scholarly journals Using potential distributions to explore environmental correlates of bat species richness in southern Africa: Effects of model selection and taxonomy

2013 ◽  
Vol 59 (3) ◽  
pp. 279-293 ◽  
Author(s):  
M. Corrie Schoeman ◽  
F. P. D. (Woody) Cotterill ◽  
Peter J. Taylor ◽  
Ara Monadjem
Keyword(s):  
Species Richness ◽  
Model Selection ◽  
Southern Africa ◽  
Spatial Scales ◽  
Ordinary Least Squares ◽  
Autoregressive Models ◽  
Least Squares Regression ◽  
Environmental Correlates ◽  
Cart Analysis ◽  
Distribution Ranges

Abstract We tested the prediction that at coarse spatial scales, variables associated with climate, energy, and productivity hypotheses should be better predictor(s) of bat species richness than those associated with environmental heterogeneity. Distribution ranges of 64 bat species were estimated with niche-based models informed by 3629 verified museum specimens. The influence of environmental correlates on bat richness was assessed using ordinary least squares regression (OLS), simultaneous autoregressive models (SAR), conditional autoregressive models (CAR), spatial eigenvector-based filtering models (SEVM), and Classification and Regression Trees (CART). To test the assumption of stationarity, Geographically Weighted Regression (GWR) was used. Bat species richness was highest in the eastern parts of southern Africa, particularly in central Zimbabwe and along the western border of Mozambique. We found support for the predictions of both the habitat heterogeneity and climate/productivity/energy hypotheses, and as we expected, support varied among bat families and model selection. Richness patterns and predictors of Miniopteridae and Pteropodidae clearly differed from those of other bat families. Altitude range was the only independent variable that was significant in all models and it was most often the best predictor of bat richness. Standard coefficients of SAR and CAR models were similar to those of OLS models, while those of SEVM models differed. Although GWR indicated that the assumption of stationarity was violated, the CART analysis corroborated the findings of the curve-fitting models. Our results identify where additional data on current species ranges, and future conservation action and ecological work are needed.

scholarly journals Logistic Regression via Excel Spreadsheets: Mechanics, Model Selection, and Relative Predictor Importance

2021 ◽  
Author(s):  
Michael Brusco
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Model Selection ◽  
Predictive Analytics ◽  
Likelihood Estimation ◽  
Ordinary Least Squares ◽  
Business Analytics ◽  
Least Squares Regression ◽  
Mechanics Model ◽  
Predictor Importance

Logistic regression is one of the most fundamental tools in predictive analytics. Graduate business analytics students are often familiarized with implementation of logistic regression using Python, R, SPSS, or other software packages. However, an understanding of the underlying maximum likelihood model and the mechanics of estimation are often lacking. This paper describes two Excel workbooks that can be used to enhance conceptual understanding of logistic regression in several respects: (i) by providing a clear formulation and solution of the maximum likelihood estimation problem; (ii) by showing the process for testing the significance of logistic regression coefficients; (iii) by demonstrating different methods for model selection to avoid overfitting, specifically, all possible subsets ordinary least squares regression and l1-regularized logistic regression (lasso); and (iv) by illustrating the measurement of relative predictor importance using all possible subsets.

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.

Home Health Agencies: Empirical Evidence on the Patient-Driven Groupings Model’s Expected Effects on Agency Reimbursements

2021 ◽  
pp. 108482232199038
Author(s):  
Elizabeth Plummer ◽  
William F. Wempe
Keyword(s):  
Ordinary Least Squares ◽  
Process Quality ◽  
Home Health ◽  
Least Squares Regression ◽  
Residual Variation ◽  
Health Agencies ◽  
Dual Eligible ◽  
Home Health Agencies ◽  
Seriously Ill Patients ◽  
Seriously Ill

Beginning January 1, 2020, Medicare’s Patient-Driven Groupings Model (PDGM) eliminated therapy as a direct determinant of Home Health Agencies’ (HHAs’) reimbursements. Instead, PDGM advances Medicare’s shift toward value-based payment models by directly linking HHAs’ reimbursements to patients’ medical conditions. We use 3 publicly-available datasets and ordered logistic regression to examine the associations between HHAs’ pre-PDGM provision of therapy and their other agency, patient, and quality characteristics. Our study therefore provides evidence on PDGM’s likely effects on HHA reimbursements assuming current patient populations and service levels do not change. We find that PDGM will likely increase payments to rural and facility-based HHAs, as well as HHAs serving greater proportions of non-white, dual-eligible, and seriously ill patients. Payments will also increase for HHAs scoring higher on quality surveys, but decrease for HHAs with higher outcome and process quality scores. We also use ordinary least squares regression to examine residual variation in HHAs’ expected reimbursement changes under PDGM, after accounting for any expected changes related to their pre-PDGM levels of therapy provision. We find that larger and rural HHAs will likely experience residual payment increases under PDGM, as will HHAs with greater numbers of seriously ill, younger, and non-white patients. HHAs with higher process quality, but lower outcome quality, will similarly benefit from PDGM. Understanding how PDGM affects HHAs is crucial as policymakers seek ways to increase equitable access to safe and affordable non-facility-provided healthcare that provides appropriate levels of therapy, nursing, and other care.

scholarly journals Predicting presenteeism using measures of health status

2021 ◽  
Author(s):  
Cheryl Jones ◽  
Katherine Payne ◽  
Alexander Thompson ◽  
Suzanne M. M. Verstappen
Keyword(s):  
Health Status ◽  
Online Survey ◽  
Activity Index ◽  
Work Productivity ◽  
Rank Correlation ◽  
Ordinary Least Squares ◽  
Statistical Correlation ◽  
Least Squares Regression ◽  
Mapping Algorithm ◽  
Spearman’S Rank Correlation

Abstract Objectives To identify whether it is feasible to develop a mapping algorithm to predict presenteeism using multiattribute measures of health status. Methods Data were collected using a bespoke online survey in a purposive sample (n = 472) of working individuals with a self-reported diagnosis of Rheumatoid arthritis (RA). Survey respondents were recruited using an online panel company (ResearchNow). This study used data captured using two multiattribute measures of health status (EQ5D-5 level; SF6D) and a measure of presenteeism (WPAI, Work Productivity Activity Index). Statistical correlation between the WPAI and the two measures of health status (EQ5D-5 level; SF6D) was assessed using Spearman’s rank correlation. Five regression models were estimated to quantify the relationship between WPAI and predict presenteeism using health status. The models were specified based in index and domain scores and included covariates (age; gender). Estimated and observed presenteeism were compared using tenfold cross-validation and evaluated using Root mean square error (RMSE). Results A strong and negative correlation was found between WPAI and: EQ5D-5 level and WPAI (r = − 0.64); SF6D (r =− 0.60). Two models, using ordinary least squares regression were identified as the best performing models specifying health status using: SF6D domains with age interacted with gender (RMSE = 1.7858); EQ5D-5 Level domains and age interacted with gender (RMSE = 1.7859). Conclusions This study provides indicative evidence that two existing measures of health status (SF6D and EQ5D-5L) have a quantifiable relationship with a measure of presenteeism (WPAI) for an exemplar application of working individuals with RA. A future study should assess the external validity of the proposed mapping algorithms.

From Measures of Association to Multilevel Models: Sociology Journals and the Quantitative Literacy Gap

2020 ◽  
pp. 0092055X2098042
Author(s):  
Thomas J. Linneman
Keyword(s):  
Multilevel Models ◽  
Ordinary Least Squares ◽  
Quantitative Literacy ◽  
Least Squares Regression ◽  
Journal Articles ◽  
Measures Of Association ◽  
Chi Square ◽  
Literacy Gap ◽  
Statistics Course ◽  
Sociology Journals

While most sociology majors must take a statistics course, the content of this course varies widely across departments. Starting from the assumption that sociology students should be able to engage effectively with the sociological literature, this article examines the statistical techniques used in 2,804 journal articles—from four generalist sociology journals from 1990 to 2019 and 11 additional sociology journals from 2019—in order to assess which techniques have risen or fallen in prevalence. Although stalwarts such as ordinary least squares regression, chi-square tests, and t tests maintain strong presences, the rise of logistic regression, interaction effects, and multilevel models has been dramatic. After assessing the proportion of articles students hypothetically could understand given various levels of statistical training, the article ends with suggestions for how to revamp the statistics course to help our students become more numerate citizens, both in their sociology courses and in the world at large.

scholarly journals Lack of Informations about COVID-19 Vaccine: From Implications to Intervention for Supporting Public Health Communications in COVID-19 Pandemic

2021 ◽  
Vol 18 (11) ◽  
pp. 6141
Author(s):  
Silva Guljaš ◽  
Zvonimir Bosnić ◽  
Tamer Salha ◽  
Monika Berecki ◽  
Zdravka Krivdić Dupan ◽  
...  
Keyword(s):  
Vaccine Efficacy ◽  
Health Belief ◽  
Ordinary Least Squares ◽  
Vaccine Hesitancy ◽  
Equal Proportion ◽  
Least Squares Regression ◽  
Online Questionnaire ◽  
Demographic Groups ◽  
Population Immunity ◽  
Wide Range

Lack of knowledge and mistrust towards vaccines represent a challenge in achieving the vaccination coverage required for population immunity. The aim of this study is to examine the opinion that specific demographic groups have about COVID-19 vaccination, in order to detect potential fears and reasons for negative attitudes towards vaccination, and to gain knowledge on how to prepare strategies to eliminate possible misinformation that could affect vaccine hesitancy. The data collection approach was based on online questionnaire surveys, divided into three groups of questions that followed the main postulates of the health belief theory—a theory that helps understanding a behaviour of the public in some concrete surrounding in receiving preventive measures. Ordinary least squares regression analyses were used to examine the influence of individual factors on refusing the vaccine, and to provide information on the perception of participants on the danger of COVID-19 infection, and on potential barriers that could retard the vaccine utility. There was an equal proportion of participants (total number 276) who planned on receiving the COVID-19 vaccine (37%), and of those who did not (36.3%). The rest (26.7%) of participants were still indecisive. Our results indicated that attitudes on whether to receive the vaccine, on how serious consequences might be if getting the infection, as well as a suspicious towards the vaccine efficacy and the fear of the vaccine potential side effects, may depend on participants’ age (<40 vs. >40 years) and on whether they are healthcare workers or not. The barriers that make participants‘ unsure about of receiving the vaccine, such as a distrust in the vaccine efficacy and safety, may vary in different socio-demographic groups and depending on which is the point of time in the course of the pandemic development, as well as on the vaccine availability and experience in using certain vaccine formulas. There is a pressing need for health services to continuously provide information to the general population, and to address the root causes of mistrust through improved communication, using a wide range of policies, interventions and technologies.

Alignment and Identification of the Relationships between Key Performance Indicators in a Multilevel Tree Structure to Support Transportation Agency Decisions

2021 ◽  
pp. 036119812110190
Author(s):  
Hector Donaldo Mata ◽  
Mohammed Hadi ◽  
David Hale
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Ordinary Least Squares ◽  
Learning Tools ◽  
Least Squares Regression ◽  
Upper Level ◽  
Selection Operator ◽  
Transportation Agencies

Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

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