Relevant bibliographies by topics / Multiple linear regression

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multiple linear regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 June 2025

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Journal articles on the topic "Multiple linear regression"

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Genç, S., and M. Mendeş. "Multiple Linear Regression versus Automatic Linear Modelling." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 76, no. 1 (2024): 131–36. http://dx.doi.org/10.1590/1678-4162-13071.

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ABSTRACT In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave very similar results especially when studied with large sample sizes (n≥100), the Automatic linear modelling is preferred for analyzing data sets due to its simplicity in analyzing data and interpreting the results, ability to present results visually and providing more detailed information especially studying large complex data sets. It will be beneficial to use the Automatic linear modelling especially in analyzing massive and complex data sets for the purposes of investigating the relationships between one continuous dependent and 10 or more predictors and determine the factors that affect the response or target variable. At the same time, it will also be possible to evaluate the effect of each predictor with a more detailed response.
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Krzywinski, Martin, and Naomi Altman. "Multiple linear regression." Nature Methods 12, no. 12 (2015): 1103–4. http://dx.doi.org/10.1038/nmeth.3665.

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Slinker, Bryan K., and Stanton A. Glantz. "Multiple Linear Regression." Circulation 117, no. 13 (2008): 1732–37. http://dx.doi.org/10.1161/circulationaha.106.654376.

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Fletcher, J. "Multiple linear regression." BMJ 338, jan28 3 (2009): b167. http://dx.doi.org/10.1136/bmj.b167.

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Bangdiwala, Shrikant I. "Regression: multiple linear." International Journal of Injury Control and Safety Promotion 25, no. 2 (2018): 232–36. http://dx.doi.org/10.1080/17457300.2018.1452336.

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Grégoire, G. "Multiple Linear Regression." EAS Publications Series 66 (2014): 45–72. http://dx.doi.org/10.1051/eas/1466005.

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Narula, Subhash C., and John F. Wellington. "Multiple criteria linear regression." European Journal of Operational Research 181, no. 2 (2007): 767–72. http://dx.doi.org/10.1016/j.ejor.2006.06.026.

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Pandis, Nikolaos. "Multiple linear regression analysis." American Journal of Orthodontics and Dentofacial Orthopedics 149, no. 4 (2016): 581. http://dx.doi.org/10.1016/j.ajodo.2016.01.012.

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Marill, Keith A. "Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression." Academic Emergency Medicine 11, no. 1 (2004): 94–102. http://dx.doi.org/10.1111/j.1553-2712.2004.tb01379.x.

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Rust, Henning W., Andy Richling, Peter Bissolli, and Uwe Ulbrich. "Linking teleconnection patterns to European temperature – a multiple linear regression model." Meteorologische Zeitschrift 24, no. 4 (2015): 411–23. http://dx.doi.org/10.1127/metz/2015/0642.

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Dissertations / Theses on the topic "Multiple linear regression"

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Ollikainen, Kati. "PARAMETER ESTIMATION IN LINEAR REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4138.

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Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist with confidence intervals not including the true parameter (even though the variable was selected). The Elder/Pregibon rule was used for variable selection. A comparison of the bootstrap Percentile and BCa confidence interval was made as well as an investigation of adjustments to the usual 95% confidence intervals based on the Bonferroni and Scheffe multiple comparison principles. The results show that linear regression has problems in capturing the true parameters in the confidence intervals for the sample sizes considered, the bootstrap intervals perform no better than linear regression, and the Scheffe method is too wide for any application considered. The Bonferroni adjustment is recommended for larger sample sizes and when the t-value for a selected variable is about 3.35 or higher. For smaller sample sizes all methods show problems with type II errors resulting from confidence intervals being too wide.<br>Ph.D.<br>Department of Industrial Engineering and Management Systems<br>Engineering and Computer Science<br>Industrial Engineering and Management Systems
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Forslund, Gustaf, and David Åkesson. "Predicting share price by using Multiple Linear Regression." Thesis, KTH, Farkost och flyg, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140645.

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The aim of the project was to design a multiple linear regression model and use it to predict the share’s closing price for 44 companies listed on the OMX Stockholm stock exchange’s Large Cap list. The model is intended to be used as a day trading guideline i.e. today’s information is used to predict tomorrow’s closing price. The regression was done in Microsoft Excel 2010[18] by using its built-in function LINEST. The LINEST-function uses the dependent variable y and all the covariates x to calculate the β-value belonging to each covariate. Several multiple linear regression models were created and their functionality was tested, but only seven models were better than chance i.e. more than 50 % in the right direction. To determine the most suitable model out of the remaining seven, Akaike’s Information Criterion (AIC), was applied. The covariates used in the final model were; Dow Jones closing price, Shanghai opening price, conjuncture, oil price, share’s opening price, share’s highest price, share’s lowest price, lending rate, reports, positive/negative insider trading, payday, positive/negative price target, number of completed transactions during one day, OMX Stockholm closing price, TCW index, increasing closing price three days in a row and decreasing closing price three days in a row. The maximum average deviation between the predicted closing price and the real closing price of all the 44 shares predicted were 6,60 %. In predicting the correct direction (increase or decrease) of the 44 shares an average of 61,72 % were achieved during the time period 2012-02-22 to 2013-02-20. If investing 50.000 SEK in each company i.e. a total investment of 2.2 million SEK, the total yield when using the regression model during the year 2012-02-22 to 2013-02-20 would have been 259.639 SEK (11,80 %) compared to 184.171 SEK (8,37 %) if the shares were never to be traded with during the same period of time. Of the 44 companies analysed, 31 (70,45 %) of them were profitable when using the regression model during the year compared to 30 (68,18 %) if the shares were never to be sold during the same period of time. The difference in yield in percentage between the model and keeping the shares for the year was 40,98 %.
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Saleem, Aban, and Jacob Blomgren. "Modelling Pupils’ Grades with Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275672.

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This thesis was based on the subjects of mathematical statistics and industrial economics and management in order to analyze the grades of pupils in the final year of elementary school. The purpose was to find out what variables had a statistically significant impact on pupils’ final grades so that municipalities and schools could better understand what variables are important when trying to improve the average school results. A multiple regression model was used on data, obtained from the database of Skolverket, in order to examine what variables were statistically important. The final regression model acquired through a model reduction procedure showed that mostly structural covariates such as the academic background of pupils, percentage of female pupils and the percentage with Swedish background had a statistically significant impact on the academic performances of the students. R2 adjusted of the final model was 0.5289. The multiple regression model was discussed by referencing to previous research. In addition, the strategic management performance framework known as Balanced Scorecard which was introduced by Robert S. Kaplan and David P. Norton was used to discuss relevant key performance indicators to achieve the strategic objectives of schools.<br>Detta examensarbete, inom ämnet för matematisk statistik och industriell ekonomi, genomfördes med syftet att analysera avgångsbetygen för år 9 i den svenska skolan. Syftet var att förstå vilka variabler som hade en statistisk signifikant påverkan på elevers avgångsbetyg, så kommuner kan förstå vilka variabler som är viktiga för att förbättra de genomsnittliga skolresultaten. En regressionsanalys utfördes, på data från Skolverket, för att se vilka variabler som var statistiskt signifikanta. Den slutgiltiga regressionsmodellen, erhållen genom iterativ reducering av variabler, visade att främst strukturella kovariat, som akademisk bakgrund hos elever, andel kvinnliga studenter och andel studenter med svensk bakgrund hade en signifikant betydelse på studenters akademiska resultat. Justerad R2 var 0.5289 för den slutgiltiga modellen. I diskussionen utvärderades modellen utifrån tidigare forskning. Vidare användes teorin om balanserat styrkort, utvecklat av Robert S. Kaplan och David P. Norton, för att diskutera relevanta nyckeltal för att uppnå strategiska mål för skolan.
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Gustafsson, Alexander, and Sebastian Wogenius. "Modelling Apartment Prices with the Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146735.

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This thesis examines factors that are of most statistical significance for the sales prices of apartments in the Stockholm City Centre. Factors examined are address, area, balcony, construction year, elevator, fireplace, floor number, maisonette, monthly fee, penthouse and number of rooms. On the basis of this examination, a model for predicting prices of apartments is constructed. In order to evaluate how the factors influence the price, this thesis analyses sales statistics and the mathematical method used is the multiple linear regression model. In a minor case-study and literature review, included in this thesis, the relationship between proximity to public transport and the prices of apartments in Stockholm are examined. The result of this thesis states that it is possible to construct a model, from the factors analysed, which can predict the prices of apartments in Stockholm City Centre with an explanation degree of 91% and a two million SEK confidence interval of 95%. Furthermore, a conclusion can be drawn that the model predicts lower priced apartments more accurately. In the case-study and literature review, the result indicates support for the hypothesis that proximity to public transport is positive for the price of an apartment. However, such a variable should be regarded with caution due to the purpose of the modelling, which differs between an individual application and a social economic application<br>Denna uppsats undersöker faktorer som är av störst statistisk signifikans för priset vid försäljning av lägenheter i Stockholms innerstad. Faktorer som undersöks är adress, yta, balkong, byggår, hiss, kakelugn, våningsnummer, etage, månadsavgift, vindsvåning och antal rum. Utifrån denna undersökning konstrueras en modell för att predicera priset på lägenheter. För att avgöra vilka faktorer som påverkar priset på lägenheter analyseras försäljningsstatistik. Den matematiska metoden som används är multipel linjär regressionsanalys. I en mindre litteratur- och fallstudie, inkluderad i denna uppsats, undersöks sambandet mellan närhet till kollektivtrafik och priset på läagenheter i Stockholm.   Resultatet av denna uppsats visar att det är möjligt att konstruera en modell, utifrån de faktorer som undersöks, som kan predicera priset på läagenheter i Stockholms innerstad med en förklaringsgrad på 91 % och ett två miljoner SEK konfidensintervall på 95 %. Vidare dras en slutsats att modellen preciderar lägenheter med ett lägre pris noggrannare. I litteratur- och fallstudien indikerar resultatet stöd för hypotesen att närhet till kollektivtrafik är positivt för priset på en lägenhet. Detta skall dock betraktas med försiktighet med anledning av syftet med modelleringen vilket skiljer sig mellan en individuell tillämpning och en samhällsekonomisk tillämpning.
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Peraça, Maria da Graça Teixeira. "Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação." reponame:Repositório Institucional da FURG, 2009. http://repositorio.furg.br/handle/1/3436.

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Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.<br>Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-22T19:51:54Z No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5)<br>Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-03T19:20:55Z (GMT) No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5)<br>Made available in DSpace on 2013-06-03T19:20:55Z (GMT). No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5) Previous issue date: 2009<br>Nas engenharias, é fundamental estimar o tempo de vida útil das estruturas construídas, o que neste trabalho significa o tempo que os íons cloretos levam para atingirem a armadura do concreto. Um dos coeficientes que influenciam na vida útil do concreto é o de difusão, sendo este diretamente influenciado pelo grau de saturação (GS) do concreto. Recentes estudos levaram ao desenvolvimento de um método de medição do GS. Embora esse método seja eficiente, ainda assim há um grande desperdício de tempo e dinheiro em utilizá-lo. O objetivo deste trabalho é reduzir estes custos calculando uma boa aproximação para o valor do GS com modelos matemáticos que estimem o seu valor através de variáveis ambientais que influenciam na sua variação. As variáveis analisadas nesta pesquisa, são: pressão atmosférica,temperatura do ar seco, temperatura máxima, temperatura mínima, taxa de evaporação interna (Pichê), taxa de precipitação, umidade relativa, insolação, visibilidade, nebulosidade e taxa de evaporação externa. Todas foram analisadas e comparadas estatisticamente com medidas do GS obtidas durante quatro anos de medições semanais, para diferentes famílias de concreto. Com essas análises, pode-se medir a relação entre estes dados verificando que os fatores mais influentes no GS são, temperatura máxima e umidade relativa. Após a verificação desse resultado, foram elaborados modelos estatísticos, para que, através dos dados ambientais, cedidos pelo banco de dados meteorológicos, se possam calcular, sem desperdício de tempo e dinheiro, as médias aproximadas do GS para cada estação sazonal da região sul do Brasil, garantindo assim uma melhor estimativa do tempo de vida útil em estruturas de concreto.<br>In engineering, it is fundamental to estimate the life-cycle of built structures, which in this study means the period of time required for chlorides to reach the concrete reinforcement. One of the coefficients that affect the life-cycle of concrete is the diffusion, which is directly influenced by the saturation degree (SD) of concrete. Recent studies have led to the development of a measurement method for the SD. Although this method is efficient, there is still waste of time and money when it is used. The objective of this study is to reduce costs by calculating a good approximation for the SD value with mathematical models that predict its value through environmental variables that affect its variation. The variables analysed in the study are: atmospheric pressure, temperature of the dry air, maximum temperature, minimum temperature, internal evaporation rate (Pichê), precipitation rate, relative humidity, insolation, visibility, cloudiness and external evaporation rate. All of them were statistically analysed and compared with measurements of SD obtained during four years of weekly assessments for different families of concrete. By considering these analyses, the relationship among these data can be measured and it can be verified that the most influent variables affecting the SD are the maximum temperature and the relative humidity. After verifying this result, statistical models were developed aiming to calculate, based on the environmental data provided by the meteorological database and without waste of time and money, the approximate averages of SD for each seasonal station of the south region of Brazil, thus providing a better estimative of life-cycle for concrete structures.
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Alt, Raimund. "Multiple hypotheses testing in the linear regression model with applications to economics and finance /." Göttingen : Cuvillier, 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=013081924&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Kinns, David Jonathan. "Multiple case influence analysis with particular reference to the linear model." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368427.

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Basha, Elizabeth (Elizabeth Ann). "In-situ prediction on sensor networks using distributed multiple linear regression models." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60096.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 199-208).<br>Within sensor networks for environmental monitoring, a class of problems exists that requires in-situ control and modeling. In this thesis, we provide a solution to these problems, enabling model-driven computation where complex models are replaced by in-situ sensing and communication. These prediction models utilize low-computation, low-communication, and distributed algorithms suited to autonomous operation and multiple applications. We achieve this through development of new algorithms that enable distributed computation of the pseudo inverse of a matrix on a sensor network, thereby enabling a wide range of prediction methods. We apply these models to three different application areas: (1) river flooding for early warning, (2) solar recharging current for power management, and (3) job congestion prediction on multi-function device networks for achieving quality of service. Additionally, we use these applications to explore other aspects of sensor networks: river flooding to design a predictive environmental monitoring sensor network, solar current to develop a dynamic version of the model for better fault tolerance, and job congestion to explore modeling multi-function device networks. For each, we comprehensively tested the full solutions. We implemented the river flood prediction and solar current prediction solutions on two different sensor network platforms with full field deployments; we had a final test of over 5 weeks operation for both. Overall, we achieve the following contributions: (1) distributed algorithms for computing a matrix pseudoinverse and multiple linear regression model on a sensor network, (2) three applications of these algorithms with associated field experiments demonstrating their versatility, (3) a sensor network architecture and implementation for river flood prediction as well as other applications requiring real-time data and a low node count to geographic area ratio, and (4) a MFD simulator predicting and resolving congestion.<br>by Elizabeth Ann Basha.<br>Ph.D.
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Galijasevic, Amar, and Josef Tegbaru. "Can IPO first day returns be predicted? A multiple linear regression analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254293.

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During the last three years the Swedish stock market has showed a strong upwards movement from the lows of 2016. At the same time the IPO activity has been large and a lot of the offerings have had a positive return during the first day of trading in the market. The goal of this study is to analyze if there is any particular IPO specific data that has a correlation with the first day return and if it can be used to predict the first day return for future IPO’s. If any regressors were shown to have correlation with the first day return, the goal is also to find a subset of regressors with even higher predictability. Then to classify which regressors show the highest correlation with a large positive return. The method which has been used is a multiple linear regression with IPO-data from the period 2017-2018. The results from the study imply that none of the chosen regressors show any significant correlation with the first day return. It is a complicated process which might be difficult to simplify and quantify into a regression model, but further studies are needed to draw a conclusion if there are any other qualitative factors which correlate with the first day return.<br>Under de senaste tre åren har den svenska aktiemarknaden visat en kraftigt uppåtgående rörelse från de låga nivåerna 2016. Samtidigt har det varit hög IPO-aktivitet, där många noteringar har haft en positiv avkastning under den första handelsdagen. Målet med denna studie är att analysera om det finns särskilda IPO-specifika faktorer som påvisar samband med avkastningen från första handelsdagen och om det kan användas för att förutsäga utvecklingen under första handelsdagen för framtida noteringar. Om regressorerna visade korrelation är målet sedan att ta fram de bästa av dessa för att se om det ökar modellens säkerhet. Vidare var det av intresse att visa vilka regressorer som korrelerar med en positiv avkastning. Metoden som användes var en multipel linjär regression med historisk data från perioden 2017-2018. Studiens resultat visar att ingen av de valda regressorerna har någon signifikant korrelation med avkastningen under första handelsdagen. Börsintroduktioner är komplicerade processer som kan vara svåra att förenkla och kvantifiera i en regressionsmodell, men ytterligare studier behövs för att dra en slutsats om det finns andra kvalitativa faktorer som kan förklara utvecklingen under första handelsdagen.
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Leda, Victor Costa [UNESP]. "Modelagem da produtividade de cana-de-açúcar utilizando índices de vegetação." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/143945.

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Submitted by VICTOR COSTA LEDA null (victorleda@gmail.com) on 2016-09-20T13:48:09Z No. of bitstreams: 1 Dissertação Victor Costa Leda Final.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5)<br>Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-09-22T19:30:58Z (GMT) No. of bitstreams: 1 leda_vc_me_bot.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5)<br>Made available in DSpace on 2016-09-22T19:30:58Z (GMT). No. of bitstreams: 1 leda_vc_me_bot.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5) Previous issue date: 2016-07-28<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>A produção da cana-de-açúcar é destaque no cenário econômico do estado São Paulo, dessa forma confirma-se a necessidade do monitoramento dessa cultura, de maneira a contribuir com melhorias em decisões e planejamentos operacionais. A produção total e a produtividade da cana-de-açúcar são fatores de grande interesse para os agricultores, pois é a partir dessa informação que a programação das operações são realizadas, porém, essas estimativas não possuem métodos de alta precisão e confiança em amostragens não destrutivas. O homem possui excelente capacidade de analisar e interpretar resultados, mas também está sujeito a subjetividades em suas avaliações. A análise empreendida no trabalho teve como objetivo a elaboração de modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, possui aproximadamente 6000 hectares, com altimetrias variando entre 600 e 700 metros. Para a coleta das informações espectrais, utilizou-se as imagens do satélite Landsat 8, com órbita/ponto em 221/076. Nos resultados do trabalho realizado, constatou-se que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97. Sendo que em períodos com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto que em períodos com coeficientes de determinação baixos, muito provavelmente foram obtidos devido a outros fatores listados terem ocorrido como dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos.<br>The production of sugarcane is a highlight in the economic scenario in the state of São Paulo, thus it confirms the need of monitoring this culture, in order to contribute to improvements in making decisions and operational planning.The production and productivity of sugarcane are factors of great interest to farmers, because, from this information the planning of operations is performed out, however, these estimates do not have high precision and reliable methods for non-destructive sampling.The human has an excellent ability to analyze and interpret results, but may also be affected by the subjectivity of their evaluations.The analysis undertaken in this work aimed at the development of mathematical models to explain the productivity of sugarcane through geoprocessing and remote sensing.The experiment was conducted in commercial area of Agrícola Rio Claro, partner of Zilor group, which is located in Lençóis Paulista and Pratânia, of approximately 6000 hectares, with altimetry ranging between 600 and 700 meters. For the collection of the spectral information, it was used the images of the satellite Landsat 8, with orbit/point 221/076. The results of the work, it was found that all the modeling were satisfactory, varying the coefficient of determination between 0.15 to 0.97. Given that, in periods with high coefficients of determination areas may be generally found in clusters, suggesting a lower incidence of variables. While in periods of low coefficient of determination, it was most likely obtained due to other factors listed of having occurred such as a dispersion of the plots in the area, soil types, rainfall and varieties, probably distinctly.
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Books on the topic "Multiple linear regression"

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0.

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Shelton, Katherine Lesley. An illustration of heteroscedasticity in the multiple linear regression model. The author], 1985.

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J, Niccolucci Michael, Schuster Ervin G, and Intermountain Research Station (Ogden, Utah), eds. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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Booth, Gordon D. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1994.

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Center, Lewis Research, ed. A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys. National Aeronautics and Space Administration, Lewis Research Center, 1985.

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Zorn, Troy G. Utility of species-specific, multiple linear regression models for prediction of fish assemblages in rivers of Michigan's lower peninsula. Michigan Dept. of Natural Resources, Fisheries Division, 2004.

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Scott Jones, Julie. Learn to Clean and Prepare Data for a Multiple Linear Regression in SPSS Using Data From the Health Survey for England (2011) (Teaching Dataset). SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605327.

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Multiple Linear Regression. Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630138.

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Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429066665.

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Creating Multiple Linear Regression Model. Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630251.

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Book chapters on the topic "Multiple linear regression"

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Olive, David J. "Multiple Linear Regression." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_2.

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Brown, Jonathon D. "Multiple Regression." In Linear Models in Matrix Form. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_4.

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Hoffmann, John P. "Multiple Linear Regression Models." In Linear Regression Models. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003162230-4.

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Eberly, Lynn E. "Multiple Linear Regression." In Topics in Biostatistics. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_9.

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Flury, Bernhard, and Hans Riedwyl. "Multiple linear regression." In Multivariate Statistics. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1217-5_5.

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Armstrong, Richard A., and Anthony C. Hilton. "Multiple Linear Regression." In Statistical Analysis in Microbiology: Statnotes. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch25.

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Holmes, William H., and William C. Rinaman. "Multiple Linear Regression." In Statistical Literacy for Clinical Practitioners. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12550-3_14.

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Sheather, Simon J. "Multiple Linear Regression." In Springer Texts in Statistics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-09608-7_5.

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Gooch, Jan W. "Multiple Linear Regression." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15294.

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Everitt, Brian, and Sophia Rabe-Hesketh. "Multiple Linear Regression." In Analyzing Medical Data Using S-PLUS. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3285-6_9.

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Conference papers on the topic "Multiple linear regression"

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Ivanovski, Tomislav, Guoxiang Zhang, Tomislav Jemric, Marko Gulic, and Maja Matetic. "Fruit firmness prediction using multiple linear regression." In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). IEEE, 2020. http://dx.doi.org/10.23919/mipro48935.2020.9245213.

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Castillo-Garit, Juan, Yudith Cañizares-Carmenate, Karel Mena-Ulecia, Yunier Perera-Sardiña, and Francisco Torrens. "Multiple Linear Regression Model of Thermolysin Inhibitors." In MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition. MDPI, 2017. http://dx.doi.org/10.3390/mol2net-02-03872.

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R, Madhumathi, Arumuganathan T, Shruthi R, and Raghavendar S. "Soil NPK Prediction Using Multiple Linear Regression." In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2022. http://dx.doi.org/10.1109/icaccs54159.2022.9785338.

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Guo, Yuanchun. "Unbiased Least Squares Regression Coefficients for Multiple Linear Regression Mathematical Models." In ICISCAE 2021: 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education. ACM, 2021. http://dx.doi.org/10.1145/3482632.3484099.

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Xu, Lei, Hongliang Yu, Xiaohong Wang, and Guochuan Sun. "Application of multiple linear regression in cement burden." In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2019. http://dx.doi.org/10.1109/imcec46724.2019.8984127.

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Gomis, Francois Kassene, Mamadou Samba Camara, Idy Diop, Sidi Mohamed Farssi, Khaly Tall, and Birahime Diouf. "Multiple linear regression for universal steganalysis of images." In 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2018. http://dx.doi.org/10.1109/isacv.2018.8354060.

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Aiyin, Wang, and Xu Yanmei. "Multiple Linear Regression Analysis of Real Estate Price." In 2018 International Conference on Robots & Intelligent System (ICRIS). IEEE, 2018. http://dx.doi.org/10.1109/icris.2018.00145.

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Audone, Bruno, and Gergio Giunta. "Multiple Linear Regression to detect shielding effectiveness degradations." In 2008 International Symposium on Electromagnetic Compatibility - EMC Europe. IEEE, 2008. http://dx.doi.org/10.1109/emceurope.2008.4786802.

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Alhamide, A. A., K. Ibrahim, and M. T. Alodat. "Multiple linear regression estimators with skew normal errors." In THE 2015 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2015 Postgraduate Colloquium. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4931340.

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Mohammed, Najeebuddin, A. Kusalava Sarma, and Shahid Dhamani. "Multiple Linear Regression Model for Inflation in India." In 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021. http://dx.doi.org/10.1109/incet51464.2021.9456277.

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Reports on the topic "Multiple linear regression"

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Kubik, Harold. MLRP, Multiple Linear Regression Program. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada204565.

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Chernis, Tony, Niko Hauzenberger, Florian Huber, Gary Koop, and James Mitchell. Predictive Density Combination Using a Tree-Based Synthesis Function. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202330.

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Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a “synthesis” function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area’s Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits – in terms of improved forecast accuracy and interpretability – of modeling the synthesis function nonparametrically.
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Huang, Tao, and Venkatesh Merwade. Developing Customized NRCS Unit Hydrographs (Finley UHs) for Ungauged Watersheds in Indiana. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317644.

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The Natural Resources Conservation Service (NRCS, formerly the Soil Conservation Service, SCS) unit hydrograph (UH) is one of the most commonly used synthetic UH methods for hydrologic modeling and engineering design all over the world. However, previous studies have shown that the application of the NRCS UH method for some ungauged watersheds in the state of Indiana produced unrealistic flood predictions for both the peak discharge and the time to peak. The objective of this work is to customize the NRCS UH by analyzing the role of its two key parameters, namely, the peak rate factor (PRF) and the lag time, in creating the runoff hydrograph. Based on 120 rainfall-runoff events collected from 30 small watersheds in Indiana over the past two decades, the observed UHs are derived and the corresponding PRF and lag time are extracted. The observed UHs in Indiana show that the mean value of PRF is 371, which is lower than the standard PRF of 484, and the NRCS lag time equation tends to underestimate the “true” lag time. Moreover, a multiple linear regression method, especially the stepwise selection technique, is employed to relate the NRCS UH parameters to the most appropriate geomorphic attributes extracted from the study watersheds. Both the statewide and regional regression models show that the main channel slope is a major factor in determining the PRF and lag time. A customized Indiana unit hydrograph, referred as Finley UH to honor David Finley who inspired this study, is derived with updated parameters and the Gamma function. Validation results show that the Finley UH provides more reliable and accurate predictions in terms of the peak discharge and the time to peak than the original NRCS UH for the watersheds in Indiana.
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Straub, Stéphane. Empirical Determinants of Good Institutions: Do We Know Anything? Inter-American Development Bank, 2000. http://dx.doi.org/10.18235/0011558.

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Abundant empirical evidence links well-functioning institutions and good governance to better economic and social outcomes. It is thus an important challenge to determine which conjunction of factors produces better institutions. Along this line, the objective of this paper is twofold. First, it examines the existing results of the literature on this matter in a critical way, tries to assess their robustness, and explores alternative methodology. Second, it makes use of a more comprehensive database, including all the aspects previously analyzed in a separate manner, to derive systematic empirical results. After discussing the traditional robustness checks employed, for example, in the empirical growth literature, which appear to be of limited usefulness, we introduce factor analysis as a preliminary step toward model specification and subsequently perform multiple regression analysis. Of the four levels of explanation that we identify, namely control and historical variables, the nature of the political game, the size and nature of existing rents to be allocated, and the nature and quality of bureaucratic incentives, the later appears to be the more clearly linked to institutional quality. However, the results prove not robust when dealing with endogeneity problems. Various kinds of interactions and non-linear effects are also investigated, yielding no clear insights. We conclude regarding the fragility of existing data, in particular with respect to the incentive structure, and the need for a better theoretical understanding of the underlying mechanisms.
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Alexander, Serena E., Mariela Alfonzo, and Kevin Lee. Safeguarding Equity in Off-Site Vehicle Miles Traveled (VMT) Mitigation in California. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2027.

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Historically, the State of California assessed the environmental impacts of proposed developments based on how it was projected to affect an area’s level of service (LOS). However, as LOS focused on traffic delays, many agencies simply widened roads, which was an ineffective way to reduce greenhouse gas emissions (GHGs). With the passage of Senate Bill (SB)743 in 2013, LOS was replaced by Vehicle Miles Traveled (VMT) as a more appropriate metric by which to gauge the environmental impacts of proposed development. Additionally, SB 743 presented an opportunity for off-site VMT mitigation strategies through banking and exchanges– allowing multiple development projects to fund a variety of strategies to reduce VMT elsewhere in the city or region. While the shift from LOS to VMT has generally been lauded, concerns remain about how to apply SB 743 effectively and equitably. This study aimed to: 1) understand how local governments are addressing this shift toward VMT while ensuring equity, including its approaches to off-site VMT mitigation; and 2) evaluate the various built environment factors that impact VMT, which should be considered by local governments, using both qualitative and quantitative research designs. The study posited that both micro and macro level aspects of the built environment needed to be considered when evaluating the impacts of proposed development on VMT, not only to ensure higher accuracy VMT models, but also because of the potential equity implications of off-site mitigation measures. Using multiple linear regression, the study shows that macroscale built environment features such as land use, density, housing, and employment access have a statistically significant impact on reducing VMT (35%), along with transit access (15%), microscale features such as sidewalks, benches, and trees (13%), and income (6%). More notably, a four-way interaction was detected, indicating that VMT is dependent on the combination of macro and micro level built environment features, public transit access, and income. Additionally, qualitative interviews indicate that transportation practitioners deal with three types of challenges in the transition to VMT impact mitigation: the lack of reliable, standardized VMT measure and evaluation tools; the lack of a strong legal foundation for VMT as a component of the California Environmental Quality Act (CEQA); and the challenge of distributing off-site VMT mitigation equitably. Overall, findings support a nuanced, multi-factor understanding of the context in which new developments are being proposed, both in terms of modeling VMT, but also when considering whether offsite mitigation would be appropriate. The results of this study can help California ensure equitable VMT mitigation that better aligns with the state’s climate goals.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Castellano, Mike J., Abraham G. Shaviv, Raphael Linker, and Matt Liebman. Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile soil organic matter fractions. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597926.bard.

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A major goal in Israeli and U.S. agroecosystems is to maximize nitrogen availability to crops while minimizing nitrogen losses to air and water resources. This goal has presented a significant challenge to global agronomists and scientists because crops require large inputs of nitrogen (N) fertilizer to maximize yield, but N fertilizers are easily lost to surrounding ecosystems where they contribute to water pollution and greenhouse gas concentrations. Determination of the optimum N fertilizer input is complex because the amount of N produced from soil organic matter varies with time, space and management. Indicators of soil N availability may help to guide requirements for N fertilizer inputs and are increasingly viewed as indicators of soil health To address these challenges and improve N availability indicators, project 4550 “Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile organic matter fractions” addressed the following objectives: Link the quantity and quality of labile soil organic matter fractions to indicators of soil fertility and environmental quality including: i) laboratory potential net N mineralization ii) in situ gross N mineralization iii) in situ N accumulation on ion exchange resins iv) crop uptake of N from mineralized soil organic matter sources (non-fertilizer N), and v) soil nitrate pool size. Evaluate and compare the potential for hot water extractable organic matter (HWEOM) and particulate organic matter quantity and quality to characterize soil N dynamics in biophysically variable Israeli and U.S. agroecosystems that are managed with different N fertility sources. Ultimately, we sought to determine if nitrogen availability indicators are the same for i) gross vs. potential net N mineralization processes, ii) diverse agroecosystems (Israel vs. US) and, iii) management strategies (organic vs. inorganic N fertility sources). Nitrogen availability indicators significantly differed for gross vs. potential N mineralization processes. These results highlight that different mechanisms control each process. Although most research on N availability indicators focuses on potential net N mineralization, new research highlights that gross N mineralization may better reflect plant N availability. Results from this project identify the use of ion exchange resin (IERs) beads as a potential technical advance to improve N mineralization assays and predictors of N availability. The IERs mimic the rhizosphere by protecting mineralized N from loss and immobilization. As a result, the IERs may save time and money by providing a measurement of N mineralization that is more similar to the costly and time consuming measurement of gross N mineralization. In further search of more accurate and cost-effective predictors of N dynamics, Excitation- Emission Matrix (EEM) spectroscopy analysis of HWEOM solution has the potential to provide reliable indicators for changes in HWEOM over time. These results demonstrated that conventional methods of labile soil organic matter quantity (HWEOM) coupled with new analyses (EEM) may be used to obtain more detailed information about N dynamics. Across Israeli and US soils with organic and inorganic based N fertility sources, multiple linear regression models were developed to predict gross and potential N mineralization. The use of N availability indicators is increasing as they are incorporated into soil health assessments and agroecosystem models that guide N inputs. Results from this project suggest that some soil variables can universally predict these important ecosystem process across diverse soils, climate and agronomic management. BARD Report - Project4550 Page 2 of 249
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Killgore, K., Jan Hoover, Amanda Oliver, W. Slack, and Alan Katzenmeyer. Ecological Model to Evaluate Borrow Areas in the Lower Mississippi River. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48257.

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An aquatic analysis of constructing borrow areas adjacent to the main line levees in the Lower Mississippi River was conducted as part of an Environmental Impact Statement for upgrading the levee system. A Habitat Suitability Index (HSI) regression model based on field collections was developed to predict fish species richness as a function of the morphometry and water quality of borrow areas. The HSI score was multiplied by acres of borrow areas created during construction to obtain habitat units (HUs) for each alternative indicating a substantial gain of fishery habitat in the floodplain. Environmental features identified by the model to increase fish species richness and overall habitat heterogeneity include the shape of the pit (e.g., bowl-shaped with deep water rather than long rectangular with shallower water), the availability of littoral areas for fish spawning and rearing, using best management practices such as tree screens and bank stabilization to lower turbidity, adding islands, and creating sinuous shorelines. The project results in an overall gain in aquatic habitat by creating permanent or semi-permanent water bodies on the floodplain that our research indicates may be occupied by at least 75 species of fish contributing to the overall biodiversity of the lower Mississippi River.
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Klemt, Myrjam. Haltung = Handlung? Inwieweit entsprechen sich Haltung und Handlung bezüglich des nachhaltigen Kleidungskonsums? Bachelorarbeit an der Universität Göttingen. Sonderforschungsgruppe Institutionenanalyse, 2019. http://dx.doi.org/10.46850/sofia.9783941627765.

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Angesichts des gegenwärtigen Trends eines zunehmenden Diskurses über einen nachhaltigen Lebensstil, den politischen Debatten um die bestehende Ressourcenknappheit und der globalen anthropogenen Erderwärmung, verfügen diese drei Konstrukte über eine Konstante. Diese Thematiken vereint, dass der Mensch als Akteur die Verantwortung für diese Entwicklungen zu tragen hat. Der Dringlichkeit zu handeln bewusst, stellt sich die Frage, inwieweit die Intention nachhaltig zu Handeln mit der wirklichen Umsetzung übereinstimmt. In Form einer Online-Umfrage zum Thema nachhaltigen Konsum von Kleidung wurden Primärdaten generiert, die es im Rahmen dieser Bachelorarbeit zu analysieren gilt. Dieser Vorgang hat zum Ziel, die Forschungsfrage inwieweit sich Intention und Handlung, in Hinblick auf nachhaltigen Konsum von Kleidungsstücken entsprechen, eigenständig zu konzeptualisieren und zu beantworten. Die damit verbundenen Erkenntnisse offerieren neue Handlungschancen, um ein nachhaltigeres Konsumverhalten in Bezug auf Kleidung in einem gesellschaftlichen Kontext zu ermöglichen. Die Gliederung der Arbeit orientiert sich an einem thematischen Aufbau, der das Verständnis dieser Thematik uneingeschränkt zulässt. Zunächst wird im Rahmen des theoretischen Teils über den entsprechenden Forschungsstand der Wissenschaft informiert. Im Weiteren wird die zugrundeliegende Fragestellung skizziert und die damit einhergehenden Forschungshypothesen hergeleitet. Diese drei Forschungshypothesen ermöglichen im Weiteren analytischen Verlauf letztlich die Beantwortung der Forschungsfrage. Es folgt der empirische Teil, welcher die Forschungsmethode beschreibt und vertiefend die Durchführung und die Auswertungsmethoden veranschaulicht. Bezüglich dessen wird auch die Operationalisierung thematisiert, welche die Realität in Form von Items messbar macht. Um die erlangten Informationen in Verbindung mit der übergeordneten Fragestellung zu setzen, müssen bezüglich der Ergebnisanalyse, zunächst die Daten deskriptiv skizziert werden. Folgend werden im Rahmen linearer und einer multiplen Regression hypothesenprüfende Ergebnisse generiert. Auf Grundlage dieser, wird die Beantwortung der vorangestellten Forschungsfrage ermöglicht. Es gilt die Ergebnisse zunächst zusammen zu tragen, um die Interpretation in den aktuellen Forschungsstand einzuordnen. Anschließend wird die eigene Forschungsmethodik einer kritischen Reflexion unterzogen, um somit mögliche Optimierungsmöglichkeiten zu benennen und weiteren Handlungsbedarf kenntlich zu machen. Konzeptualisiert werden die empirischen Ergebnisse in Form des abschließenden Fazits, welches sich auch Bereichen widmet in denen potenzieller Handlungsbedarf benannt wird. Im besten Fall lädt diese Arbeit und die damit verbundenen neuen empirischen Erkenntnisse ein, Handlungsräume in Bezug auf Determinanten wie Moral, Information und Preis neu zu bewerten, um nachhaltigen Kleidungskonsum zu ermöglichen. Auf dieser Grundlage könnten sich geeignetere Strategien im Rahmen weitergehender Forschung entwickeln lassen, um nachhaltiges Verhalten tiefgreifender zu ermöglichen und damit einen gesellschaftlichen Beitrag leisten zu können.
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