and as the reaction severity of hydrothermal carbonization increases, the hydrochar is converted to a more carbon-intensive structure. Principal component analysis using near-infrared spectra suggests that the spectral regions at 2132 and 2267 nm assigned to lignins and 1449 nm assigned to phenolic groups of lignins are informative bands that indicate the carbonization degree. Partial least squares regression models trained with near-infrared spectra accurately predicts the carbon content, oxygen/carbon, and hydrogen/carbon ratios with high coefficients of determination and low root mean square errors. The established models demonstrate better prediction than ordinary least squares regression models.

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