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Relevant bibliographies by topics / Linear multiple stepwise regression analysis

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Academic literature on the topic 'Linear multiple stepwise regression analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Linear multiple stepwise regression analysis"

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Park, ManKi, HyeRan Yoon, KyoungHo Kim, and JungHwan Cho. "Quantitative analysis by diffuse reflectance infrared Fourier transform and linear stepwise multiple regression analysis I —Simultaneous quantitation of ethenzamide, isopropylantipyrine, caffeine, and allylisopropylacetylurea in tablet by DRIFT and linear stepwise multiple regression analysis—." Archives of Pharmacal Research 11, no. 2 (1988): 99–113. http://dx.doi.org/10.1007/bf02857712.

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Ray, Supratim, Chandana Sengupta, and Kunal Roy. "QSAR modeling for lipid peroxidation inhibition potential of flavonoids using topological and structural parameters." Open Chemistry 6, no. 2 (2008): 267–76. http://dx.doi.org/10.2478/s11532-008-0014-7.

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AbstractIn the present study, Quantitative Structure-Activity Relationship (QSAR) modeling has been carried out for lipid peroxidation (LPO)-inhibition potential of a set of 27 flavonoids, using structural and topological parameters. For the development of models, three methods were used: (1) stepwise regression, (2) factor analysis followed by multiple linear regressions (FA-MLR) and (3) partial least squares (PLS) analysis. The best equation was obtained from stepwise regression analysis (Q2 = 0.626) considering the leave-oneout prediction statistics.

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Liu, Yingxia, Gerard B. M. Heuvelink, Zhanguo Bai, et al. "Analysis of spatio-temporal variation of crop yield in China using stepwise multiple linear regression." Field Crops Research 264 (May 2021): 108098. http://dx.doi.org/10.1016/j.fcr.2021.108098.

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Ghasemi, Jahan B., Parvin Zohrabi, and Habibollah Khajehsharifi. "Quantitative structure–activity relationship study of nonpeptide antagonists of CXCR2 using stepwise multiple linear regression analysis." Monatshefte für Chemie - Chemical Monthly 141, no. 1 (2009): 111–18. http://dx.doi.org/10.1007/s00706-009-0225-4.

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Zhan, Xinhua, Xiao Liang, Guohua Xu, and Lixiang Zhou. "Influence of plant root morphology and tissue composition on phenanthrene uptake: Stepwise multiple linear regression analysis." Environmental Pollution 179 (August 2013): 294–300. http://dx.doi.org/10.1016/j.envpol.2013.04.033.

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Jia, Renfu, Shibiao Fang, Wenrong Tu, and Zhilin Sun. "Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis." Discrete Dynamics in Nature and Society 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/8957530.

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This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR) and principal component analysis (PCA) to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

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Oliveira, Cinthia Pereira de, Rinaldo Luiz Caraciolo Ferreira, José Antônio Aleixo da Silva, et al. "Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil." Forests 12, no. 4 (2021): 473. http://dx.doi.org/10.3390/f12040473.

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In recent years, with the growing environmental concern regarding climate change, there has been a search for efficient alternatives in indirect methods for the quantification of biomass and forest carbon stock. In this article, we seek to obtain pioneering results of biomass and carbon estimates from forest inventory data and LiDAR technology in a dry tropical forest in Brazil. We use forest inventory data in two areas together with data from the LiDAR flyby, generating estimates of local biomass and carbon levels obtained from local species. We approach three types of models for data analysis: Multiple linear regression with principal components (PCA), conventional multiple linear regression and stepwise multiple linear regression. The best fit total above ground biomass (TAGB) and total above ground carbon (TAGC) model was the stepwise multiple linear regression, concluding, then, that LiDAR data can be used to estimate biomass and total carbon in dry tropical forest, proven by an adjustment considered in the models employed, with a significant correlation between the LiDAR metrics. Our finding provides important information about the spatial distribution of TAGB and TAGC in the study area, which can be used to manage the reserve for optimal carbon sequestration.

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Kokaly, R. "Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression." Remote Sensing of Environment 67, no. 3 (1999): 267–87. http://dx.doi.org/10.1016/s0034-4257(98)00084-4.

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Boulet, Sebastien, Elsa Boudot, and Nicolas Houel. "Relationships between each part of the spinal curves and upright posture using Multiple stepwise linear regression analysis." Journal of Biomechanics 49, no. 7 (2016): 1149–55. http://dx.doi.org/10.1016/j.jbiomech.2016.02.054.

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Hanif, Aswar. "Menggunakan Stepwise Linear Regression Untuk Menentukan Faktor Yang Mempengaruhi Produktivitas Tenaga Kerja." Jurnal Informatika 5, no. 1 (2018): 73–80. http://dx.doi.org/10.31311/ji.v5i1.2701.

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Abstrak Semakin lama masa kerja, semakin banyak pengalaman yang dimiliki seseorang atas pekerjaannya. Seorang yang memiliki tingkat kehadiran yang tinggi, dianggap sebagai pekerja yang baik. Kedua faktor ini membentuk asumsi bahwa masa kerja dan tingkat kehadiran, secara positif atau negatif, mempengaruhi produktivitas pekerja. Dikarenakan besarnya pengaruh produktivitas pekerja terhadap kesehatan sebuah perusahaan, kegiatan menganalisis produktivitas tenaga kerja perusahaan, seharusnya tidak didasarkan pada asumsi-asumsi, meskipun asumsi tersebut bisa diterima. Menggunakan Regresi Linier Berganda, sebuah model persamaan dihasilkan dari data-data mengenai tenaga kerja. Tapi, karena nilai Koefisien Determinasi yang dihasilkan kurang memuaskan, dilakukan analisis ulang terhadap data. Kali ini menggunakan Regresi Linier Stepwise. Analisis kedua ini dapat menghasilkan nilai Koefisien Determinasi yang lebih tinggi dari nilai sebelumnya, meskipun harus diterima bahwa nilai yang baru ini masih terlalu rendah. Meskipun begitu, beberapa fakta mengenai sistem kerja perusahaan dan latar belakang tenaga kerjanya, dapat dijadikan penjelasan mengenai hasil analisis yang telah dilakukan.  Kata kunci: produktivitas, regresi linier stepwise  Abstract  The longer the employment length, the more experience a person has on his or her job. A person who has a high attendance at work, is considered a good worker. These two factors form the assumption that both employment length and work attendance, influence laborer productivity, either positively or negatively. As Labor productivity holds a lot of weight in relation to a company’s health, conducting an analysis of a company's productivity, must not be based on assumptions, even though it’s an acceptable one. Using multiple linear regression, a model was generated from data about the workforce. But, because the Coefficient of Determination value was less than satisfactory, an analysis was performed again on the data, this time using Stepwise Regression. The second analysis managed to produce a higher Coefficient of Determination value than the previous one, but it must accepted that the value remains too low. Though a few facts about the company’s work system and labours history could provide some explanation on this result.  Keyword: productivity, stepwise linear regression

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Dissertations / Theses on the topic "Linear multiple stepwise regression analysis"

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Hu, Qing. "Predictor Selection in Linear Regression: L1 regularization of a subset of parameters and Comparison of L1 regularization and stepwise selection." Link to electronic thesis, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-051107-154052/.

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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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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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Kareflod, Michaela, and Jennifer Ljungquist. "A Study of Hot el Occupancy : Using Multiple Linear Regression and Market Strategy Analysis." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189017.

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This paper is based on collaboration between a company called StayAt HotelApart AB and two KTH students. It examines which factors that are influencing the hotel’s occupancy and how this may be increased by enhancing the market strategy. The aim is to provide a foundation for strategy development to the company. The study is performed by connecting applied mathematics with industrial management. The mathematical part is based on a multiple linear regression on occupancy with historical data from 2011 to 2016 mainly collected from StayAt. The analysis of the market strategy is performed by means of the mathematical results and by using two marketing models, SWOT analysis and 4P’s. The result shows that relative price, weather, high- and low season for the hotel, months on market, occupancy for the competitive set, location and market shares are significant factors influencing the hotel’s occupancy. The main recommendations concluded from the analysis of the market strategy are to put effort on digitalisation, visualising the brand, publications, CSR initiatives, exploiting existing resources and carefully considering timing of marketing.<br>Den här uppsatsen baseras på ett samarbete mellan företaget StayAt HotelApart AB och två KTH- studenter. Den utvärderar vilka faktorer som påverkar hotellets beläggning och hur denna kan öka genom en förbättrad marknadsstrategi. Syftet är att leverera en grund för strategiutveckling till företaget. Studien är genomförd genom att sammankoppla tillämpad matematik med industriell ekonomi. Den matematiska delen baseras på en regressionsanalys av hotellets beläggning med historisk data från 2011 till 2016 som främst är försedd av StayAt. Analysen av marknadsstrategin är genomförd med hjälp av de matematiska resultaten samt genom att applicera två modeller inom marknadsföring, SWOT analys och 4P. Resultaten visar att relativt pris, väder, hög- och lågsäsong för hotellet, månader på marknaden, beläggning för konkurrenter, läge och marknadsandelar är signifikanta faktorer som påverkar hotellets beläggning. De primära rekommendationerna som tagits fram utifrån analysen av marknadsstrategin är att lägga resurser på digitalisering, publikationer och CSR initiativ, att visualisera varumärket, utnyttja existerande resurser samt att grundligt överlägga timing av marknadsföring

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Yeasmin, Mahbuba 1965. "Multiple maxima of likelihood functions and their implications for inference in the general linear regression model." Monash University, Dept. of Econometrics and Business Statistics, 2003. http://arrow.monash.edu.au/hdl/1959.1/5821.

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Goicoechea, Saioa, and Patricia López. "Modeling the air change rate in a naturally ventilated historical church : MultipleLinear Regression analysis." Thesis, Högskolan i Gävle, Akademin för teknik och miljö, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13640.

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In this thesis the air infiltration through the envelope of a naturally ventilated stone church located in Bergby (Gävle, Sweden) is studied. The project is focused on Multiple Linear Regression (MLR) modeling the air change rate (ACH) inside the church hall and studying the factors (stack effect and wind effect) that influence the air infiltration. The weather parameters outside the building were recorded in a weather station and the properties of the air inside the church was analyzed with different methods. Infrared thermography techniques and thermistors were used to measure the temperature inside, the tracer gas method to measure the ACH and the blower door technique to measure the tightness of the building envelope. In order to know the pressure coefficients on the church envelope a physical model of the building was studied in a wind tunnel. Firstly, only the values obtained from the weather station were used to calculate the predictors of ACH and see which parameter influence more on its variation: temperature difference (∆T) indicating the stack effect; and wind speed (WS), the component of wind speed perpendicular to the long-side facades of the church (WS90) and their square values (WS2 and WS902) indicating the wind effect. The data obtained in the wind tunnel were later used to do the MLR study with new predictors for indicating wind effect (∆Cp∙WS, ∆Cp∙WS2, ∆CpOUT-IN·A∙WS, ∆CpOUT-IN·A∙WS2, ∆CpC-H∙WS, ∆CpC-H∙WS2). Better prediction of ACH was obtained with the square of the wind speed (WS2) instead of the magnitude itself (WS). However, the latter (WS) provided better results than the regression with the magnitude of the perpendicular component of the wind (WS90). Although wind speed influences in ACH, it alone seems to be a very poor predictor of ACH since has a negative correlation with ΔT when the data under study include both day and night. However when high wind speed are detected it has quite strong influence. The most significant predictions of ACR were attained with the combined predictors ∆T & WS and ∆T & ∆CpOUT-IN·A∙WS2. The main conclusion taken from the MLR analysis is that the stack effect is the most significant factor influencing the ACH inside the church hall. This leads to suggest that an effective way of reducing ACH could be sealing the floor and ceiling of the church because from those areas the air infiltration has big influence on the ACH inside the church hall, and more in this case that have been noted that the floor is very leaky. Although different assumptions have been done during the analyses that contribute to make the predictions deviate from reality, at the end it would be possible to asses that MLR can be a useful tool for analyzing the relative importance of the driving forces for ACR in churches and similar buildings, as long as the included predictors not are too mutually correlated, and that attained models that are statistically significant also are physically realistic.<br>Church project

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Högbom, Johannes, and August Regnell. "Analysis of Performance Measures affecting the economic success on the PGA Tour using multiple linear regression." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275678.

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This bachelor thesis examined the relationship between performance measures and prize money earnings on the PGA Tour. Using regression analysis and data from seasons 2004 through 2019 retrieved from the PGA Tour website this thesis examined if prize money could be predicted. Starting with 102 covariates, comprehensibly covering all aspects of the game, the model was reduced to 13 with Driving Distance being most prominent, favouring simplicity resulting in an R2Adjusted of 0.6918. The final model was discussed in regard to relevance, reliability and usability. This thesis further analysed how the entry of ShotLink, the technology responsible for the vast statistical database surrounding the PGA Tour, have affected golf in general and the PGA Tour in particular. Analysis regarding how ShotLink affected golf on different levels, both for players as well as other stakeholders, where conducted. These show developments on multiple levels; on how statistics are used, golf related technologies, broadcasts, betting market, and both amateur and PGA Tour playing golf players. The analysis of the latter, using statistics from the PGA Tour website, showed a significant improvement in scoring average since ShotLinks inception.<br>Detta kandidatexamensarbete undersökte relationen mellan prestationsmått och prispengar på PGA Touren. Genom regressionsanalys och data från säsongerna 2004 till och med 2019 hämtat från PGA Tourens hemsida undersökte detta arbete om prispengar kunde predikteras. Startandes med 102 kovariat, täckandes alla aspekter av spelet, reducerades sedan modellen till 13 med Utslags Distans mest framträdande, i förmån för simplicitet och resulterande i ett R2Adj på 0.6918. Den slutliga modellen diskuterades sedan gällande relevans, reliabilitet och användbarhet. Vidare analyserar detta arbete hur ShotLinks entré, tekniken ansvarig för den omfattande statistikdatabasen som omger PGA Touren, har påverkat golf generellt och PGA Touren specifikt. Analyser gällande hur ShotLink har påverkat golf på olika nivåer, både för spelare och andra intressenter, genomfördes. Dessa visar utvecklingar på flera fronter; hur statistik används, golfrelaterade teknologier, mediasändningar, bettingmarknad samt både för amatörspelare och spelare på PGA Touren. Den senare analysen, genom användande av statistik från PGA Tourens hemsida, visade på en signifikant förbättring i genomsnittsscore sedan ShotLink infördes.

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Brandner, Hanna. "Idenitfying the Influential Factors of the Temporal Variation of Water Consumption : A Case Study using Multiple Linear Regression Analysis." Thesis, KTH, Vattendragsteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192650.

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This thesis is a part of the water development project conducted by Svenskt Vatten, which is the Swedish Water and Wastewater Association (SWWA) as well as Tyréns, a consultancy company with offices based in Stockholm, Sweden. Prior to this thesis work, a quality assessment was conducted for some of the locations provided by municipalities in Sweden. This thesis builds upon the revised water consumption data, and also continues to work with validating and modifying the water measurement data in order to proceed with the next step of the water development project, which is to identify any trends in the temporal variation of water consumption. The main objective of this thesis work is to investigate the influence of climatic, time-related and categorical factors on water consumption data collected for different regions in Sweden, and includes a number of different sectors such as residential, industrial and agricultural water user sectors. For the analysis of data, spectral analysis and sinusoidal modelling will be applied in order to find the periodicity of the data, and then simulate the fitted sinusoidal equation to the observed water consumption data for the hourly interval period. Multiple linear regression analysis is then used to assess what independent variables such as climate, time-related and categorical variables can explain the variation in water consumption over hourly and daily periods of time. Spectral analysis identifies high peaks in the spectral density of the data at 12 and 24 hour cycles, for the hourly water consumption data. For the total daily consumption of water, there is a peak at 7 days, which clarifies that there is a weekly pattern occurring throughout the year. The results from the simple linear regression analysis, where the linear relationship between temperature and water consumption was determined, reveals that the water consumption tends to increase within an increasing temperature, where in Lönashult, Alvesta municipality the water demand increased by 5.5% with every 2 ºC rise in temperature, at a threshold of 12 ºC. For Kalix municipality the three areas selected have around 1-2 % increase in water demand with every 2 ºC rise in temperature for the period of May to December. In Gothenburg, areas that were mixed villa areas or areas with summer homes there was a rise of around 2-12 % in water demand, however areas that are situated in the inner city Gothenburg, or that have majority student housing, the water consumption tends to decrease by 2-7% in water demand with every 2 ºC rise in temperature, with a threshold of 12 ºC. In multiple regression analysis, the hourly water consumption results in adjusted R2 values were in the range from 0.58 to 0.87 (58-87%) for the best model approach and therefore has a significant relationship between water consumption and the explanatory variables chosen for this study. For the daily water consumption, the adjusted R2 values were in the range of 0.22-0.83 (22-83%). The adjusted R2 values are lower for certain areas and can be explained by a number of factors, such as the different variables used for the daily water consumption analysis, as variables that explain more the periodicity of the data such as the sinusoidal fitted variable and hourly or night/day changes in consumption are not included. As well as this, not all independent variables such as the climate variables were available or complete for particular time periods, and also errors in the data can lead to a significantly lower R2 value.

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Prudencio, Gerald, Diego Pino, Luis Arauzo, and Carlos Raymundo. "Productivity analysis of LHD equipment using the multiple linear regression method in an underground mine in Peru." International Institute of Informatics and Systemics, IIIS, 2019. http://hdl.handle.net/10757/656294.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>The current study is based on a multiple linear regression analysis with an objective to formulate an equation related to the productivity analysis of LHD equipment using independent variables such as the effective utilization of the equipment. To identify the independent variables, main productive factors, such as the actual capacity of the buckets, the transport cycles in the cleaning process, and the performance by means of curves, were analyzed. Comparisons of a Peruvian underground mine case study exhibited that the battery-powered equipment denoted similar production efficiencies to that exhibited by its diesel counterparts; however, the three-tier approach observed that the battery-powered equipment could achieve production efficiencies that are up to 13.8% more as compared to that achieved using its diesel counterparts because of increased effective utilization that can be attributed to long MTBF. The results of this study exhibit that LHDs under battery-powered storage are feasible for underground mining not only because of the fact that they do not emit any polluting gases, which helps to mitigate pollution, but also because of their good production performance that can be considered to be an important pillar in deep mining. Copyright 2019.

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Buzatoiu, Roxana. "Long Term Forecasting of Industrial Electricity Consumption Data With GRU, LSTM and Multiple Linear Regression." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289632.

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Accurate long-term energy consumption forecasting of industrial entities is of interest to distribution companies as it can potentially help reduce their churn and offer support in decision making when hedging. This thesis work presents different methods to forecast the energy consumption for industrial entities over a long time prediction horizon of 1 year. Notably, it includes experimentations with two variants of the Recurrent Neural Networks, namely Gated Recurrent Unit (GRU) and Long-Short-Term-Memory (LSTM). Their performance is compared against traditional approaches namely Multiple Linear Regression (MLR) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Further on, the investigation focuses on tailoring the Recurrent Neural Network model to improve the performance. The experiments focus on the impact of different model architectures. Secondly, it focuses on testing the effect of time-related feature selection as an additional input to the Recurrent Neural Network (RNN) networks. Specifically, it explored how traditional methods such as Exploratory Data Analysis, Autocorrelation, and Partial Autocorrelation Functions Plots can contribute to the performance of RNN model. The current work shows through an empirical study on three industrial datasets that GRU architecture is a powerful method for the long-term forecasting task which outperforms LSTM on certain scenarios. In comparison to the MLR model, the RNN achieved a reduction in the RMSE between 5% up to to 10%. The most important findings include: (i) GRU architecture outperforms LSTM on industrial energy consumption datasets when compared against a lower number of hidden units. Also, GRU outperforms LSTM on certain datasets, regardless of the choice units number; (ii) RNN variants yield a better accuracy than statistical or regression models; (iii) using ACF and PACF as dicovery tools in the feature selection process is unconclusive and unefficient when aiming for a general model; (iv) using deterministic features (such as day of the year, day of the month) has limited effects on improving the deep learning model’s performance.<br>Noggranna långsiktiga energiprognosprognoser för industriella enheter är av intresse för distributionsföretag eftersom det potentiellt kan bidra till att minska deras churn och erbjuda stöd i beslutsfattandet vid säkring. Detta avhandlingsarbete presenterar olika metoder för att prognostisera energiförbrukningen för industriella enheter under en lång tids förutsägelsehorisont på 1 år. I synnerhet inkluderar det experiment med två varianter av de återkommande neurala nätverken, nämligen GRU och LSTM. Deras prestanda jämförs med traditionella metoder, nämligen MLR och SARIMA. Vidare fokuserar undersökningen på att skräddarsy modellen för återkommande neurala nätverk för att förbättra prestanda. Experimenten fokuserar på effekterna av olika modellarkitekturer. För det andra fokuserar den på att testa effekten av tidsrelaterat funktionsval som en extra ingång till RNN -nätverk. Specifikt undersökte den hur traditionella metoder som Exploratory Data Analysis, Autocorrelation och Partial Autocorrelation Funtions Plots kan bidra till prestanda för RNN -modellen. Det aktuella arbetet visar genom en empirisk studie av tre industriella datamängder att GRU -arkitektur är en kraftfull metod för den långsiktiga prognosuppgiften som överträffar ac LSTM på vissa scenarier. Jämfört med MLR -modellen uppnådde RNN en minskning av RMSE mellan 5 % upp till 10 %. De viktigaste resultaten inkluderar: (i) GRU -arkitekturen överträffar LSTM på datauppsättningar för industriell energiförbrukning jämfört med ett lägre antal dolda enheter. GRU överträffar också LSTM på vissa datauppsättningar, oavsett antalet valenheter; (ii) RNN -varianter ger bättre noggrannhet än statistiska modeller eller regressionsmodeller; (iii) att använda ACF och PACF som verktyg för upptäckt i funktionsvalsprocessen är otydligt och ineffektivt när man siktar på en allmän modell; (iv) att använda deterministiska funktioner (t.ex. årets dag, månadsdagen) har begränsade effekter på att förbättra djupinlärningsmodellens prestanda.

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Books on the topic "Linear multiple stepwise regression analysis"

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On stepwise procedures for some multiple inference problems. Alqvist & Wiksell International, 1989.

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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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Robertson, Rob. Effects of collinearity, sample size, multiple correlation, and predictor-criterion correlation salience on the order of variable entry in stepwise regression. 1997.

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Gelman, Andrew, and Deborah Nolan. Multiple regression and nonlinear models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0010.

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This chapter covers multiple regression and links statistical inference to general topics such as lurking variables that arose earlier. Many examples can be used to illustrate multiple regression, but we have found it useful to come to class prepared with a specific example, with computer output (since our students learn to run the regressions on the computer). We have found it is a good strategy to simply use a regression analysis from some published source (e.g., a social science journal) and go through the model and its interpretation with the class, asking students how the regression results would have to differ in order for the study’s conclusions to change. The chapter includes examples that revisit the simple linear model of height and income, involve the class in models of exam scores, and fit a nonlinear model (for more advanced classes) for golf putting.

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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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Miles, Jeremy. General and generalised linear models. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0017.

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This chapter discusses general and generalised linear models (GLM and GLZ respectively). It outlines GLMs (mean, properties of GLMs and the mean), samples and populations, comparison of two groups of data, multiple regression and the GLM, analysis of variance (ANOVA) and the GLM, GLM in SPSS, and the GLZ).

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Veech, Joseph A. Habitat Ecology and Analysis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829287.001.0001.

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Habitat is crucial to the survival and reproduction of individual organisms as well as persistence of populations. As such, species-habitat relationships have long been studied, particularly in the field of wildlife ecology and to a lesser extent in the more encompassing discipline of ecology. The habitat requirements of a species largely determine its spatial distribution and abundance in nature. One way to recognize and appreciate the over-riding importance of habitat is to consider that a young organism must find and settle into the appropriate type of habitat as one of the first challenges of life. This process can be cast in a probabilistic framework and used to better understand the mechanisms behind habitat preferences and selection. There are at least six distinctly different statistical approaches to conducting a habitat analysis – that is, identifying and quantifying the environmental variables that a species most strongly associates with. These are (1) comparison among group means (e.g., ANOVA), (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, (5) multivariate techniques (Principal Components Analysis and Discriminant Function Analysis), and (6) occupancy modelling. Each of these is lucidly explained and demonstrated by application to a hypothetical dataset. The strengths and weaknesses of each method are discussed. Given the ongoing biodiversity crisis largely caused by habitat destruction, there is a crucial and general need to better characterize and understand the habitat requirements of many different species, particularly those that are threatened and endangered.

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Book chapters on the topic "Linear multiple stepwise regression analysis"

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Zhou, Yiqian, Rehman Qureshi, and Ahmet Sacan. "Analysis of Paired miRNA-mRNA Microarray Expression Data Using a Stepwise Multiple Linear Regression Model." In Bioinformatics Research and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59575-7_6.

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Armstrong, Richard A., and Anthony C. Hilton. "Stepwise Multiple Regression." In Statistical Analysis in Microbiology: Statnotes. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch26.

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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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Vehkalahti, Kimmo, and Brian S. Everitt. "Multiple Linear Regression." In Multivariate Analysis for the Behavioral Sciences. CRC Press, 2018. http://dx.doi.org/10.1201/9781351202275-4.

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Navarra, Antonio, and Valeria Simoncini. "Multiple Linear Regression Methods." In A Guide to Empirical Orthogonal Functions for Climate Data Analysis. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3702-2_8.

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Thrane, Christer. "Linear regression with several independent variables Multiple regression." In Applied Regression Analysis. Routledge, 2019. http://dx.doi.org/10.4324/9780429443756-4.

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Sakawa, Masatoshi, and Hitoshi Yano. "Interactive Decision Making for Multiobjective Fuzzy Linear Regression Analysis." In Multiple Criteria Decision Making. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2666-6_18.

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Ji, Nan, Jincai Chang, and Yuanyuan Luo. "Multiple Linear Regression Analysis Based on Software R." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40618-8_51.

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Cleophas, Ton J., Aeilko H. Zwinderman, and Toine F. Cleophas. "Subgroup Analysis using Multiple Linear Regression: Confounding, Interaction, Synergism." In Statistics Applied to Clinical Trials. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0337-7_9.

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Silva, Jesús, Omar Bonerge Pineda Lezama, and Darwin Solano. "Multiple Linear Regression Analysis of Factors Affecting the Consumption." In Inventive Computation Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33846-6_96.

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Conference papers on the topic "Linear multiple stepwise regression analysis"

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Liu, Pudong, Runhe Shi, Hong Wang, Kaixu Bai, and Wei Gao. "Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model." In SPIE Optical Engineering + Applications, edited by Wei Gao, Ni-Bin Chang, and Jinnian Wang. SPIE, 2014. http://dx.doi.org/10.1117/12.2060530.

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Lan, Yuqing, and Shuhang Guo. "Multiple Stepwise Regression Analysis on Knowledge Evaluation." In 2008 International Conference on Management of e-Commerce and e-Government. IEEE, 2008. http://dx.doi.org/10.1109/icmecg.2008.65.

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Zhou, Yiqian, Rehman Qureshi, and Ahmet Sacan. "Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data." In 2012 7th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2012. http://dx.doi.org/10.1109/hibit.2012.6209046.

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Yiqian Zhou, J. Gerhart, and A. Sacan. "Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112544.

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Xiao, Randong, Jiajia Zhu, Zilong Zhao, Haitao Yu, and Yong Du. "A Passenger Flow Prediction Method for Bus Lines Based on Multiple Stepwise Regression Analysis." In 2021 11th International Conference on Information Science and Technology (ICIST). IEEE, 2021. http://dx.doi.org/10.1109/icist52614.2021.9440559.

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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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"Transmission loss modelling and analysis with multiple linear regression." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.g2.appalasamy.

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Abuella, Mohamed, and Badrul Chowdhury. "Solar power probabilistic forecasting by using multiple linear regression analysis." In SoutheastCon 2015. IEEE, 2015. http://dx.doi.org/10.1109/secon.2015.7132869.

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Ulgen, Toygar, and Gokturk Poyrazoglu. "Predictor Analysis for Electricity Price Forecasting by Multiple Linear Regression." In 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). IEEE, 2020. http://dx.doi.org/10.1109/speedam48782.2020.9161866.

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li, yi, Xiaofei Lu, and Li Zhang. "Precision analysis of atmospheric transmittance based on multiple linear regression." In Fourth Seminar on Novel Optoelectronic Detection Technology and Application, edited by Weiqi Jin and Ye Li. SPIE, 2018. http://dx.doi.org/10.1117/12.2309758.

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