Academic literature on the topic 'OLS Regression Method'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'OLS Regression Method.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "OLS Regression Method"

1

Usman, M., S. I. S. Doguwa, and B. B. Alhaji. "Comparing the Prediction Accuracy of Ridge, Lasso and Elastic Net Regression Models with Linear Regression Using Breast Cancer Data." Bayero Journal of Pure and Applied Sciences 14, no. 2 (2022): 134–49. http://dx.doi.org/10.4314/bajopas.v14i2.16.

Full text
Abstract:
Regularised regression methods have been developed in the past to overcome the shortcomings of ordinarily least squares (OLS) regression of not performing well with respect to both prediction accuracy and model complexity. OLS method may fail or produce regression estimates with high variance in the presence of multi-collinearity or when the predictor variables are greater than the number of observations. This study compares the predictive performance and additional information gained of Ridge, Lasso and Elastic net regularised methods with the classical OLS method using data of breast cancer patients. The findings have shown that using all the predictor variables, the OLS method failed because of the presence of multiple collinearity, while regularised Ridge, Lasso and Elastic net methods produced results that showed the predictor variables mostly significant. Using the training data, the Elastic net and Lasso seemed to indicate more significant predictor variables than the Ridge method. The result also indicated that breast cancer patients in age groups 30-39, those that are married and in stage1 of the disease, have longer survival times, while patients that are in stage2 and stage3 have shorter survival times. The OLS regression produced results only when four of the predictor variables were excluded; even then, the regularised methods still outperformed the OLS regression in terms of prediction accuracy.
APA, Harvard, Vancouver, ISO, and other styles
2

Kim, Jaejin, and Johnson Ching-Hong Li. "Which robust regression technique is appropriate under violated assumptions? A simulation study." Methodology 19, no. 4 (2023): 323–47. http://dx.doi.org/10.5964/meth.8285.

Full text
Abstract:
Ordinary least squares (OLS) regression is widely employed for statistical prediction and theoretical explanation in psychology studies. However, OLS regression has a critical drawback: it becomes less accurate in the presence of outliers and non-random error distribution. Several robust regression methods have been proposed as alternatives. However, each robust regression has its own strengths and limitations. Consequently, researchers are often at a loss as to which robust regression method to use for their studies. This study uses a Monte Carlo experiment to compare different types of robust regression methods with OLS regression based on relative efficiency (RE), bias, root mean squared error (RMSE), Type 1 error, power, coverage probability of the 95% confidence intervals (CIs), and the width of the CIs. The results show that, with sufficient samples per predictor (n = 100), the robust regression methods are as efficient as OLS regression. When errors follow non-normal distributions, i.e., mixed-normal, symmetric and heavy-tailed (SH), asymmetric and relatively light-tailed (AL), asymmetric and heavy-tailed (AH), and heteroscedastic, the robust method (GM-estimation) seems to consistently outperform OLS regression.
APA, Harvard, Vancouver, ISO, and other styles
3

Van Schaeybroeck, B., and S. Vannitsem. "Post-processing through linear regression." Nonlinear Processes in Geophysics 18, no. 2 (2011): 147–60. http://dx.doi.org/10.5194/npg-18-147-2011.

Full text
Abstract:
Abstract. Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
APA, Harvard, Vancouver, ISO, and other styles
4

Murat, Yazici. "A new approach called Weighted Least Squares Ratio (WLSR) Method to M-estimators." Journal of Information Sciences and Computing Technologies 5, no. 1 (2015): 399–414. https://doi.org/10.5281/zenodo.3968500.

Full text
Abstract:
Regression Analysis (RA) is an important statistical tool that is applied in most sciences. The Ordinary Least Squares (OLS) is a tradition method in RA and there are many regression techniques based on OLS. The Weighted Least Squares(WLS) method is iteratively used in M-estimators. The Least Squares Ratio (LSR) method in RA gives better results than OLS, especially in case of the presence of outliers. This paper includes a new approach to M-estimators, called Weighted Least Squares Ratio (WLSR), and comparison of WLS and WLSR according to mean absolute errors of estimation of the regression parameters (mae ß) and dependent value (mae y).
APA, Harvard, Vancouver, ISO, and other styles
5

Khotimah, Khusnul, Kusman Sadik, and Akbar Rizki. "KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL." Indonesian Journal of Statistics and Its Applications 4, no. 1 (2020): 97–115. http://dx.doi.org/10.29244/ijsa.v4i1.502.

Full text
Abstract:
Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.
APA, Harvard, Vancouver, ISO, and other styles
6

Jana, Padrul, Dedi Rosadi, and Epha Diana Supandi. "COMPARISON OF ROBUST ESTIMATION ON MULTIPLE REGRESSION MODEL." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 2 (2023): 0979–88. http://dx.doi.org/10.30598/barekengvol17iss2pp0979-0988.

Full text
Abstract:
This study aimed to compare the robustness of the OLS method with a robust regression model on data that had outliers. The methods used on the robust regression model were M-estimation, MM-estimation, and S-estimation. The step taken was to check the characteristics of the data against outliers. Furthermore, the data were modeled with and without outliers using the OLS method and the M-, MM-, and S-estimations. The results were very different between the data with and without the outlier models in the OLS method. It was reflected in the intercept and standard error variables generated from the models. Meanwhile, the regression model with the M-, MM-, and S-estimations was quite stable and able to withstand the presence of outliers. Based on the three estimations that were robust against the outliers, the MM-estimation was the best candidate because, in addition to having a stable intercept parameter estimation, it also had the smallest standard error, which was 61.9 in the resulting model.
APA, Harvard, Vancouver, ISO, and other styles
7

Türkyılmaz, Serpil, and Kadriye Nurdanay Öztürk. "Analysis of Factors Affecting CO2 Emissions in Türkiye Using Quantile Regression." Sustainability 16, no. 22 (2024): 9634. http://dx.doi.org/10.3390/su16229634.

Full text
Abstract:
This study aims to show how the impact of factors on carbon dioxide (CO2) emissions differs at the quantile level and to demonstrate the superiority of the quantile regression method over the OLS method by using quantile regression and ordinary least squares (OLS) methods in order to examine the factors affecting CO2 emissions in Türkiye in depth. Covering the period 1990–2021, this study evaluates the relationship between CO2 emissions and GDP per capita growth, population growth, and renewable energy consumption. One of the important findings of the study is that the increase in the population ratio, which is insignificant according to the OLS method, positively affects CO2 emissions at the 0.25 quantile point. According to both OLS and quantile regression methods, GDP growth does not affect CO2 emissions, while renewable energy consumption has a significant and negative effect according to both models. These results demonstrate that economic growth has no discernible impact on CO2 emissions in Türkiye, while investments in renewable energy can significantly lower emissions and open the door for quantile regression to be used more widely in related research. Unlike traditional methods that focus only on the conditional mean, the quantile regression method provides a comprehensive framework for Türkiye’s sustainable development policies by exploring factor effects at different emission levels.
APA, Harvard, Vancouver, ISO, and other styles
8

Imrhan, Sheik N. "A Method of Developing more Realistic Predictive Models." Proceedings of the Human Factors Society Annual Meeting 30, no. 9 (1986): 945–49. http://dx.doi.org/10.1177/154193128603000922.

Full text
Abstract:
This study demonstrates a better method of regression analysis than Ordinary Least Squares (OLS) method under certain conditions. Ridge Regression, as it is called is useful in situations where there are strong intercorrelations among regressor variables – a condition called multicollinearity. When OLS regression is used to model the relationship between the response variable and the regressor variables the model may exhibit some undesirable properties. Using ergonomics data exhibiting multicollinearity the use of the Ridge technique is demonstrated. An OLS model for the same data is also presented and compared with the Ridge models. The Ridge model was superior to the latter. It exhibited more realistic properties and predicted more accurately. It is therefore proposed as a valuable tool to the Human Factors/Ergonomics researcher in the development of regression models with highly intercorrelated regressor variables.
APA, Harvard, Vancouver, ISO, and other styles
9

Ajewole, Kehinde Peter, and Adekunle David Adefolarin. "APPLICATION OF THE MAXIMUM LIKELIHOOD APPROACH TO ESTIMATION OF POLYNOMIAL REGRESSION MODEL." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 10, no. 05 (2022): 2693–700. https://doi.org/10.5281/zenodo.6576297.

Full text
Abstract:
The  ordinary  least  squares  (OLS)  method  had  been  extensively  applied  to  estimation  of  different classes  of  regression  model  under  specific  assumptions.  However,  this  estimation  procedure  OLS does  not  perform  well  with  outliers  and  small  sample  sizes.  As  a  result,  this  work  considered  the application of the maximum likelihood method for polynomial regression model using sample sizes as  against  the  large  sample  assumption  in  OLS.  The  efficiency  of  the  maximum  likelihood  (ML) estimation technique  was put to test by comparing its model fit to that of the OLS using some real world data  sets. The  results of analysis of these  data  sets using both  methods  showed  that  the ML outperformed the OLS since it gave better estimates with lower mean square error (MSE) values in all the four data sets considered and higher coefficient of determination (R2) values. Although, both methods resulted in overall good fit, but the ML is more efficient than the OLS because it resulted in lower MSE for small sample sizes.
APA, Harvard, Vancouver, ISO, and other styles
10

Shariff, N. S. M., and H. M. B. Duzan. "A Comparison of OLS and Ridge Regression Methods in the Presence of Multicollinearity Problem in the Data." International Journal of Engineering & Technology 7, no. 4.30 (2018): 36. http://dx.doi.org/10.14419/ijet.v7i4.30.21999.

Full text
Abstract:
The presence of multicollinearity will significantly lead to inconsistent parameter estimates in regression modeling. The common procedure in regression analysis that is Ordinarily Least Squares (OLS) is not robust to multicollinearity problem and will result in inaccurate model. To solve this problem, a number of methods are developed in the literatures and the most common method is ridge regression. Although there are many studies propose variety method to overcome multicolinearity problem in regression analysis, this study proposes the simplest model of ridge regression which is based on linear combinations of the coefficient of the least squares regression of independent variables to determine the value of k (ridge estimator in ridge regression model). The performance of the proposed method is investigated and compared to OLS and some recent existing methods. Thus, simulation studies based on Monte Carlo simulation study are considered. The result of this study is able to produce similar findings as in existing method and outperform OLS in the existence of multicollinearity in the regression modeling.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "OLS Regression Method"

1

Haubeltova, Libuse. "Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics." Thesis, Högskolan Dalarna, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-29979.

Full text
Abstract:
The main purpose of this degree project is to reveal the Airbnb customer’s preferences and quantify the impact of non-market factors on the market price of tourist accommodation in Berlin, Germany. The data retrieved from Airbnb listings, publicly available on Inside Airbnb (2017), was supplemented on indicator of sharing economy accommodation using machine learning method in order to distinguish between amateur and business-running professional hosts. The main aim is to examine the consumers’ preferences and quantify the marginal effect of "real sharing economy" accommodation and other key variables on market price. This is accomplished by model approach using hedonic pricing method, which is used to estimate the economic value of particular attribute. Surprisingly, our data indicates the negative impact of sharing economy indicator on price. The set of motivations of consumers, which determine their valuation of Airbnb listings, was identified. The trade-off between encompass and parsimony of the set was desired in order to build an effective model. Calculation of proportion of explained variance showed that the price is affected mainly by number of accommodated persons, degree of privacy, number of bedrooms, cancellation policy, distance from the city centre and sharing economy indicator in decreasing order.
APA, Harvard, Vancouver, ISO, and other styles
2

Stocker, Toni Clemens. "On the asymptotic properties of the OLS estimator in regression models with fractionally integrated regressors and errors." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-57370.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bandreddy, Naga Abhiram. "Defining Correlation Between Radon, Uranium Deposits, and Oil and Gas Wells Using GIS Regression Methods." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1564687565423414.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Markovic, Strahinja. "Application of LF-NMR measurements and supervised learning regression methods for improved characterization of heavy oils and bitumens." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/89363.

Full text
Abstract:
This work studies the physicochemical properties of unconventional hydrocarbon resources such as heavy oils and bitumens. The principal methods used in the research consisted of LF-NMR experiments, hypothesis testing, and statistical and data-driven modeling. The research output consists of several machine learning and analytical models capable of predicting heavy oil and bitumen viscosity and core sample water saturation with high accuracy. These results provide a strong case for in-situ LF-NMR applications in well logging.
APA, Harvard, Vancouver, ISO, and other styles
5

Oleksandra, Shovkun. "Some methods for reducing the total consumption and production prediction errors of electricity: Adaptive Linear Regression of Original Predictions and Modeling of Prediction Errors." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34398.

Full text
Abstract:
Balance between energy consumption and production of electricityis a very important for the electric power system operation and planning. Itprovides a good principle of effective operation, reduces the generation costin a power system and saves money. Two novel approaches to reduce thetotal errors between forecast and real electricity consumption wereproposed. An Adaptive Linear Regression of Original Predictions (ALROP)was constructed to modify the existing predictions by using simple linearregression with estimation by the Ordinary Least Square (OLS) method.The Weighted Least Square (WLS) method was also used as an alternativeto OLS. The Modeling of Prediction Errors (MPE) was constructed in orderto predict errors for the existing predictions by using the Autoregression(AR) and the Autoregressive-Moving-Average (ARMA) models. For thefirst approach it is observed that the last reported value is of mainimportance. An attempt was made to improve the performance and to getbetter parameter estimates. The separation of concerns and the combinationof concerns were suggested in order to extend the constructed approachesand raise the efficacy of them. Both methods were tested on data for thefourth region of Sweden (“elområde 4”) provided by Bixia. The obtainedresults indicate that all suggested approaches reduce the total percentageerrors of prediction consumption approximately by one half. Resultsindicate that use of the ARMA model slightly better reduces the total errorsthan the other suggested approaches. The most effective way to reduce thetotal consumption prediction errors seems to be obtained by reducing thetotal errors for each subregion.
APA, Harvard, Vancouver, ISO, and other styles
6

Bylesjö, Max. "Latent variable based computational methods for applications in life sciences : Analysis and integration of omics data sets." Doctoral thesis, Umeå universitet, Kemi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1616.

Full text
Abstract:
With the increasing availability of high-throughput systems for parallel monitoring of multiple variables, e.g. levels of large numbers of transcripts in functional genomics experiments, massive amounts of data are being collected even from single experiments. Extracting useful information from such systems is a non-trivial task that requires powerful computational methods to identify common trends and to help detect the underlying biological patterns. This thesis deals with the general computational problems of classifying and integrating high-dimensional empirical data using a latent variable based modeling approach. The underlying principle of this approach is that a complex system can be characterized by a few independent components that characterize the systematic properties of the system. Such a strategy is well suited for handling noisy, multivariate data sets with strong multicollinearity structures, such as those typically encountered in many biological and chemical applications. The main foci of the studies this thesis is based upon are applications and extensions of the orthogonal projections to latent structures (OPLS) method in life science contexts. OPLS is a latent variable based regression method that separately describes systematic sources of variation that are related and unrelated to the modeling aim (for instance, classifying two different categories of samples). This separation of sources of variation can be used to pre-process data, but also has distinct advantages for model interpretation, as exemplified throughout the work. For classification cases, a probabilistic framework for OPLS has been developed that allows the incorporation of both variance and covariance into classification decisions. This can be seen as a unification of two historical classification paradigms based on either variance or covariance. In addition, a non-linear reformulation of the OPLS algorithm is outlined, which is useful for particularly complex regression or classification tasks. The general trend in functional genomics studies in the post-genomics era is to perform increasingly comprehensive characterizations of organisms in order to study the associations between their molecular and cellular components in greater detail. Frequently, abundances of all transcripts, proteins and metabolites are measured simultaneously in an organism at a current state or over time. In this work, a generalization of OPLS is described for the analysis of multiple data sets. It is shown that this method can be used to integrate data in functional genomics experiments by separating the systematic variation that is common to all data sets considered from sources of variation that are specific to each data set.<br>Funktionsgenomik är ett forskningsområde med det slutgiltiga målet att karakterisera alla gener i ett genom hos en organism. Detta inkluderar studier av hur DNA transkriberas till mRNA, hur det sedan translateras till proteiner och hur dessa proteiner interagerar och påverkar organismens biokemiska processer. Den traditionella ansatsen har varit att studera funktionen, regleringen och translateringen av en gen i taget. Ny teknik inom fältet har dock möjliggjort studier av hur tusentals transkript, proteiner och små molekyler uppträder gemensamt i en organism vid ett givet tillfälle eller över tid. Konkret innebär detta även att stora mängder data genereras även från små, isolerade experiment. Att hitta globala trender och att utvinna användbar information från liknande data-mängder är ett icke-trivialt beräkningsmässigt problem som kräver avancerade och tolkningsbara matematiska modeller. Denna avhandling beskriver utvecklingen och tillämpningen av olika beräkningsmässiga metoder för att klassificera och integrera stora mängder empiriskt (uppmätt) data. Gemensamt för alla metoder är att de baseras på latenta variabler: variabler som inte uppmätts direkt utan som beräknats från andra, observerade variabler. Detta koncept är väl anpassat till studier av komplexa system som kan beskrivas av ett fåtal, oberoende faktorer som karakteriserar de huvudsakliga egenskaperna hos systemet, vilket är kännetecknande för många kemiska och biologiska system. Metoderna som beskrivs i avhandlingen är generella men i huvudsak utvecklade för och tillämpade på data från biologiska experiment. I avhandlingen demonstreras hur dessa metoder kan användas för att hitta komplexa samband mellan uppmätt data och andra faktorer av intresse, utan att förlora de egenskaper hos metoden som är kritiska för att tolka resultaten. Metoderna tillämpas för att hitta gemensamma och unika egenskaper hos regleringen av transkript och hur dessa påverkas av och påverkar små molekyler i trädet poppel. Utöver detta beskrivs ett större experiment i poppel där relationen mellan nivåer av transkript, proteiner och små molekyler undersöks med de utvecklade metoderna.
APA, Harvard, Vancouver, ISO, and other styles
7

Galindo-Prieto, Beatriz. "Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection : VIPOPLS, VIPO2PLS, and MB-VIOP methods." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130579.

Full text
Abstract:
Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnPLS model interpretability when analyzing a large number of data sets simultaneously (Paper IV). The results of this thesis, and their enclosed papers, showed that VIPOPLS, VIPO2PLS, and MB-VIOP methods successfully assess the most relevant variables for model interpretation in PLS, OPLS, O2PLS, and OnPLS models. In addition, predictability, robustness, dimensionality reduction, and other variable selection purposes, can be potentially improved/achieved by using these methods.
APA, Harvard, Vancouver, ISO, and other styles
8

Xu, Yuanfang. "An OLS-Based Method for Causal Inference in Observational Studies." Diss., 2019. http://hdl.handle.net/1805/20225.

Full text
Abstract:
Indiana University-Purdue University Indianapolis (IUPUI)<br>Observational data are frequently used for causal inference of treatment effects on prespecified outcomes. Several widely used causal inference methods have adopted the method of inverse propensity score weighting (IPW) to alleviate the in uence of confounding. However, the IPW-type methods, including the doubly robust methods, are prone to large variation in the estimation of causal e ects due to possible extreme weights. In this research, we developed an ordinary least-squares (OLS)-based causal inference method, which does not involve the inverse weighting of the individual propensity scores. We first considered the scenario of homogeneous treatment effect. We proposed a two-stage estimation procedure, which leads to a model-free estimator of average treatment effect (ATE). At the first stage, two summary scores, the propensity and mean scores, are estimated nonparametrically using regression splines. The targeted ATE is obtained as a plug-in estimator that has a closed form expression. Our simulation studies showed that this model-free estimator of ATE is consistent, asymptotically normal and has superior operational characteristics in comparison to the widely used IPW-type methods. We then extended our method to the scenario of heterogeneous treatment effects, by adding in an additional stage of modeling the covariate-specific treatment effect function nonparametrically while maintaining the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE and thus can be utilized to study the treatment effect heterogeneity. We discussed ways of using advanced machine learning techniques in the proposed method to accommodate high dimensional covariates. We applied the proposed method to a case study evaluating the effect of early combination of biologic & non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up treatment plan in children with newly onset of juvenile idiopathic arthritis disease (JIA). The proposed method gives strong evidence of significant effect of early combination at 0:05 level. On average early aggressive use of biologic DMARDs leads to around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month than the step-up plan for treating JIA.
APA, Harvard, Vancouver, ISO, and other styles
9

Silva, Renata Ortiz da. "A universalidade da violência contra as mulheres na sua singularidade: abordagens fenomenológico-existenciais sobre crimes de gênero em Umuarama-PR." Master's thesis, 2021. http://hdl.handle.net/10400.2/10584.

Full text
Abstract:
Esta pesquisa caracterizou a violência doméstica sofrida por mulheres, um tema que tem sido objeto de estudo no âmbito nacional e internacional. No Brasil, a legislação que ampara os aspectos legais da mulher em situação de violência é a Lei Nº 11.340/2006 - Lei Maria da Penha. O objetivo central deste trabalho é analisar a violência contra a mulher a partir de 11 relatos de mulheres atendidas na Delegacia da Mulher (DM) de Umuarama - Paraná – Brasil, a fim de compreender: como a singularidade e a universalidade se encontram em um relato com situações de violência de gênero? Quais elementos impedem a mulher de romper e/ou resistir quando há situação de violência? As hipóteses levantadas foram: o papel histórico da mulher na sociedade atrelado à submissão ao homem, bem como a ausência do Estado em proporcionar segurança para a mulher, o que, por muitas vezes impossibilita romper o quadro de violência que vivencia. O processo de investigação foi ancorado no levantamento de referencial teórico e estudo de caso. A coleta de dados foi realizada por meio de entrevista semiestruturada. Para a análise dos dados, utilizou-se o método progressivo-regressivo, proposto por Sartre no quadro de sua fenomenologia existencial. A partir desta pesquisa, constatou-se que a violência psicológica e/ou verbal esteve presente em todas as entrevistas realizadas, e que a violências física, ameaça, coação e descumprimento de medida protetiva, foram outras configurações que a violência assumiu entre as entrevistadas, sendo que seus maridos/companheiros foram os agressores. Como justificativa para não romper o relacionamento diante das agressões, as entrevistadas relataram o seguinte: medo do agressor, instabilidade financeira e falta de apoio familiar, que na perspectiva sartriana podem ser consideradas como má-fé. As sequelas da violência vivenciada dividem-se em: físicas (hematomas), psicológicas (processos depressivos, ansiedade, e tentativas de suicídio) e sociais (isolamento social). No que se refere às relações familiares, algumas entrevistas descrevem histórico de violência na sua família de origem. Sobre o atendimento ofertado, as entrevistadas afirmam conhecer somente a delegacia da mulher, e terem acesso a poucas informações sobre seus direitos. A investigação nos mostrou que as hipóteses levantadas nesta pesquisa foram confirmadas e que os dados resultantes desta pesquisa fornecem subsídios para que o município de Umuarama-PR repense e reorganize a oferta de políticas públicas para mulheres.<br>This research characterized the domestic violence suffered by women, a theme that has been the object of study at national and international scopes. In Brazil, the legislation that supports the legal aspects of women in situations of violence is Law No. 11,340 / 2006 - Law Maria da Penha. The main objective of this work is to analyze violence against women from 11 reports of women treated at the Women's Police Station (DM) in Umuarama - Paraná - Brazil, in order to understand: how individuality and universality are found in a report with situations of gender violence? What elements prevent women from breaking up and/or resisting when there is a violence situation? The hypotheses raised were: the historical role of women in society linked to submission to men, as well as the State's absence in providing security for women, which, many times, makes it impossible to break the violence situation they experience. The investigation process was anchored in the survey of theoretical references and case study. Data collection was performed through semi-structured interviews. For data analysis, the progressive-regressive method, proposed by Sartre in the framework of his existential phenomenology, was used. From this research, it was found that psychological and/or verbal violence was present in all interviews, and that physical violence, threat, coercion and non-compliance with protective measures, were other configurations that violence assumed among the interviewees, being that their husbands/companions were the aggressors. As a justification for not breaking the relationship in the face of aggression, the interviewees reported the following: fear of the aggressor, financial instability and lack of family support, which in the Sartrian perspective can be considered as bad-faith. The consequences of the violence experienced are divided into: physical (bruises), psychological (depressive processes, anxiety, and suicide attempts) and social (social isolation). Regarding family relationships, some interviews describe a history of violence in their family of origin. Considering the service offered, the interviewees claim to know only the women's police station, and have access to little information about their rights. The investigation showed us that the hypotheses raised in this research were confirmed and that the data resulting from this research provide subsidies for the municipality of Umuarama-PR to rethink and reorganize the offer of public policies for women.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "OLS Regression Method"

1

Witkov, Carey, and Keith Zengel. Chi-Squared Data Analysis and Model Testing for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198847144.001.0001.

Full text
Abstract:
This book is the first to make chi-squared model testing, one of the data analysis methods used to discover the Higgs boson and gravitational waves, accessible to undergraduate students in introductory physics laboratory courses. By including uncertainties in the curve fitting, chi-squared data analysis improves on the centuries old ordinary least squares and linear regression methods and combines best fit parameter estimation and model testing in one method. A toolkit of essential statistical and experimental concepts is developed from the ground up with novel features to interest even those familiar with the material. The presentation of one- and two-parameter chi-squared model testing, requiring only elementary probability and algebra, is followed by case studies that apply the methods to simple introductory physics lab experiments. More challenging topics, requiring calculus, are addressed in an advanced topics chapter. This self-contained and student-friendly introduction to chi-squared analysis and model testing includes a glossary, end-of-chapter problems with complete solutions, and software scripts written in several popular programming languages, that the reader can use for chi-squared model testing. In addition to introductory physics lab students, this accessible introduction to chi-squared analysis and model testing will be of interest to all who need to learn chi-squared model testing, e.g. beginning researchers in astrophysics and particle physics, beginners in data science, and lab students in other experimental sciences.
APA, Harvard, Vancouver, ISO, and other styles
2

Rusten, Kristian A. Referential Null Subjects in Early English. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198808237.001.0001.

Full text
Abstract:
This book offers a large-scale quantitative investigation of referential null subjects as they occur in Old, Middle, and Early Modern English. Using corpus linguistic methods, and drawing on five corpora of early English, the book empirically addresses the occurrence of subjectless finite clauses in more than 500 early English texts, and excerpts of texts, spanning nearly 850 years of the history of English. The book gives an in-depth quantitative analysis of c.80,000 overt and null referential pronominal subjects in 181 Old English texts. On the basis of this substantial data material, the book re-evaluates previous conflicting claims concerning the occurrence and distribution of null subjects in Old English. The book critically addresses the question of whether the earliest stage of English can be considered a canonical or partial pro-drop language. It also provides an empirical examination of the role played by central licensors of null subjects proposed in the theoretical literature, including verbal agreement and Aboutness topicality. The predictions of two important pragmatic accounts of null arguments are also tested. In order to provide a longitudinal perspective, results are provided from an investigation of c.139,000 overt and null referential pronominal subjects occurring in more than 300 Middle and Early Modern English texts and text samples. Throughout, the book builds its arguments by means of powerful statistical tools, including generalized fixed-effects and mixed-effects logistic regression modelling, and is the most comprehensive examination so far provided of null subjects in the history of English.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "OLS Regression Method"

1

Rasheed, Ruqayah H., and Sedqi E. Rezouki. "Cost Prediction of Roads Construction Projects Using OLS Regression Method." In Geotechnical Engineering and Sustainable Construction. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6277-5_53.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hanitzsch, Thomas. "Die Modellierung hierarchischer Datenstrukturen in der Kommunikations- und Medienwissenschaft. Ein Vergleich von OLS-Regression und Mehrebenenanalyse an einem Beispiel aus der Journalismusforschung." In Methoden der Journalismusforschung. VS Verlag für Sozialwissenschaften, 2011. http://dx.doi.org/10.1007/978-3-531-93131-9_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Dessart, Grégory, and Pierre-Yves Brandt. "Humanness and Non-Humanness in Children’s Drawings of God: A Case Study from French-Speaking Switzerland." In When Children Draw Gods. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-94429-2_4.

Full text
Abstract:
AbstractPast research on children’s concepts of God has suggested a developmental tendency moving from anthropomorphic to non-anthropomorphic representations. Besides replication, we tested a model of de-anthropomorphization. Methods. We collected drawings of God (N = 532) from 5- to 17-year-old children in French-speaking Switzerland and constructed a model of anthropomorphism and de-anthropomorphization. Age, gender, and religiosity (i.e., schooling) were utilized as predictor variables in logistic regression analyses. Results. Consistent with past research, both age and religious schooling facilitated the occurrence of non-anthropomorphic God representations. Analyses on de-anthropomorphization revealed that age had a positive effect on most strategies (with one exception), and that schooling did not play a significant role in that regard, neither did gender. Discussion. The current findings move beyond binary oppositions concerning anthropomorphic God figures, which appear to be conceptually much more complex than previously anticipated. Theoretical as well as practical implications are discussed.
APA, Harvard, Vancouver, ISO, and other styles
4

Sümbül, Harun. "Deep Network Model and Regression Analysis using OLS Method for Predicting Lung Vital Capacity." In Business, Management and Economics. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104737.

Full text
Abstract:
With the advancement of technology, many new devices and methods with machine learning and artificial intelligence (ML-AI) have been developed and these methods have begun to play an important role in human life. ML-AI technology is now widely used in many applications such as security, military, communications, bioengineering, medical treatment, food industry, and robotics. In this chapter, deep learning methods and medical usage techniques that have become popular in recent years will be discussed. Experimental and simulation results and a comprehensive example of the biomedical use of the deep network model will be presented. In addition, the regression analysis using the ordinary least squares (OLS) method for estimating lung vital capacity (VC) will be discussed. The simulation results showed that the VC parameter was predicted with higher than 90% accuracy using the proposed deep network model with real data.
APA, Harvard, Vancouver, ISO, and other styles
5

Xin, Jiheng. "Linear Regression Predicts Future Sales and Selling Prices." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230892.

Full text
Abstract:
linear regression is a common technique in machine learning, in the process of our use of machine learning, we will inevitably use linear regression for statistics, this time we used the correlation model of linear regression to study part of the public data set of Olist store Brazilian e-commerce in 2016-2018. The OLS method is mainly adopted, and the relevant sales volume and total sales price are predicted according to the existing data, and the relevant data in the future are predicted. In solving this problem, we have encountered many problems, including the classification of data, and which specific model can be selected to make the fitting effect better is a problem that we need to repeatedly consider.
APA, Harvard, Vancouver, ISO, and other styles
6

Flowerdew, Robin. "Modelling Migration with Poisson Regression." In Technologies for Migration and Commuting Analysis. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-755-8.ch014.

Full text
Abstract:
Most statistical analysis is based on the assumption that error is normally distributed, but many data sets are based on discrete data (the number of migrants from one place to another must be a whole number). Recent developments in statistics have often involved generalising methods so that they can be properly applied to non-normal data. For example, Nelder and Wedderburn (1972) developed the theory of generalised linear modelling, where the dependent or response variable can take a variety of different probability distributions linked in one of several possible ways to a linear predictor, based on a combination of independent or explanatory variables. Several common statistical techniques are special cases of the generalised linear models, including the usual form of regression analysis, Ordinary Least Squares regression, and binomial logit modelling. Another important special case is Poisson regression, which has a Poisson-distributed dependent variable, linked logarithmically to a linear combination of independent variables. Poisson regression may be an appropriate method when the dependent variable is constrained to be a non-negative integer, usually a count of the number of events in certain categories. It assumes that each event is independent of the others, though the probability of an event may be linked to available explanatory variables. This chapter illustrates how Poisson regression can be carried out using the Stata package, proceeding to discuss various problems and issues which may arise in the use of the method. The number of migrants from area i to area j must be a non-negative integer and is likely to vary according to zone population, distance and economic variables. The availability of high-quality migration data through the WICID facility permits detailed analysis at levels from the region to the output areas. A vast range of possible explanatory variables can also be derived from the 2001 Census data. Model results are discussed in terms of the significant explanatory variables, the overall goodness of fit and the big residuals. Comparisons are drawn with other analytic techniques such as OLS regression. The relationship to Wilson’s entropy maximising methods is described, and variants on the method are explained. These include negative binomial regression and zero-censored and zero-truncated models.
APA, Harvard, Vancouver, ISO, and other styles
7

Osterlind, Steven J. "At Least Squares." In The Error of Truth. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198831600.003.0007.

Full text
Abstract:
This chapter focuses on the next important mathematical invention: the method of least squares. First, it sets the historical context for its invention by describing the events in France and Germany leading up to the French Revolution. Next, the chapter describes how the method of least squares was invented twice, first by Adrien-Marie Legendre (as an appendix to his celestial investigations in Nouvelles méthodes pour la détermination des orbites des comètes), and then in a more sophisticated version by Carl Gauss, in Disquisitiones Arithmeticae. After that, an easy-to-understand description of method itself is given. Thus, the chapter goes from observation to probability and on to prediction, through regression, discussing ordinary least squares (OLS), intercepts, and slopes.
APA, Harvard, Vancouver, ISO, and other styles
8

O. Zapata, Hector, and Damilola S. Adebayo. "Flexible Error Specification in CAPM." In Applied and Theoretical Econometrics [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1008857.

Full text
Abstract:
A four-parameter Generalized Lambda Distribution (GLD) quantile regression is applied to a standard capital asset pricing model (CAPM) error specification to jointly estimate moments of the residuals using the daily prices of two farmland Real Estate Investment Trusts (REITs), Nasdaq: LAND and NYSE: FPI as a function of the U.S. S&amp;P 500 from April 2014 to August 2024. The GLD regression also captures the effect of outliers found in the OLS CAPM model, resulting in a closer fit to the theoretical distribution of the GLD residuals. Simulation results revealed symmetrically distributed CAPM coefficients of farmland REITs. The findings suggest that LAND and FPI do not offer portfolio diversification beyond that provided by a market index such as the S&amp;P 500. While the numerical magnitude of the estimated coefficients from the GLD regression is identical to those of least squares, the GLD estimates are more accurate and robust to outliers and more consistent with the distributional properties of daily returns. Future research with this relatively new regression method is briefly discussed.
APA, Harvard, Vancouver, ISO, and other styles
9

Tsuji, Masatsugu, Teruyuki Bunno, Hiroki Idota, Hiroaki Miyoshi, Masaru Ogawa, and Yasushi Ueki. "An Empirical Analysis of Indices and Factors of ICT Use by Small- and Medium-Sized Enterprises in Japan." In Advances in Electronic Government, Digital Divide, and Regional Development. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-709-1.ch011.

Full text
Abstract:
This chapter attempts to extract factors which promote the introduction and usage of ICT by SMEs (small- and medium-sized enterprises) through the method of mail surveys and in-depth interviews conducted in two of the largest SME clusters in Japan, Higashi-Osaka and Ohta Ward, Tokyo. The questionnaire was sent to more than 6,000 SMEs there, and received nearly 1,200 replies. Questions are related to company characteristics and purposes for ICT use. Moreover, the followings indexes are selected which present the degree of ICT use by SMEs: (i) software that contributes to efficient utilization of managerial resources; and (ii) Internet usage. Based on these data, factors are extracted by utilizing the regression methods such as OLS, logit and probit estimation. Among them, the most important elements in promoting ICT use are found to be a future-oriented vision for SMEs such as expectations for restructuring business process through ICT, and managerial orientations.
APA, Harvard, Vancouver, ISO, and other styles
10

Zhang, Yang, and Yue Wu. "Introducing Machine Learning Models to Response Surface Methodologies." In Response Surface Methodology in Engineering Science [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98191.

Full text
Abstract:
Traditional response surface methodology (RSM) has utilized the ordinary least squared (OLS) technique to numerically estimate the coefficients for multiple influence factors to achieve the values of the responsive factor while considering the intersection and quadratic terms of the influencers if any. With the emergence and popularization of machine learning (ML), more competitive methods has been developed which can be adopted to complement or replace the tradition RSM method, i.e. the OLS with or without the polynomial terms. In this chapter, several commonly used regression models in the ML including the improved linear models (the least absolute shrinkage and selection operator model and the generalized linear model), the decision trees family (decision trees, random forests and gradient boosting trees), the model of the neural nets, (the multi-layer perceptrons) and the support vector machine will be introduced. Those ML models will provide a more flexible way to estimate the response surface function that is difficult to be represented by a polynomial as deployed in the traditional RSM. The advantage of the ML models in predicting precise response factor values is then demonstrated by implementation on an engineering case study. The case study has shown that the various choices of the ML models can reach a more satisfactory estimation for the responsive surface function in comparison to the RSM. The GDBT has exhibited to outperform the RSM with an accuracy improvement for 50% on unseen experimental data.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "OLS Regression Method"

1

Lisjak, Josip, Hrvoje Tomic, Ante Roncevic, and Miodrag Roic. "TOWARDS THE INVESTMENT POTENTIAL ASSESSMENT USING SPATIAL DATA MULTI-CRITERIA ANALYSIS AND LINEAR REGRESSION." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/2.1/s08.19.

Full text
Abstract:
The paper presents the results of research on the possibility of calculating the investment potential of a particular area based on its spatial characteristics. The level of spatial unit in this research is local administrative unit (cities or municipalities), while the geographic coverage is entire area of Republic of Croatia. Regarding the method, the results could be applied internationally and are not limited to national borders. Furthermore, when deciding on investing, it is important to know the risk. This risk in the pre-investment cycle is generally estimated on the basis of well-known wellestablished economic methods - without applying multiple criteria in the potential assessment and, among others, criteria of spatial characteristics as one of the most influential ones. Therefore, there was a need to model the investment potential as a precondition for risk calculations based on spatial criteria, which was carried out through this research using multi-criteria GIS analysis. The research in this paper is focused on testing the correlation of spatial features of certain local unit with its development index. The source data used are existing spatial data in the National Spatial Data Infrastructure (NSDI) platform, open data, and the development index as a composite index. The paper shows the results of OLS method and conclusions about influence from certain spatial characteristics on development index, and accordingly the location investment potential based on the results can be modelled.
APA, Harvard, Vancouver, ISO, and other styles
2

Emmer, Filip. "Sdílené ubytování a jeho vliv na ceny nemovitostí: Případová studie z Prahy." In XXV. mezinárodní kolokvium o regionálních vědách. Masaryk University Press, 2022. http://dx.doi.org/10.5817/cz.muni.p280-0068-2022-41.

Full text
Abstract:
This work focuses on short-term rentals, which is essetnial part of the sharing economy. This particular segment and its impact on the property prices has been frequently discussed in both mass media and the academic research. The aim of this study is to identify whether or not short term rentals have statistically significant impact on the property prices. To fulfill the main research aim, multiple OLS regression model is applied. Results of this method indicate that Airbnb has local impact and contributes to price rise in areas with significant amount of short-term rental supply. On the other hand, model provides no evidence of impact on the whole city of Prague, which was selectí as a basis for this study.
APA, Harvard, Vancouver, ISO, and other styles
3

Kaltenecker, E., and K. Okoye. "ARE INNOVATION AND INTERNATIONALIZATION INTERTWINED? A QUANTITATIVE STUDY OF THE IMPACT OF THE TYPES OF PROGRAMS IN ELITE BUSINESS SCHOOLS." In The 7th International Conference on Education 2021. The International Institute of Knowledge Management, 2021. http://dx.doi.org/10.17501/24246700.2021.7115.

Full text
Abstract:
Although relevant pieces of literature discuss innovation in management education and the importance of internationalization of business schools, there is a lack of scholarly articles analyzing the mutual influence between internationalization and innovation in business schools, particularly when considering the types of programs and their location. Henceforth, to fill the identified theoretical gap, this article pursues the following research question: Are innovation and internationalization intertwined in elite business schools? The study follows a two-step methodology in its investigation and experiments. First, we performed a correlational analysis using linear regression (OLS) to determine if there is a relationship between internationalization and innovation of business schools by considering two types of programs: the Global MBA, and the Executive MBA. The results of the OLS method show that there is no correlation between Innovation and Internationalization in the Global MBA programs (p=.546) whereas, there exists a positive correlation between Innovation and Internationalization in Executive MBA (p=.00). Second, we conducted a One-way multivariate analysis of variance (MANOVA) to evaluate the impact of the location of the MBA programs on internationalization and innovation. We found that location presented no significant relationship with internationalization and innovation in the Global MBA program (as their significance levels were p=.483 and p=.490, respectively) and Executive MBA programs (p=.222 and p=.654, respectively). In both cases, these results mean that internationalized or highly innovative programs such as Global MBA and Executive MBA programs can be found all over the world. Thus, we reached two main sets of conclusions. First, innovation and internationalization are uncorrelated in Global MBA programs, whereas both variables (innovation and internationalization) are correlated in Executive MBA programs. For the second set of conclusions, we note that the location of business schools does not impact their internationalization and innovation. Keywords: Educational Innovation, Internationalization, Executive Educatio
APA, Harvard, Vancouver, ISO, and other styles
4

Santana, Graziela Lima de Souza. "Analysis of public transparency in municipalities in Bahia." In V Seven International Multidisciplinary Congress. Seven Congress, 2024. http://dx.doi.org/10.56238/sevenvmulti2024-150.

Full text
Abstract:
This article aimed to investigate the association between economic development and public transparency in the municipalities of Bahia, by analyzing the level of disclosure of Patrimonial Accounting Procedures (PCP), after the obligation determined by Ordinance Secretariat of the National Treasury (STN) nº 548, of September 24, 2015. It is relevant due to its reflection on public transparency, accountability and management of public resources. The research is classified as quantitative and descriptive, using multiple regression, using the Least Squares Method (OLS), with 417 municipalities in Bahia being analyzed in 2022. The association between the variables of interest was analyzed using the Asset Accounting Procedures Disclosure Index (ID-PCP) from the study by Piccini (2018) as a proxy for public transparency and Gross Domestic Product (GDP) as a proxy for economic development. The research results were consistent with the hypothesis, as they indicate the existence of a positive relationship between transparency in public accounting and economic performance, from the perspective of Gross Domestic Product (GDP), that is, the greater the transparency in accounting public, the higher the GDP indices. The use of measurement proxies and the difficulty in finding condensed data on the State Audit Court (TCE) portal and on the Bahia Municipal Audit Court (TCM-BA) portal were presented as limitations of the work. Therefore, it is suggested that further studies verify the implementation of Patrimonial Accounting Procedures (PCP) in public bodies and small municipalities, in different socioeconomic contexts, as well as the use of other specific investigation mechanisms, demonstrating the contribution of accounting to practical issues. in the public sector.
APA, Harvard, Vancouver, ISO, and other styles
5

Silva, Ana Paula Trocoli da. "The impact of responsibility in fiscal management on the financial condition of municipalities." In V Seven International Multidisciplinary Congress. Seven Congress, 2024. http://dx.doi.org/10.56238/sevenvmulti2024-088.

Full text
Abstract:
Responsibility in fiscal management, in accordance with the Fiscal Responsibility Law, presupposes planned and transparent action, through compliance with limits and conditions, including in relation to registration in Remains Payable. The present study sought to identify the influence of responsibility in fiscal management on the financial condition of municipalities. To this end, the influence of the main variable Remaining Payable on the financial situation of municipalities was analyzed, using the econometric model based on Martins et al. (2021). The financial condition is represented by the surplus/deficit divided by the municipalities' total revenue, while the volume of commitments recorded in Remains Payable divided by total expenditure is used as a metric for responsibility in fiscal management. It was also investigated whether the election year (2020 was a municipal election year) has a significant influence on the financial situation of municipalities. To achieve the research objective, a sample of municipalities in Bahia with a population of up to 100 thousand inhabitants was used, whose information was made available in the Brazilian Public Sector Accounting and Tax Information System (Siconfi). The absence of some data was a limitation of this study. Bahia is the state in the Northeast with the most municipalities, which justifies the sample selection. Furthermore, there are no related studies that cover the region. The period chosen was from 2019 to 2021, using multiple linear regression and applying the Ordinary Least Squares (OLS) method. The results suggest that the main variable Remains to Pay negatively influences the financial situation of the municipalities, while the election year, represented by the dummy variable , did not present statistical significance. This result highlights the impact of Remains Payable on the financial situation of municipalities, revealing that better control of this institute by managers must be carried out.
APA, Harvard, Vancouver, ISO, and other styles
6

Jia, Yuqing, Yaxiao Mo, Wenbo Wang, Shengming Guo, and Li Ma. "Deep-Sea Source Ranging Method Using Modified General Regression Neural Network." In 2021 OES China Ocean Acoustics (COA). IEEE, 2021. http://dx.doi.org/10.1109/coa50123.2021.9520053.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yun, Wu, Li Xiukun, and Cao Zhimin. "DOA Estimation of Wideband LFM Sources based on Narrowband Methods Integration Using Random Forest Regression." In 2021 OES China Ocean Acoustics (COA). IEEE, 2021. http://dx.doi.org/10.1109/coa50123.2021.9519995.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Atthi, Aneel Jordan, Aliyu Adebayo Sulaimon, and Oluwatoyin Kunle Akinsete. "New Oil Formation Volume Factor Correlation for Nigerian Crude Oils." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/211968-ms.

Full text
Abstract:
Abstract A comprehensive description of reservoir fluid properties is critical in developing solutions and resolving reservoir engineering issues. The oil formation volume factor, βo, is an indispensable reservoir fluid property in reservoir engineering calculations. In this study, we used a total of 11040 data points from 1840 oil samples to develop new βo correlations for the Nigerian crude oils specifically, and another set of correlations for the other regions herein referred to as the global crude oils. Linear regression (LR), multiple linear regression (MLR), multiple non-linear regression (MNLR), neural network (NN), support vector machine (SVM), and the group method of data handling (GMDH) techniques were used to develop several correlations. Results show that the GMDH method yielded the best correlation while the MNLR is the least accurate. The root means square error (RMSE) for the Nigerian, and Global correlations are 0.0033, and 0.0256 respectively. The two correlations are reliably better in terms of accuracy than the existing correlations. The new correlations would facilitate a more accurate reservoir characterization, and reliable design of surface equipment.
APA, Harvard, Vancouver, ISO, and other styles
9

Ma, Jie, Song Hu, and Haipeng Wang. "The Logistic Lasso and Ridge Regression Algorithm-Based Airline Passenger Satisfaction Prediction Model." In 2024 International Conference on Smart Transportation Interdisciplinary Studies. SAE International, 2025. https://doi.org/10.4271/2025-01-7168.

Full text
Abstract:
&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Airline passenger satisfaction is important for airline operation service quality management. When airline companies carry out advertisement campaigns or plan a marketing strategy, the resources and budgets are not unlimited. Thus, an airline can only focus on improving a few factors that drive passenger satisfaction. To understand the key satisfies for the young and the old adults, respectively, we leverage five airline passenger satisfaction methods to identify the key factors that explain the airline service satisfaction of different passengers. In particular, we investigate and compare the ridge and the Lasso regularization in terms of the resulting model’s sparsity and computational efficiency. The top three important factors that influence the old’s satisfaction are departure and arrival time convenience, legroom service, and baggage handling. Our findings indicate that the young people place a higher value on entertainment, while the old adults place a higher value on usefulness and comfort. The Lasso is the most accurate model with the overall error of 9.65% to predict the young passenger’s satisfaction, while the Best Subset with BIC with the overall error of 10% is the best mode for the old adults. It’s suggested that airline companies could use the Lasso model for predicting the airline satisfaction of the young people, and use the best subset with BIC for predicting the airline satisfaction of the old adults. The study findings would help the airlines improving their state-of-the-art operations to have outstanding service.&lt;/div&gt;&lt;/div&gt;
APA, Harvard, Vancouver, ISO, and other styles
10

Tang, Fenfen, Emmanuel Hatzakis, Hilary Green, and Selina Wang. "The Analysis and Authentication of Avocado Oil using High Field- & Low Field-NMR." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/hnwv1042.

Full text
Abstract:
The popularity of avocado oil has increased among consumers due to its organoleptic properties and health-promoting effects. Avocado oil in the US market has been found to be adulterated with cheaper oils like other high-value edible oils, such as olive oil, or of poor quality. A variety of analytical methods, including chromatography and spectroscopy, have been used to evaluate the quality and purity of avocado oils. In addition, recently, high-resolution (HR) NMR has been successfully applied to determine fatty acid contents and to discriminate avocado oil from other vegetable oils. Despite their advantages, these methods suffer from several weaknesses. For example, they can be either labor-intensive and time-consuming, or expensive and requiring highly skilled experts. LF-NMR has been utilized for the analysis of many food products. It is more affordable, user-friendly, and fits well in an industrial environment, in addition to being rapid and non-destructive. However, most of the LF-NMR applications involve relaxometry instead of spectroscopy, which limits its potential in food analysis. As LF-NMR has been developed into a more powerful and versatile tool over decades, here we applied LF-NMR with chemometrics to distinguish avocado oil from other vegetable oils, including olive, canola, soybean, high-oleic (HO) safflower and HO sunflower oil, and validated the results by fatty acids and triacylglycerols profiling using GC-FID and HPLS-CAD, respectively. With the exploitation of advanced multivariate data analysis, such as Random Forest, LF-NMR provided comparable discrimination performance of different types of vegetable oils to HR-NMR, despite the challenges of high oleic oils. LF-NMR combined with PLS regression was able to efficiently and rapidly determine fatty acid contents using GC-FID as the reference method for modeling. LF-NMR was shown to have the potential for monitoring avocado oil processing and authentication in many sectors, as an alternative or complementary method to conventional food analysis instrumentations.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "OLS Regression Method"

1

Jakobsen, Tor Georg. Using ChatGPT with Stata: Regression Analysis (Part I). Instats Inc., 2025. https://doi.org/10.61700/w17ywtk8775q41567.

Full text
Abstract:
This workshop provides a comprehensive introduction to regression techniques essential for social science research. Participants will learn both theoretical and practical aspects of regression analysis, including OLS regression and dummy variables, using the statistical software Stata. It features hands-on exercises, detailed instructions, and AI-powered assistance from ChatGPT to clarify concepts and troubleshoot commands. Ideal for beginners, this course lays a solid foundation for mastering advanced statistical methods.
APA, Harvard, Vancouver, ISO, and other styles
2

Stark, Sasha, Heather Wardle, and Isabel Burdett. Examining lottery play and risk among young people in Great Britain. GREO, 2021. http://dx.doi.org/10.33684/2021.002.

Full text
Abstract:
Purpose &amp; Significance: Despite the popularity of lottery and scratchcards and some evidence of gambling problems among players, limited research focuses on the risks of lottery and scratchcard play and predictors of problems, especially among young people. The purpose of this project is to examine whether lottery and scratchcard participation is related to gambling problems among 16-24 year olds in Great Britain and whether general and mental health and gambling behaviours explain this relationship. Methodology: Samples of 16-24 year olds were pooled from the 2012, 2015, and 2016 Gambling in England and Scotland: Combined Data from the Health Survey for England and the Scottish Health Survey (n=3,454). Bivariate analyses and Firth method logistic regression were used to examine the relationship between past-year lottery and scratchcard participation and gambling problems, assessing the attenuating role of mental wellbeing, mental health disorders, self-assessed general health, and playing other games in past year. Results: There is a significant association between scratchcard play and gambling problems. The association somewhat attenuated but remained significant after taking into account wellbeing, mental health disorders, general health, and engagement in other gambling activities. Findings also show that gambling problems are further predicted by age (20-24 years), gender (male), lower wellbeing, and playing any other gambling games. Implications: Results are valuable for informing youth-focused education, decisions around the legal age for National Lottery products, and the development of safer gambling initiatives for high risk groups and behaviours, such as scratchcard play.
APA, Harvard, Vancouver, ISO, and other styles
3

Knight, R. D., B. A. Kjarsgaard, E G Potter, and A. Plourde. Uranium, thorium, and potassium analyses using pXRF spectrometry. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328973.

Full text
Abstract:
The application of portable XRF spectrometry (pXRF) for determining concentrations of uranium (U), thorium (Th) and potassium (K) was evaluated using a combination of 12 Certified Reference Materials, 17 Standard Reference Materials, and 25 rock samples collected from areas of known U occurrences or mineralization. Samples were analysed by pXRF in Soil, Mining Cu/Zn and Mining Ta/Hf modes. Resulting pXRF data were compared to published recommended values, obtained by total or near total digestion methods with ICP-MS and ICP-OES analysis. Results for pXRF show a linear relationship, for thorium, potassium, and uranium (&amp;amp;lt;5000 ppm U) as compared to the recommended concentrations. However, above 5000 ppm U, pXRF results show an exponential relationship with under reporting of pXRF concentrations compared to recommended values. Accuracy of the data can be improved by post-analysis correction using linear regression equations for potassium and thorium, and samples with &amp;amp;lt;5000 ppm uranium; an exponential correction curve is required at &amp;amp;gt;5000 ppm U. In addition, pXRF analyses of samples with high concentrations of uranium (e.g. &amp;amp;gt;1 wt.% U) significantly over-estimated potassium contents as compared to the published values, indicating interference between the two elements not calibrated by the manufacturer software.
APA, Harvard, Vancouver, ISO, and other styles
4

Wu, Bin, Lixia Guo, Kaikai Zhen, and Chao Sun. Diagnostic and prognostic value of miRNAs in hepatoblastoma: A systematic review with meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.11.0045.

Full text
Abstract:
Review question / Objective: Background and aim: Increasing evidence has revealed the valuable diagnostic and prognostic applications of dysregulated microRNAs (miRNAs) in hepatoblastoma (HB), the most common hepatic malignancy during childhood. However, these results are inconsistent and remain to be elucidated. In the present study, we aimed to systematically compile up-to-date information regarding the clinical value of miRNAs in HB. Methods: Articles concerning the diagnostic and prognostic value of single miRNAs for HB were searched from databases. The sensitivity (SEN), specificity (SPE), positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), area under the curve (AUC), and hazard ratios (HRs) were separately pooled to explore the diagnostic and prognostic performance of miRNA. Subgroup and meta-regression analyses were further carried out only in the event of heterogeneity. Results: In all, 20 studies, involving 264 HB patients and 206 healthy individuals, met the inclusion criteria in the six included literature articles. For the diagnostic analysis of miRNAs in HB, the pooled SEN and SPE were 0.76 (95% CI: 0.72–0.80) and 0.75 (95% CI: 0.70–0.80), respectively. Moreover, the pooled PLR was 2.79 (95% CI: 2.12–3.66), NLR was 0.34 (95% CI: 0.26–0.45), DOR was 10.24 (95% CI: 6.55–16.00), and AUC was 0.83, indicating that miRNAs had moderate diagnostic value in HB. For the prognostic analysis of miRNAs in HB, the abnormal expressions of miR-21, miR-34a, miR-34b, miR-34c, miR-492, miR-193, miR-222, and miR-224 in patients were confirmed to be associated with a worse prognosis. The pooled HR was 1.74 (95% CI: 1.20–2.29) for overall survival (OS) and 1.74 (95% CI: 1.31–2.18) for event-free survival (EFS), suggesting its potential as a prognostic indicator for HB. Conclusion: To the best of our knowledge, this is the first comprehensive systematic review and meta-analysis that examines the diagnostic and prognostic role of dysregulated miRNAs in HB patients. The combined meta-analysis results supported the previous individual finds that miRNAs might provide a new, noninvasive method for the diagnostic and prognostic analyses ofHB.
APA, Harvard, Vancouver, ISO, and other styles
5

Ahmed, Badrun Nessa, and Rizwana Islam. TEACHING AND LEARNING EXPERIENCE AT THE NATIONAL UNIVERSITY AFFILIATED TERTIARY COLLEGES IN BANGLADESH. Bangladesh Institute of Development Studies, 2024. http://dx.doi.org/10.57138/axvn7639.

Full text
Abstract:
The Government of Bangladesh is currently implementing the College Education Development Project (CEDP) to improve participating colleges' teaching and learning environment and strengthen the strategic planning and management capacity of National University (NU) affiliated tertiary colleges in Bangladesh. The focus of CEDP is to improve the capacity of the National University College system to plan, manage, implement, and monitor institutional programs, as well as strengthen the foundation for the next phase of development activities. CEDP promotes institution-led activities that focus on creating quality teaching-learning environments in government and non-government colleges through the availability of competitive grants. The achievement of the College Education Development Project (CEDP) is the satisfaction level of students, teachers, and employers in terms of the quality and relevance of teaching. To measure the satisfaction level of the relevant stakeholders (i.e., students, teachers, and employers), three beneficiary feedback surveys (i.e., baseline, mid-term, and endline) are planned to be conducted, among which the baseline was carried out in 2019. The Bangladesh Institute of Development Studies (BIDS) conducted the Mid-term Satisfaction Survey in May-June 2022. The mid-term survey is the second of the three planned surveys of the CEDP, measuring the mid-term satisfaction level of the stakeholders, students and teachers of National University-affiliated colleges, and employers of NU graduates. This study uses data from the Mid-term Satisfaction Survey to assess the mid-term satisfaction level of students, teachers, and employers. The study was designed using a mixed-method approach, both quantitative and qualitative, to address the objectives of this study. Data analysis has used both the baseline data collected in 2019 and the mid-term data collected in this study. Using the baseline and mid-term data, a two-round panel data was constructed at the college level. Depending on the specific indicators, the program's effect at the college level was calculated. We compare the overall satisfaction level regarding all the relevant indicators by stakeholder types, i.e., principals, teachers, and students, and observe differences among the average satisfaction levels. The overall teaching and learning environment satisfaction level is 3.81 among college principals, 2.95 among teachers, and 2.57 among students. A similar pattern is also found for other indicators except the collaboration of colleges with industries. The satisfaction level regarding the collaboration of colleges with industries is noted as the lowest for principals (1.62) and teachers (1.76), and for students, it is slightly higher (2.10 on a scale of 5). The lowest satisfaction level among students is recorded for connectivity through the internet (1.89), and the highest for teaching skills (3.92). The regression results show that for the full sample, the Difference-in-Difference (DiD) of the satisfaction scores on the quality of academic infrastructure, the quality of internet connection, and the quality of facilities for students’ soft skill improvement are statistically significant. The DiD for the other two satisfaction scores, namely, the teaching and learning environment and the degree of industry linkage, are not statistically significantly different from zero. These results show that the colleges that received Institutional Development Grants (IDGs) have made a positive and statistically significant impact on the improvement of the quality of academic infrastructure, quality of internet connection and other related facilities, and quality of facilities for students’ soft skill compared to those who did not receive this grant. However, the grant has made some changes in the teaching and learning environment and the degree of industry linkage between IDG awarded colleges and IDG non-recipient colleges. These changes are not statistically significant. The overall findings from the mid-term satisfaction survey highlighted that: (1) Institutional Development Grant (IDG) has made positive and statistically significant impact on the improvement of quality of academic infrastructure, quality of internet connection and other related facilities, and quality of facilities for students’ soft skill compared to those who did not receive this grant; (2) The grant has made some changes in the teaching and learning environment and the degree of industry linkage between IDG-awarded colleges and IDG non-recipient colleges. These changes are not significant enough to increase the satisfaction level of the students, teachers, and principals. Therefore, this study proposes these recommendations for increasing the overall satisfaction level of all stakeholders: (1) The poor level of industry collaboration has been highlighted by all types of beneficiaries. To facilitate industry collaboration, job fairs should be organised every year, preferably at the district level; (2) Introducing short course facilities can increase the job market opportunities of the NU-affiliated colleges; (3) Subject-based pedagogical training for the NU teachers is highly recommended; (4) The interrelation and collaboration between NU-affiliated colleges and universities should be increased. The colleges that are not well equipped with enough facilities can collaborate with the universities to share their equipment, such as computer labs, libraries, scientific labs, etc. This will help the less privileged colleges provide quality teaching and learning facilities to the students; (5) Forming and activating the activities of Alumni Associations in the NU-affiliated colleges; (6) There should be funds available for the renovation of old academic buildings, addition to an existing building, and upgrading labs and research facilities for teachers wherever appropriate, (7) There should be some provision of need-based funds/emergency grant that might be used or made available to the college authorities in case of sudden emergency or need (e.g., a sudden flash flood in Sylhet division)
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography