Academic literature on the topic 'Ordinary least square (OLS)'
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Journal articles on the topic "Ordinary least square (OLS)"
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Enjarwati, Tria. "IMPACT OF GOVERNMENT FISCAL SPACE AND MANPOWER TO THE GROSS DOMESTIC PRODUCTS OF INDONESIA PERIOD 1990-2015." Journal of Developing Economies 3, no. 1 (July 31, 2018): 20. http://dx.doi.org/10.20473/jde.v3i1.8562.
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To purpose of this study was to examine and analyze the effect of fiscal space and labour absorption to Indonesia Gross Domestic Product (GDP) within period 1990-2015. This study uses the least squares method or Ordinary Least Square (OLS) with time series data. Variables used in this study is the Gross Domestic Product (GDP) as the dependent variable, whereas for independent variables using the fiscal space and labour absorption. The results of regression calculations using the least squares method or Ordinary Least Square (OLS) in this study indicate that the fiscal space variable has a positive significant effect, and labour absorption variable has a positive significant effect to indonesia Gross Domestic Product (GDP).
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Yang, Hong Ying, Shuang Lei Feng, Bo Wang, Wei Sheng Wang, and Chun Liu. "Hybrid Corrected Approach for Wind Power Forecasting Based on Ordinary Least Square Method." Advanced Materials Research 846-847 (November 2013): 1392–97. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1392.
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This paper shows an application of Ordinary Least Square (OLS) in the wind power forecasting field. The OLS algorithm is applied to obtain the estimated parameter of the hybrid correction model, and then the properly structured correction model was used to correct the forecasting errors form the physical forecasting method and the statistical forecasting method. Satisfactory experimental results are obtained for day-ahead forecast by using actual wind power data.
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Weiss, Andrew A. "A Comparison of Ordinary Least Squares and Least Absolute Error Estimation." Econometric Theory 4, no. 3 (December 1988): 517–27. http://dx.doi.org/10.1017/s0266466600013438.
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In a linear-regression model with heteroscedastic errors, we consider two tests: a Hausman test comparing the ordinary least squares (OLS) and least absolute error (LAE) estimators and a test based on the signs of the errors from OLS. It turns out that these are related by the well-known equivalence between Hausman and the generalized method of moments tests. Particular cases, including homoscedasticity and asymmetry in the errors, are discussed.
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Rashid, Intan Maizura Abd. "Determinants of FDI Inflows in Agriculture Sector Using Pooled Ordinary Least Square (OLS), Pooled Generalized Least Square (GLS), Augmented Dickey-Fuller (ADF) and Philips-perron Unit Root Test." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 2560–67. http://dx.doi.org/10.37200/ijpr/v24i5/pr201955.
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M, Anila, and G. Pradeepini. "Least Square Regression for Prediction Problems in Machine Learning using R." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 960. http://dx.doi.org/10.14419/ijet.v7i3.12.17612.
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The most commonly used prediction technique is Ordinary Least Squares Regression (OLS Regression). It has been applied in many fields like statistics, finance, medicine, psychology and economics. Many people, specially Data Scientists using this technique know that it has not gone with enough training to apply it and should be checked why & when it can or can’t be applied.It’s not easy task to find or explain about why least square regression [1] is faced much criticism when trained and tried to apply it. In this paper, we mention firstly about fundamentals of linear regression and OLS regression along with that popularity of LS method, we present our analysis of difficulties & pitfalls that arise while OLS method is applied, finally some techniques for overcoming these problems.
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R, Aditya Setyawan, Mustika Hadijati, and Ni Wayan Switrayni. "Analisis Masalah Heteroskedastisitas Menggunakan Generalized Least Square dalam Analisis Regresi." EIGEN MATHEMATICS JOURNAL 1, no. 2 (December 31, 2019): 61. http://dx.doi.org/10.29303/emj.v1i2.43.
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Regression analysis is one statistical method that allows users to analyze the influence of one or more independent variables (X) on a dependent variable (Y).The most commonly used method for estimating linear regression parameters is Ordinary Least Square (OLS). But in reality, there is often a problem with heteroscedasticity, namely the variance of the error is not constant or variable for all values of the independent variable X. This results in the OLS method being less effective. To overcome this, a parameter estimation method can be used by adding weight to each parameter, namely the Generalized Least Square (GLS) method. This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis and examine the comparison of estimation results using the OLS method with the GLS method in the case of heteroscedasticity.The results show that the GLS method was able to maintain the nature of the estimator that is not biased and consistent and able to overcome the problem of heteroscedasticity, so that the GLS method is more effective than the OLS method.
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Tusilowati, Tusilowati, L. Handayani, and Rais Rais. "SIMULASI PENANGANAN PENCILAN PADA ANALISIS REGRESI MENGGUNAKAN METODE LEAST MEDIAN SQUARE (LMS)." JURNAL ILMIAH MATEMATIKA DAN TERAPAN 15, no. 2 (December 6, 2018): 238–47. http://dx.doi.org/10.22487/2540766x.2018.v15.i2.11362.
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The simulation of handling of outliers on regression analysis used the method which was commonly used to predict the parameter in regression analysis, namely Least Median Square (LMS) due to the simple calculation it had. The data with outliers would result in unbiased parameter estimate. Hence, it was necessary to draw up the robust regression to overcome the outliers. The data used were simulation data of the number of data pairs ( X,Y) by 25 and 100 respectively. The result of the simulation was divided into 5 subsets of data cluster of parameter regression prediction by Ordinary Least Square (OLS) and Least Median Square (LMS) methods. The prediction result of the parameter of each method on each subset of data cluster was tested with both method to discover the which better one. Based on the research findings, it was found that The Least Median Square (LMS) method was known better than Ordinary Least Square (OLS) method in predicting the regression parameter on the data which had up to 3% of the percentage of the outlier.
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Machado, Vieira Filho, and de Oliveira. "Forensic Speaker Verification Using Ordinary Least Squares." Sensors 19, no. 20 (October 10, 2019): 4385. http://dx.doi.org/10.3390/s19204385.
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In Brazil, the recognition of speakers for forensic purposes still relies on a subjectivity-based decision-making process through a results analysis of untrustworthy techniques. Owing to the lack of a voice database, speaker verification is currently applied to samples specifically collected for confrontation. However, speaker comparative analysis via contested discourse requires the collection of an excessive amount of voice samples for a series of individuals. Further, the recognition system must inform who is the most compatible with the contested voice from pre-selected individuals. Accordingly, this paper proposes using a combination of linear predictive coding (LPC) and ordinary least squares (OLS) as a speaker verification tool for forensic analysis. The proposed recognition technique establishes confidence and similarity upon which to base forensic reports, indicating verification of the speaker of the contested discourse. Therefore, in this paper, an accurate, quick, alternative method to help verify the speaker is contributed. After running seven different tests, this study preliminarily achieved a hit rate of 100% considering a limited dataset (Brazilian Portuguese). Furthermore, the developed method extracts a larger number of formants, which are indispensable for statistical comparisons via OLS. The proposed framework is robust at certain levels of noise, for sentences with the suppression of word changes, and with different quality or even meaningful audio time differences.
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Prananda, Dio, Idris Idris, and Dewi Zaini Putri. "DAMPAK KESEHATAN TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA." Jurnal Ecogen 1, no. 3 (February 7, 2019): 578. http://dx.doi.org/10.24036/jmpe.v1i3.5028.
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This study aims to determine and analyze the impact of life expentacy, fertilitiy rates, morbidity rates, and investment on economic growth in Indonesia. This type of research is associative descriptive research, where the data used was secondary data from 1985 to 2015 obtained from related institutions, which are analyzed using the Ordinary Least Square (OLS) method. The findings of this study indicate that life expectancy, fertility rates, morbidity rates, and investment have a significant effect on economic growth in Indonesia. Keywords: life expectancy, fertility rates, morbidity rates, investment, and Ordinary Least Square (OLS)
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Niftiyev, Ibrahim. "Dutch Disease Effects in the Azerbaijan Economy: Results of Multivariate Linear Ordinary Least Squares (OLS) Estimations." Higher School of Economics Economic Journal 25, no. 2 (2021): 309–46. http://dx.doi.org/10.17323/1813-8691-2021-25-2-309-346.
Full textDissertations / Theses on the topic "Ordinary least square (OLS)"
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Li, Yang. "An Empirical Analysis of Family Cost of Children : A Comparison of Ordinary Least Square Regression and Quantile Regression." Thesis, Uppsala University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126660.
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Quantile regression have its advantage properties comparing to the OLS model regression which are full measurement of the effects of a covariate on response, robustness and Equivariance property. In this paper, I use a survey data in Belgium and apply a linear model to see the advantage properites of quantile regression. And I use a quantile regression model with the raw data to analyze the different cost of family on different numbers of children and apply a Wald test. The result shows that for most of the family types and living standard, from the lower quantile to the upper quantile the family cost on children increases along with the increasing number of children and the cost of each child is the same. And we found a common behavior that the cost of the second child is significantly more than the cost of the first child for a nonworking type of family and all living standard families, at the upper quantile (from 0.75 quantile to 0.9 quantile) of the conditional distribution.
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Amir, Daban. "Kenyas export till samtliga handelspartner - påverkande faktorer? : En empirisk analys på makronivå med tillämpning av gravitationsmodellen." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-35622.
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Tidigare studier visar att ökad handel spelar en tydlig roll för ett lands ekonomiska tillväxt. Genom att träda in på den globala marknaden öppnas många möjligheter för ökad handel och nya arbetstillfällen. Utrikeshandeln är betydelsefull för små öppna ekonomier som till exempel Kenya och bör utgöra en stor del av landets BNP. I och med detta är det viktigt att studera vilka faktorer som påverkar ett lands utrikeshandel. Syftet med uppsatsen är att undersöka vilka faktorer som påverkar Kenyas export. Analysen visar att handelspartnernas BNP har en betydande påverkan på Kenyas export. Det geografiska avståndet har en negativ påverkan på Kenyas utrikeshandel. De regionala handelsavtalen har som förväntat en positiv påverkan på exporten.
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Nevarez, Martinez Deyanira. "Identifying Housing Patterns in Pima County, Arizona Using the DEYA Affordability Index and Geospatial Analysis." The University of Arizona, 2015. http://hdl.handle.net/10150/576108.
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When the Fair Housing Act of 1968 was passed 47 years ago, the United States was in the midst of the civil rights movement and fair housing was identified as a pillar of equality. While, progress has been made, there is much work that needs to be done in order to achieve integration. As a country, the United States is a highly segregated country. It is important to understand the factors that contribute to this and it is important to understand the relationships that exists between them in order to attempt to solve the problem. While the legal barriers to integration have been lifted choices continue to be limited to families of color that lack the resources to live in desirable neighborhoods. The ultimate goal of this study is to examine the relationship between the impact of individual indicators and housing patterns in the greater Tucson/Pima county region. An affordability index, the DEYA index, was created to determine where affordability is at its highest. The index includes different weights for foreclosure, Pima County spending on affordable housing, the existence of Pima County general obligations bond affordable housing projects, land value and inclusion in the community land trust. Once this was determined a regression analysis was used to determine the relationship between affordability and individual factors that may be affecting integration. The indicators used were broken down into 3 categories: the categories were education, housing and neighborhoods and employment and economic health.
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Forslind, Fanni. "The Effect of Immigration on Income Distribution : A Comparative Study of Ordinary Least Squares and Beta Regression." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-433098.
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The purpose of this study is to estimate the relationship between income inequality and immigration in Sweden. To do so, data from the data base Kolada with observations from all 290 municipalities in Sweden is used. As a proxy for income distribution the Gini coefficient is used and as a proxy for immigration the share of foreign born of working age is used. The model also controls for income tax, education level and unemployment level. The dependent variable the Gini coefficient is bounded by a unit interval and it is therefore not possible to simply run a linear regression. Such a model could potentially predict outside the interval. To properly estimate the relationship two approaches are made. Firstly a model is estimated with Ordinary Least Squares (OLS) after the dependent variable is transformed on to the real line through log-odds. Then a model is estimated using beta regression. The study concludes that there is a statistically significant positive correlation between income inequality and immigration in Sweden. The OLS estimated model shows that a 1 unit increase in immigration, on average increases the log-odds of 0.28336 units, ceteris paribus. Beta regression provides perhaps more intuitive results. If immigration increases with 1% the income inequality increases with on average 0.1046%, ceteris paribus. Because of the easier interpretation, among other things, beta regression is determined to be a better estimation method in this study.
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Kronenberg, Kai. "ADVERTISING EFFECTIVENESS ON INTERNATIONAL TOURISM DEMAND IN ÅRE – AN ECONOMETRIC ANALYSIS." Thesis, Mittuniversitetet, Avdelningen för turismvetenskap och geografi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-19249.
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The objective of this study is to estimate advertising effects on international tourismdemand for the leading Swedish winter destination, Åre. The increasing share of foreigninbound tourists in this destination region creates a strong interest by shareholders toidentify the factors responsible for this trend. According to traditional micro-economictheory, economic factors, such as income and price, are considered as main determinantsfor tourism demand (Song and Witt 2000). However, according to advertising theories(Comanor and Wilson, 1974) and previous tourism research (Bhagwat and Debruine, 2008;Divisekera and Kulendran, 2006), this study additionally focuses on the brand awarenessof Åre as perceived by international tourists. More concretely, advertising theoriesdistinguish between the brand and the information function of advertising (Nelson, 1974).The former function follows the idea that advertising increases the level of productdifferentiation to build up a base of loyal customers. By contrast, the information functionimplies that advertising primarily provides information about products in order to increasethe market transparency. Accordingly, in order to estimate the impact of advertisingexpenditures for off- and online channels as well as promotional activities, furtherexplanatory variables, e.g. mega events, are considered in this study (Salman, 2003; Songet al., 2010). By applying ordinary least square (OLS) methods, demand elasticitycoefficients are estimated for each of the sending countries Norway, Finland, Russia,Denmark and the UK. Results show that advertising is the main significant driver oftourism demand from the UK, Russia and Finland, while a comparably weak advertisingleverage can be shown for Denmark and Norway. Interestingly, in contrast to microeconomictheories tested in previous research, income and tourism price levels reveal asbeing less significant drivers for demand in all analysed tourism markets. In turn, theresults provide evidence that the increased usage of online channels most significantlyaffects consumers’ buying behaviour. Finally, with respect to brand image perception,results reveal that the destination of Åre is perceived as a brand by tourists from Denmark.Moreover, for customers from the countries Norway and Finland, Åre indicates a weakbrand perception, while tourists from Russia and the UK don’t perceive Åre as a brand atall. The results gained by this research conducted at the level of the tourism destinationprovide useful hints about the factors influencing travel behaviour of tourists from maininternational markets. The study supports destination managers to appropriately adjustmarketing campaigns according to the predominant level of brand perception in respectivesending countries.KK-Foundation project ‘Engineering the Knowledge Destination’ (no. 20100260; Stockholm, Sweden).
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Peres, Ariádine. "Restrições ao crédito e o uso dos recursos financeiros nas empresas brasileiras." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/98311.
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Este estudo têm como objetivo identificar qual o comportamento de empresas brasileiras de capital aberto com relação à aplicação dos recursos financeiros de seus fluxos de caixa (recursos internos) em investimentos e não investimentos (em fins que não se configuram como um projeto real de investimento) no curto e longo prazo e mostrar como essa questão está relacionado com o grau de restrições financeiras enfrentado pelas empresas. Para alcançar esse objetivo foram estimadas quatro regressões pelo método OLS (Ordinary Least Square / Mínimos Quadrados Ordinários), cada uma delas com uma das variáveis resposta correspondentes aos principais usos de caixa, ou seja, retenção de caixa, investimentos, dividendos e redução do financiamento externo e com as variáveis explicativas dadas pelo fluxo de caixa nos períodos t, t-1 e t-2 e algumas variáveis de controle específicas da firma. Os resultados sugerem que empresas brasileiras restritas e irrestritas se comportam de forma diferente ao receberem um choque positivo em seus fluxos de caixa e que o comportamento das mesmas também difere no curto e no longo prazo. Empresas restritas e irrestritas ao receberem um choque positivo em seus fluxos de caixa, retêm caixa no período contemporâneo e alocam tais recursos intertemporalmente. Empresas restritas investem mais no curto prazo enquanto as irrestritas investem mais no longo prazo. No curto prazo, empresas irrestritas distribuem mais dividendos do que empresas irrestritas e no longo prazo, os coeficientes dos fluxos de caixa não são significativos para nenhum dos grupos. No curto prazo empresas irrestritas reduzem o financiamento externo, enquanto empresas restritas levantam mais financiamentos externos e no longo prazo, esse comportamento se inverte. Dessa forma, fica clara a importância de se considerar o longo prazo bem como as restrições financeiras enfrentadas pelas empresas.This study aim to identify what is the behaviour of Brazilian public companies regarding the use of financial resources of cash flows (internal resources) in investments and not investments (for purposes that are not configured as a real investment project) in the short and long term and show how this is related to the degree of financial constraints faced by firms. To achieve this aim, four regressions were estimated by OLS ( Ordinary Least Square), each with one of the response variables corresponding to the main uses of cash, ie , cash holding, investments, dividends and external finance reduction and the explanatory variables given by the cash flow in periods t , t - 1 and t - 2 and some control variables specific of the firm. The results suggest that restricted and unrestricted Brazilian companies behave differently when they receive a positive shock on cash flows and their behavior also differs in the short and long term. When constrained and unconstrained firms receive a positive impact on cash flows, they retain cash in the contemporary period and intertemporally allocate such resources. Constrained firms invest more in the short term while the unrestricted invest more in the long run. In the short term, unconstrained firms distribute more dividends than unconstrained firms and in the long run, the coefficients of cash flows are not significant for either groups. In the short term unconstrained firms reduce external finance, while constrained firms raise more external finance and in the long term, this behavior is reversed. Thus, it is clear that it matters to consider the long term as well as financial constraints faced by firms.
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Can, Mutan Oya. "Comparison Of Regression Techniques Via Monte Carlo Simulation." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605175/index.pdf.
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The ordinary least squares (OLS) is one of the most widely used methods for modelling the functional relationship between variables. However, this estimation procedure counts on some assumptions and the violation of these assumptions may lead to nonrobust estimates. In this study, the simple linear regression model is investigated for conditions in which the distribution of the error terms is Generalised Logistic. Some robust and nonparametric methods such as modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares, least trimmed squares (LTS), Theil and weighted Theil are compared via computer simulation. In order to evaluate the estimator performance, mean, variance, bias, mean square error (MSE) and relative mean square error (RMSE) are computed.
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Santos, Levi Alã Neves dos. "Mínimos quadrados ordinários (MQO) na produção científica brasileira: a interdisciplinaridade entre a econometria e as metrias da informação (bibliometria, informetria e cientometria)." Universidade Federal da Bahia, 2017. http://repositorio.ufba.br/ri/handle/ri/25329.
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Analisa a produção científica brasileira (artigos nacionais, artigos internacionais, anais de eventos e livros) através dos Mínimos Quadrados Ordinários (MQO). Para tanto, discorre sobre o percurso histórico e de aplicação das metrias que a Ciência da Informação (CI) vem construindo, desde a mais primordial de todas, a bibliometria, oriunda da biblioteconomia, passando pelas visões modernas como a cienciometria até a informetria. Explica como a econometria constrói o seu modelo de análise, que é utilizado para pesquisas na economia e, ao mesmo tempo, reflete como esse método pode ser trazido para as metrias da informação. Explica e expõe o método de estimação por MQO para a análise de regressão, que é a proposta desta tese. Pesquisa aplicada descritiva com abordagem quantitativa com procedimentos baseados no tipo de pesquisa estudo de caso do levantamento de dados a partir do Portal do Plano Tabular do CNPq do ano de 2010. Os critérios para delineamento da pesquisa foram aprofundados, na revisão de literatura, em referências tanto da área da CI quanto da bibliometria, estatística e econometria. Este estudo, metodologicamente, conta com a abordagem conceitual da bibliometria e da CI em busca de teorias aplicáveis aos estudos em MQO e a aplicação empírica do MQO se aproxima da concepção econométrica. A tese conclui que a utilização de técnicas de análises das funções de regressão construída por meio de MQO possibilita a criação de um modelo de previsão da produção científica brasileira. Esse modelo é construído a partir da correlação e determinação detectada entre o número de doutores e a produção científica destes em cada estado do Brasil. Com a aplicação de estratégias econométricas (índice de correlação, índice de determinação, forma funcional de curva de regressão e cálculo dos parâmetros da função por MQO), foi possível construir um modelo de previsão.
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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.
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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.
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Deng, Kefu. "The value and validity of software effort estimation models built from a multiple organization data set." Click here to access this resource online, 2008. http://hdl.handle.net/10292/473.
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The objective of this research is to empirically assess the value and validity of a multi-organization data set in the building of prediction models for several ‘local’ software organizations; that is, smaller organizations that might have a few project records but that are interested in improving their ability to accurately predict software project effort. Evidence to date in the research literature is mixed, due not to problems with the underlying research ideas but with limitations in the analytical processes employed: • the majority of previous studies have used only a single organization as the ‘local’ sample, introducing the potential for bias • the degree to which the conclusions of these studies might apply more generally is unable to be determined because of a lack of transparency in the data analysis processes used. It is the aim of this research to provide a more robust and visible test of the utility of the largest multi-organization data set currently available – that from the ISBSG – in terms of enabling smaller-scale organizations to build relevant and accurate models for project-level effort prediction. Stepwise regression is employed to enable the construction of ‘local’, ‘global’ and ‘refined global’ models of effort that are then validated against actual project data from eight organizations. The results indicate that local data, that is, data collected for a single organization, is almost always more effective as a basis for the construction of a predictive model than data sourced from a global repository. That said, the accuracy of the models produced from the global data set, while worse than that achieved with local data, may be sufficiently accurate in the absence of reliable local data – an issue that could be investigated in future research. The study concludes with recommendations for both software engineering practice – in setting out a more dynamic scenario for the management of software development – and research – in terms of implications for the collection and analysis of software engineering data.
Books on the topic "Ordinary least square (OLS)"
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Walsh, Bruce, and Michael Lynch. Analysis of Short-term Selection Experiments: 1. Least-squares Approaches. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0018.
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This chapter examines short-term (a few generations) selection response in the mean of a trait. Traditionally, such experiments are analyzed using least-squares (LS) approaches. While ordinary LS (OLS) is often used, genetic drift causes the residual to be both correlated and heteroscedastic, resulting in the sampling variances given by OLS being too small. This chapter details the appropriate general LS (GLS) approaches to properly account for this residual error structure. It also reviews some of the common features observed in short-term selection experiments and examines experimental designs, such as the use of a control population versus a divergence-selection approach. It concludes by discussing another linear model used mainly by plant breeders, generation-means analysis (GMA), wherein remnant seed for several generations of response are crossed and then grown in a common garden. Such an analysis can provide insight into the genetic nature of any response.
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Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.
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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.
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Cooray, Arusha, Krishna Chaitanya Vadlamannati, and Indra de Soysa. Do bigger health budgets cushion pandemics? An empirical test of COVID-19 deaths across the world. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/922-8.
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How has government healthcare spending prepared countries for tackling the COVID-19 pandemic? Arguably, spending is the primary policy tool of governments in providing effective health. We argue that the effectiveness of spending in reducing COVID deaths is conditional on the existence of healthcare equity and lower political corruption, because the health sector is particularly susceptible to political spending. Our results, obtained using ordinary least squares (OLS) and two-stage least squares (2SLS) estimation, suggest that higher spending targeted at reducing inequitable access to health has reduced COVID deaths. Consistent with the findings of others, our results indirectly suggest that health spending is necessary, but not sufficient unless accompanied by building resilience against the spread of deadly disease. Equitable health systems ease the effects of COVID presumably because they allow states to reach and treat people. Spending aimed at increasing health system capacity by increasing access thus seems a sound strategy for fighting the spread of disease, ultimately benefiting us all.
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Kritz, Mary M., and Douglas T. Gurak. International Student Mobility. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815273.003.0011.
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This chapter examines the role that sending country structural factors play in influencing the proportion of tertiary students studying abroad. It examines how outbound mobility ratio (OMR) responds to sending county supply and demand for tertiary education, population size, per capital GDP, development, education expenditures, and other factors. In all Ordinary Least Squares (OLS) and fixed-effect model specifications, the OMR had a negative relationship to tertiary supply. While countries with larger populations send more students abroad, they have smaller OMRs. Fixed-effects models also showed that changes in tertiary supply and the percentage of GDP spent on tertiary education were negatively related to OMRs. The chapter reviews government scholarship programmes sponsored by Global South countries and the practices they pursue to encourage student return and strengthen tertiary capacity in science, technology, engineering, and mathematics (STEM). These programmes in developing countries in Africa, Asia, and Latin America are changing international student flows.
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Brazier, John, Julie Ratcliffe, Joshua A. Salomon, and Aki Tsuchiya. Modelling health state valuation data. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198725923.003.0005.
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This chapter examines the technical issues in modelling health state valuation data. Most measures of health define too many states to directly value all of them (e.g. SF-6D defines 18,000 health states). The solution has been to value a subset and by using modelling to predict the values of all states. This chapter reviews two approaches to modelling: one using multiattribute utility theory to determine health values given an assumed functional form; and the other is using statistical modelling of SF-6D preference data that are skewed, bimodal, and clustered by respondents. This chapter examines the selection of health states for valuation, data preparation, model specification, and techniques for modelling the data starting with ordinary least squares (OLS) and moving on to more complex techniques including Bayesian non-parametric and semi-parametric approaches, and a hybrid approach that combines cardinal preference data with the results of paired data from a discrete choice experiment.
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Li, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.
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This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R in research projects. Specifically, it teaches students how to import, inspect, and manage data; understand the logic of statistical inference; visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots; and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. Third, it teaches students how to replicate the findings in published journal articles and diagnose model assumption violations. The principle behind this book is to teach students to learn as little R as possible but to do as much reproducible, substance-driven data analysis at the beginner or intermediate level as possible. The minimalist approach dramatically reduces the learning cost but still proves adequate information for meeting the practical research needs of senior undergraduate and beginning graduate students. Having completed this book, students can use R and statistical analysis to answer questions regarding some substantively interesting continuous outcome variable in a cross-sectional design.
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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.
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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.
Book chapters on the topic "Ordinary least square (OLS)"
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Zdaniuk, Bozena. "Ordinary Least-Squares (OLS) Model." In Encyclopedia of Quality of Life and Well-Being Research, 4515–17. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2008.
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Larbi, Isaac, Clement Nyamekye, Fabien C. C. Hountondji, Gloria C. Okafor, and Peter Rock Ebo Odoom. "Climate Change Impact on Climate Extremes and Adaptation Strategies in the Vea Catchment, Ghana." In African Handbook of Climate Change Adaptation, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42091-8_95-1.
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AbstractClimate change impact on rainfall and temperature extreme indices in the Vea catchment was analyzed using observation and an ensemble mean of bias-corrected regional climate models datasets for Representative Concentration Pathway (RCP 4.5) scenario. Rainfall extreme indices such as annual total wet-day precipitation (PRCPTOT), extremely wet days (R99P), consecutive wet days (CWD), consecutive dry days (CDD), and temperature indices such as warmest day (TXx) and warmest night (TNx) from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI) were computed for both the historical (1986–2016) and future (2020–2049) period using the RClimdex. The parametric ordinary least square (OLS) regression approach was used to detect trends in the time series of climate change and extreme indices. The results show an increase in mean annual temperature at the rate of 0.02 °C/year and a variability in rainfall at the catchment, under RCP 4.5 scenario. The warmest day and warmest night were projected to increase by 0.8 °C and 0.3 °C, respectively, in the future relative to the historical period. The intensity (e.g., R99p) and frequency (e.g., CDD) of extreme rainfall indices were projected to increase by 29 mm and 26 days, respectively, in the future. This is an indication of the vulnerability of the catchment to the risk of climate disasters (e.g., floods and drought). Adaptation strategies such as early warning systems, availability of climate information, and flood control measures are recommended to reduce the vulnerability of the people to the risk of the projected impact of climate extreme in the future.
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Larbi, Isaac, Clement Nyamekye, Fabien C. C. Hountondji, Gloria C. Okafor, and Peter Rock Ebo Odoom. "Climate Change Impact on Climate Extremes and Adaptation Strategies in the Vea Catchment, Ghana." In African Handbook of Climate Change Adaptation, 1937–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_95.
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AbstractClimate change impact on rainfall and temperature extreme indices in the Vea catchment was analyzed using observation and an ensemble mean of bias-corrected regional climate models datasets for Representative Concentration Pathway (RCP 4.5) scenario. Rainfall extreme indices such as annual total wet-day precipitation (PRCPTOT), extremely wet days (R99P), consecutive wet days (CWD), consecutive dry days (CDD), and temperature indices such as warmest day (TXx) and warmest night (TNx) from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI) were computed for both the historical (1986–2016) and future (2020–2049) period using the RClimdex. The parametric ordinary least square (OLS) regression approach was used to detect trends in the time series of climate change and extreme indices. The results show an increase in mean annual temperature at the rate of 0.02 °C/year and a variability in rainfall at the catchment, under RCP 4.5 scenario. The warmest day and warmest night were projected to increase by 0.8 °C and 0.3 °C, respectively, in the future relative to the historical period. The intensity (e.g., R99p) and frequency (e.g., CDD) of extreme rainfall indices were projected to increase by 29 mm and 26 days, respectively, in the future. This is an indication of the vulnerability of the catchment to the risk of climate disasters (e.g., floods and drought). Adaptation strategies such as early warning systems, availability of climate information, and flood control measures are recommended to reduce the vulnerability of the people to the risk of the projected impact of climate extreme in the future.
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Danlami, Abubakar Hamid, and Shri Dewi Applanaidu. "Sustaining a Cleaner Environment by Curbing Down Biomass Energy Consumption." In African Handbook of Climate Change Adaptation, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42091-8_211-1.
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AbstractEnvironmental degradation, soil erosion, and desertification are some of the consequences of high rate of traditional biomass fuel use by households in developing countries. The critical issues to raise here are how can these households be encouraged to change their energy consumption behavior? What are the factors that cause the rampant use of biomass fuel in developing countries? How and to what extent can these factors be manipulated so that households in developing countries are encouraged to adopt clean energy fuel an alternative to the most widely used biomass fuel? Therefore, this chapter tries to find answer to the above questions raised, by carrying out an in depth analysis of households’ use of biomass fuel in developing countries using Bauchi State, Nigeria, as the case study. Cluster area sampling technique was utilized to generate the various responses, where a total number of 539 respondents were analyzed. The study estimated ordered logit model to analyze the factors that influence the movement of households along the energy ladder from nonclean energy to the cleaner energy. Furthermore, Ordinary Least Squares (OLS) model was estimated to analyze the impacts of socio-economic, residential, and environmental factors on biomass energy consumption. It was found that age of the household head and his level of education, income, living in urban areas, home ownership, and hours of electricity supply have positive and significant impact on household energy switching from traditional biomass energy use to the cleaner energy. Therefore, policies that will enhance household income and the increase in the availability of cheap cleaner energy will encourage households switching to cleaner energy sources thereby reducing the level of environmental pollution in the study area.
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Danlami, Abubakar Hamid, and Shri Dewi Applanaidu. "Sustaining a Cleaner Environment by Curbing Down Biomass Energy Consumption." In African Handbook of Climate Change Adaptation, 1423–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_211.
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AbstractEnvironmental degradation, soil erosion, and desertification are some of the consequences of high rate of traditional biomass fuel use by households in developing countries. The critical issues to raise here are how can these households be encouraged to change their energy consumption behavior? What are the factors that cause the rampant use of biomass fuel in developing countries? How and to what extent can these factors be manipulated so that households in developing countries are encouraged to adopt clean energy fuel an alternative to the most widely used biomass fuel? Therefore, this chapter tries to find answer to the above questions raised, by carrying out an in depth analysis of households’ use of biomass fuel in developing countries using Bauchi State, Nigeria, as the case study. Cluster area sampling technique was utilized to generate the various responses, where a total number of 539 respondents were analyzed. The study estimated ordered logit model to analyze the factors that influence the movement of households along the energy ladder from nonclean energy to the cleaner energy. Furthermore, Ordinary Least Squares (OLS) model was estimated to analyze the impacts of socio-economic, residential, and environmental factors on biomass energy consumption. It was found that age of the household head and his level of education, income, living in urban areas, home ownership, and hours of electricity supply have positive and significant impact on household energy switching from traditional biomass energy use to the cleaner energy. Therefore, policies that will enhance household income and the increase in the availability of cheap cleaner energy will encourage households switching to cleaner energy sources thereby reducing the level of environmental pollution in the study area.
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Ezell, Michael E., and Kenneth C. Land. "Ordinary Least Squares (OLS)." In Encyclopedia of Social Measurement, 943–50. Elsevier, 2005. http://dx.doi.org/10.1016/b0-12-369398-5/00171-7.
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Osterlind, Steven J. "At Least Squares." In The Error of Truth, 101–18. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198831600.003.0007.
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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.
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Alves, Sandra. "CEO Duality and Firm Performance." In Conceptual and Theoretical Approaches to Corporate Social Responsibility, Entrepreneurial Orientation, and Financial Performance, 227–46. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2128-1.ch012.
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Two divergent theories emerge from the literature on CEO duality. The agency theory advocates that a dual CEO negatively affects corporate performance, because it compromises the monitoring and control of the CEO, whilst the stewardship theory suggests the contrary effect due to the unity of command it presents. For a sample of 26 non-financial listed Portuguese firms from 2002 to 2016, this study draws on agency and stewardship theories to evaluate the relationship between CEO duality and firm performance, proxied by Tobin's Q. Using ordinary least square (OLS) and two stage least squares (2SLS) techniques to control potential problems simultaneity between CEO duality and firm performance, the author finds a negative relationship between CEO duality and Tobin's Q. This suggests that investors perceive no value in having a concentration of power with a dual leadership structure. Therefore, this study recommends that the positions of chairman and CEO should be separated for listed Portuguese firms.
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Halperin, Sandra, and Oliver Heath. "16. Patterns of Association: Bivariate Analysis." In Political Research, 422–50. Oxford University Press, 2020. http://dx.doi.org/10.1093/hepl/9780198820628.003.0016.
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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.
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Li, Quan. "Regression Diagnostics and Sensitivity Analysis." In Using R for Data Analysis in Social Sciences, 206–62. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.003.0006.
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This chapter shows why the Gauss-Markov assumptions are important in ordinary least squares (OLS) regression, how to diagnose assumption violations in OLS regression, and how to conduct sensitivity analysis and correct for some assumption violations. The issues covered include linearity and model specification, perfect and high multicollinearity, constant error variance, independence of error term observations, outlier and influential observations, and normality test. A mastery of materials in this chapter is necessary for systematic data analysis of a continuous outcome variable in a cross-sectional design
Conference papers on the topic "Ordinary least square (OLS)"
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Keshmiri, Soheil, and Shahram Payandeh. "An Optimal Orthogonal Recharging Route Planner: A Multi-Robots, Multi-Rendezvous Recharging Scheme." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-38914.
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The issue of recharging a group of worker robots in their working environment has been tackled. For this purpose, a special purpose tanker robot has been devised with a planner, capable of generating recharging route that minimizes the cumulative sum of orthogonal distances of worker robots from their current locations to their corresponding recharging rendezvous locations along the recharging route (hence the term Orthogonal Recharging Route or ORR Planner). It has been proven that the ORR planner will result into a recharging route that minimizes the total worker robots distance traversal for recharging, irrespective of location of charging station/tanker. Experiments have been conducted to examine the practicality of the technique in contrast with scenarios of fixed charging station, as well as results of previous work based on Ordinary and Weighted Least Squares (OLS and WLS respectively) regressions. Results obtained in simulations are provided for illustrative comparison purpose among the different techniques.
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Brown, Jeffrey M., Emily B. Carper, Joseph A. Beck, and Alexander A. Kaszynski. "Emulation of As-Manufactured Transonic Rotor Airfoil Modal Behavior and the Significance of Frequency Veering." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91670.
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Abstract This effort investigates the ability to efficiently emulate the modal characteristics of a transonic compressor rotor as a function of measured geometric variations. The transonic rotor is measured with a structured blue light scanner that provides a dense geometric representation of the as-manufactured part. Principal component analysis (PCA) is applied to create a reduced order, orthogonal basis of parameters used as independent emulator parameters. Ordinary least squares (OLS) and Gaussian stochastic process (GSP) regression models are employed as emulators for airfoil frequency and mode shape across the first twenty resonant modes. While many modes are emulated with high accuracy, some challenge conventional emulation. It is shown that geometric variations cause significant variation in modal behavior for closely spaced frequencies. This work identifies that as-manufactured geometry deviations can lead to frequency veering and associated chaotic modal behavior. Effective emulation of frequncy and mode shape is demonstrated, but the physics-based understanding of as-manufactured geometry frequeny veering defines the way for future improvements in emulation accuracy and computational requirements.
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Birškytė, Liucija. "The impact of government debt on public finance stability in Lithuania." In Contemporary Issues in Business, Management and Economics Engineering. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/cibmee.2019.030.
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Purpose – though the global financial crisis is well behind us several EU countries continue to experience problems with public finance stability and need to cope with the consequences of high public debt. The purpose of the article is to find the relationship between government debt and of public finance stability in Lithuania. Research methodology – in order to achieve the aim of the article Financial Stability Index (FSI) for Lithuania has been created. It is based on theory and previous research. To find the determinants of FSI the multiple regression analysis model was specified and tested using Ordinary Least Squares (OLS). Findings – the results of multiple regression analysis indicate the government debt has a statistically significant impact on FSI, ceteris paribus. Other findings of the research show that profit or loss of the non-financial sector, foreign trade balance as well as a foreign direct investment are significant determinants of public finance stability. Research limitations – one of the limitations of this research is the small sample size that has an impact on the validity and generalizability of the results. Having a longer time-series data or panel data for more countries would improve the robustness and applicability of research results. Practical implications – the results of the research provide guidance to policymakers in the public finance area. Originality/Value − this paper contributes to the scarce literature on government debt and other determinants of financial stability in Lithuania
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Blazheska, Angela, and Igor Ivanovski. "QUANTITATIVE ANALYSIS OF THE OPERATIONAL PERFORMANCE OF THE SELECTED NON-LIFE INSURANCE COMPANIES IN THE INSURANCE MARKET OF REPUBLIC OF NORTH MACEDONIA." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0030.
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The aim of this paper is to analyze the operational performance of the 5 dominant companies on the non-life insurance market in Republic of North Macedonia. As input in the analysis, the quarterly data for the 2009-2019 period is included for the key indicators such as the gross written premium (GWP), the gross liquidated damages, the number of insurance contracts and settled claims as well as the operating costs of the companies. These variables are observed through OLS (Ordinary Least Squares) regression analysis and VAR (Vector Autoregressive) model which demonstrates the dependence of the GWP to the rest of the indicators and their responsiveness to shocks. The findings of the study offers valuable insight and opportunities for short term recommendations and further exploration. The companies are missing the sustainability and viability of their management models and define the “shortcism” as more important for the market and operational performance. In these regard, the business models must introduce contemporary and comprehensive tools and techniques, dominantly based on IT solutions and adequate HCM changes, for risk identification and actions for lowering the claims ratio and their volume. Moreover, all the companies should evaluate the elements of the operating costs, both for sales as well as of the administrative ones, as critical components for the companies’ profitability. Very importantly, significant changes at the ALM models and higher rate of returns should inevitably create additional advantage for dynamic and sustainable models for consumer acquisition and new products and services development.
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Makrevska Disoska, Elena, and Katerina Shapkova Kocevska. "THE IMPACT OF HUMAN FREEDOMS ON ECONOMIC GROWTH." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0016.
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The impact of formal institutions, including rule of law, human rights, and civil liberties on economic growth has been in the focus of the latest research agenda of the new institutional economics due to the current pandemic of the Corona-19 virus. Some limitations are necessary to be imposed to address a pandemic, but this is a real risk of lasting deterioration in basic human freedoms. Increased surveillance, restrictions on free expression and information, and limits on public participation are becoming increasingly common. The present fear is that the authorities worldwide are using the current situation to repress human rights for political purposes. This paper aims to explore the effect of the overall institutional environment, understood as the concept of human freedom, on economic prosperity in different jurisdictions around the world. Human freedom is a general term for personal, civil, and economic freedom and therefore the interconnection with economic growth can be seen in both directions. In our analysis, we use the Human Freedom Index published by the Fraser Institute as a proxy for human freedom. Here, human freedom is understood as the absence of coercive constraint. The index is calculated based on 79 distinct indicators representing different aspects of personal and economic freedom. This analysis seeks to answer several questions. First, we are interested in examining whether there is empirical evidence about the causality between human freedoms and economic growth. Second, we are interested in whether human freedom has a positive impact on growth rates. And third, we are interested in examining the influence of other determinants on economic growth. To test the causality between human freedom and economic growth, we have conducted a Granger causality analysis. The empirical strategy for identification of the possible influence of human freedom to growth rates includes the development of ordinary least squares (OLS) panel regression models for selected economies of the world, or around 174 cross-section units (countries) in the period between 2008 and 2017.
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Tang, Karen, Elijah Otis, Alexandra Loverock, Cameron Wild, and Igor Yakovenko. "The Role of Motives in Understanding the Link Between Personality and Cannabis Misuse." In 2020 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2021. http://dx.doi.org/10.26828/cannabis.2021.01.000.19.
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Background and aim: A robust association exists between substance use and personality, with personality risk factors representing phenotypes of vulnerability to substance misuse. As such, personality risk factors may be valuable constructs for understanding specific motivations for substance misuse. Given the loosening of restrictions on cannabis worldwide, research focusing on understanding cannabis use in young adults, a particularly at-risk population, remains a vital area of research. The existing data provides extensive support for the mediating role of coping motives on personality risk factors and problematic cannabis use; however, the role of other types of motives has remained largely unexplored. Our study examined the mediating role of cannabis use motives between personality and cannabis misuse among university students. We also explored the predictive value of personality phenotypes for cannabis use problems. Research question and hypothesis: Do motivations for cannabis use mediate or explain the relationship between personality type and cannabis use problem severity? Hypothesis 1: sensation-seeking (SS) and impulsivity (IMP), but not anxiety sensitivity and hopelessness, will be associated with greater cannabis use problem severity. Hypothesis 2: motives for use (i.e., coping, conformity, social, enhancement, expansion) will mediate the association between personality risk and cannabis use problem severity. Method: A survey was administered to 1073 undergraduate students. We examined whether motivations for use (mediator variable) explained the relationship between personality (predictor variable) and cannabis use disorder severity (outcome variable) using an ordinary least-squares (OLS) based mediation analysis. Results: As hypothesized, SS and IMP predicted greater cannabis use problems. A noteworthy finding was that conformity motives were a significant mediator between SS and IMP and cannabis use, whereby higher levels of SS/IMP led to greater endorsement of conformity motives, which in turn led to lower cannabis misuse. Enhancement motives were also a significant mediator between IMP and cannabis use. Expansion motives were a significant mediator between SS and cannabis use. Conclusion: Understanding reasons for use (i.e., motives) allows us to identify those at greatest risk for cannabis misuse. Findings from this study may help explain the underlying mechanisms by which personality risk factors lead to cannabis use disorder in young adults. A greater understanding of these personality phenotypes may have implications for the development of personality-specific interventions for cannabis use.
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Harun, Suriyati, Yasmin Yahya, Nurashikin Saaludin, and Wan Suriyani Che Wan Ahmad. "Comparison of ordinary least square and mixed-effect regression models for estimation of tree diameter increment." In IMCOM '15: The 9th International Conference on Ubiquitous Information Management and Communication. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2701126.2701167.
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Rozali, Mohd F., Ihsan M. Yassin, Azlee Zabidi, Wahidah Mansor, and Nooritawati Md Tahir. "Application of Orthogonal Least Square (OLS) for selection of Mel Frequency Cepstrum Coefficients for classification of spoken letters using MLP classifier." In its Applications (CSPA). IEEE, 2011. http://dx.doi.org/10.1109/cspa.2011.5759923.
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Yining, Deng, Niu Mandi, and Zhang Qingyu. "The Empirical Analysis of the Influence of Urban Innovation Ability on the Economic growth Based on the Least Square Regression Model (OLS)." In 2021 International Conference on Financial Management and Economic Transition (FMET 2021). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/aebmr.k.210917.083.
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Liu, Jingdong, Won-Ho Choi, and Fei Hao. "Research on the Influencing Factors of Film Box Office Based on Ordinary Least Square and Threshold Quantile Autoregressive Model." In 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS). IEEE, 2019. http://dx.doi.org/10.1109/iucc/dsci/smartcns.2019.00065.
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