Relevant bibliographies by topics / Multiple regression – minitab

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multiple regression – minitab'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Multiple regression – minitab"

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Jerome, Lawrence. "Multiple linear and non-linear regression in Minitab." MSOR Connections 9, no. 3 (August 2009): 17–20. http://dx.doi.org/10.11120/msor.2009.09030017.

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Arminian, A., M. S. Kang, M. Kozak, S. Houshmand, and P. Mathews. "MULTPATH: A Comprehensive Minitab Program for Computing Path Coefficients and Multiple Regression for Multivariate Analyses." Journal of Crop Improvement 22, no. 1 (May 20, 2008): 82–120. http://dx.doi.org/10.1080/15427520802043182.

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Sagir, Abdu Masanawa, and Saratha Sathasivan. "The use of artificial neural network and multiple linear regressions for stock market forecasting." MATEMATIKA 33, no. 1 (September 20, 2017): 1. http://dx.doi.org/10.11113/matematika.v33.n1.956.

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In the recent economic crises, one of the precise uniqueness that all stock markets have in common is the uncertainty. An attempt was made to forecast future index of the Malaysia Stock Exchange Market using artificial neural network (ANN) model and a traditional forecasting tool – Multiple Linear Regressions (MLR). This paper starts with a brief introduction of stock exchange of Malaysia, an overview of artificial neural network and machine learning models used for prediction. System design and data normalization using MINITAB software were described. Training algorithm, MLR Model and network parameter models were presented. Best training graphs showing the training, validation, test and all regression values were analyzed.
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Giasson, Elvio, Robin Thomas Clarke, Alberto Vasconcellos Inda Junior, Gustavo Henrique Merten, and Carlos Gustavo Tornquist. "Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil." Scientia Agricola 63, no. 3 (June 2006): 262–68. http://dx.doi.org/10.1590/s0103-90162006000300008.

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Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.
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Kondolembang, Ferry. "ANALISIS REGRESI BERGANDA DENGAN METODE STEPWISE PADA DATA HBAT." BAREKENG: Jurnal Ilmu Matematika dan Terapan 5, no. 1 (March 1, 2011): 15–20. http://dx.doi.org/10.30598/barekengvol5iss1pp15-20.

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Multiple regression analysis as a statistical technique that can be used to analyze the relationship between a single dependent (respon) variable and several independent (peredictor) variables. Application for this analysis to be done specially in social economic. HBAT is a manufacture of paper products. Surveys of HBAT customer will be used to application multiple regression analysis in this paper to explain relationship satisfication between the other variables. Methods to selective entering and deleting among these variables until some overall criterion measure is achived. Objective methods for selecting variables that maximizes the prediction while employing the smallest number of variables. Results is the best model from multiple regression analysis is Y = -1.15106 + 0.36900 X6 - 0.41714 X7 + 0.31896 X9 + 0.17435 X11 + 0.77513 X12, means that customer satisfaction is significantly influenced by the complaint resolution, product quality, salesforce image, e-commerce activities, and product line. Besides that the assumptions in multiple regression analysis are met. SAS software has facility more complete than SPSS, Minitab, and R.
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Rosariawari, Firra, and Muhammad Almadhany. "PEMODELAN KEBISINGAN LALU LINTAS BERDASARKAN VOLUME LALU LINTAS MENGGUNAKAN MULTIPLE LINEAR REGRESSION PADA JALAN KEDUNG COWEK SURABAYA." EnviroUS 2, no. 1 (August 16, 2021): 101–5. http://dx.doi.org/10.33005/envirous.v2i1.81.

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Jalan Kedung Cowek merupakan jalan arteri primer yang melayani skala nasional, sehingga mempunyai volume lalu lintas tinggi yang menyebabkan kebisingan lalu lintas. Maka perlu adanya model kebisingan lalu lintas yang dapat memprediksi kebisingan lalu lintas untuk memudahkan dalam upaya pengontrolan kebisingan lalu lintas. Penelitian ini bertujuan untuk memperoleh model kebisingan lalu lintas berdasarkan volume lalu lintas setiap jenis kendaraan. Metode yang dilakukan dalam penelitian adalah dengan survei langsung di Jalan Kedung Cowek untuk memperoleh data tingkat kebisingan dan volume lalu lintas. Analisis data untuk memperoleh pemodelan kebisingan lalu lintas menggunakan Multiple Linear Regression dengan bantuan software Minitab versi 19. Model kebisingan lalu lintas pada Jalan Kedung Cowek yang diperoleh yaitu LEQ = 71,292 + 0,001811 MC + 0,00487 LV – 0,0078 HV dengan nilai koefisien determinasi (R2) sebesar 0,5940. Model tersebut masih belum terlalu baik, karena banyaknya kendaraan yang memakai knalpot tidak standar yang menyebabkan tingkat kebisingan menjadi lebih tinggi.
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Pang, Li Jun, Tan Ji Yu, and Zong Ling Cao. "Effect of the Heat Treatment Process on Machinability of TC4 Alloy." Advanced Materials Research 320 (August 2011): 64–68. http://dx.doi.org/10.4028/www.scientific.net/amr.320.64.

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The orthogonal design approach is adopted to perform the cutting force experiment. It is based on the three typical metallographic phases of TC4 titanium alloy. The multiple-variable linear regression equations of the cutting force are built in term of the main technical parameters using the MINITAB software. Regression analysis is done, and an accurate empirical formula of TC4 cutting force is established. It is also optimized the cutting process parameters. The heat treatment’s technical laws of cutting force of TC4 titanium alloy are further undertaken, which provide a theoretical basis for improving cutting performance and optimizing turning parameter.
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Dhakal, Chuda Prasad. "Multiple Regression Model Fitted for Rice Production Forecasting in Nepal: A Case of Time Series Data." Nepalese Journal of Statistics 2 (September 26, 2018): 89–98. http://dx.doi.org/10.3126/njs.v2i0.21157.

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Background: Fitting a multiple regression model is always challenging and the level of difficulty varies according to the purpose for which it is fitted. Two major difficulties that arise while fitting a multiple regression model for forecasting are selecting 'potential predictors' from numerous possible variables to influence on the forecast variable and investigating the most appropriate model with a subset of the potential predictors.Objective: Purpose of this paper is to demonstrate a procedure adopted while fitting multiple regression model (with an attempt to optimize) for rice production forecasting in Nepal.Materials and Methods: This study has used fifty years (1961-2010) of time series data. A list of twenty-one predictors thought to impact on rice production was scanned based upon past literature, expert's hunches, availability of the data and the researcher's insight which left eleven possible predictors. Further, these possible predictors were subjected to family of automated stepwise methods which left five ‘potential predictors’ namely harvested area, rural population, farm harvest price, male agricultural labor force and, female agricultural labor force. Afterwards, best subset regression was performed in Minitab Version 16 which finally left three 'appropriate predictors' that best fit the model namely harvested area, rural population and farm harvest price.Results: The model fit was significant with p < .001. Also, all the three predictors were found highly significant with p < 0.001. The model was parsimonious which explained 93% variation in rice production with 54% overlapping predictive work done. Forecast error was less than 5%.Conclusion: Multiple regression model can be used in rice production forecasting in the country for the enhanced ease and efficiency.Nepalese Journal of Statistics, Vol. 2, 89-98
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Pechinthorn, Komm, Jirangrug Samarkjarn, and Nico Irawan. "Factors in Controlling the Successful Peer Teaching Method in Higher Education." ELLITE: Journal of English Language, Literature, and Teaching 5, no. 1 (May 31, 2020): 1–7. http://dx.doi.org/10.32528/ellite.v5i1.3126.

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This educational research paper was conducted by using questionnaires to collect the essential student survey data. The source of the study was a chosen mid-sized English for Everyday Use subject of 60 students in a public university under the International College of Rajamangala University Krungthep (ICUTK) in the capital city, Bangkok district in Thailand with the time frame from June to October 2019.The research employed multiple regression equation models by Minitab version 18 to study the structural relationships on satisfactions of peer teaching methodology, meaning that students were required to teach their peers instead of teachers in classroom. The selected variables were gender, GPA, age and attendance of the students. A major contribution of this education research was its significant direct relationship which mediated the method of teacher and student learning by testing the peer teaching methodology in classroom compared to the traditional classroom. Clear descriptive statistics such as mean, standard deviations were provided from the calculation and multiple regressions analysis shows the coefficients, t-stats, errors and significant values of each selected variables. Moreover, the calculation model for those variables was formed for the future prediction for the related educators. The results indicated the importance of peer teaching method in class room for student learning supporting by the highly satisfied scores have impact on all variables included in this research.
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Herlambang, Yudha. "PERHITUNGAN REGRESI LINIER BERGANDA DENGAN METODE LEAST SQUARE DAN ELIMINASI GAUSS DALAM PEMROGRAMAN PASCAL 7.00." EKUITAS (Jurnal Ekonomi dan Keuangan) 3, no. 4 (November 24, 2016): 196. http://dx.doi.org/10.24034/j25485024.y1999.v3.i4.1894.

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Dalam suatu permasalahan yang kita amati tidak jarang antara variabel yang diteliti dengan faktor-faktor yang mempengaruhinya terdapat hubungan saling keterkaitan. Hubungan antara variabel yang diamati dengan faktor-faktor yang mempengaruhinya seringkali dinyatakan dalam bentuk persamaan regressi. Hanya saja tingkat sampai sejauh mana hubungan antara variabel yang diamati dengan faktor-faktor yang mempengaruhinya tadi lebih mudah dinyatakan dalam bentuk persamaan regressi linier. Karena hal-hal yang mempengaruhi terhadap suatu variabel yang diteliti terdiri dari banyak fakor, maka seringkali kita nyatakan dalam bentuk persamaan regressi linier berganda atau multiple regression. Tulisan ini mengemukakan suatu program komputer dalam bahasa Pemrograman Pascal untuk mencari nilai-nilai parameter regressi linier berganda, bila variabel bebas yang mempengaruhi terhadap variabel terikat adalah lebih dari satu, sehingga membentuk persamaan regressi linier multiple. Program komputer yang dirancang di sinis akan diuji validitasnya terhadap software aplikasi statistik yang lain yang telah menjadi standar pemakaian, misalkan Microstat atau Minitab, sehingga bila diberikan input nilai variabel dari permasalahan yang sama dan nantinya dapat diperoleh hasil pemrosesan yang sama atau mendekati sama.
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Dissertations / Theses on the topic "Multiple regression – minitab"

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Palmer, Andrew J. "A novel empirical model of the k-factor for radiowave propagation in Southern Africa for communication planning applications." Thesis, 2003. http://hdl.handle.net/2263/28102.

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The objective of this study was to provide an adequate model of the k-factor for scientific radio planning in South Africa for terrestrial propagation. An extensive literature survey played an essential role in the research and provided verification and confirmation for the novelty of the research on historical grounds. The approach of the research was initially structured around theoretical analysis of existing data, which resulted from the work of J. W. Nel. The search for analytical models was extended further to empirical studies of primary data obtained from the South African Weather Service. The methodology of the research was based on software technology, which provided new tools and opportunities to process data effectively and to visualise the results in an innovative manner by a means of digital terrain maps (DTMs) and spreadsheet graphics. MINITAB
Thesis (PhD)--University of Pretoria, 2005.
Electrical, Electronic and Computer Engineering
unrestricted
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Book chapters on the topic "Multiple regression – minitab"

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Lee, Cheng-Few, John Lee, Jow-Ran Chang, and Tzu Tai. "Multiple Linear Regression." In Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses, 513–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38867-0_15.

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"Multiple Regression Analysis." In Applied Statistical Inference with MINITAB, 257–94. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/b15846-11.

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"More on Multiple Regression." In Applied Statistical Inference with MINITAB, 295–324. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/b15846-12.

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"MULTIPLE LINEAR REGRESSION." In Business and Financial Statistics Using Minitab 12 and Microsoft Excel 97, 234–49. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812384829_0015.

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P. Agbo, Emmanuel. "The Role of Statistical Methods and Tools for Weather Forecasting and Modeling." In Weather Forecasting [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96854.

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The need to understand the role of statistical methods for the forecasting of climatological parameters cannot be trivialized. This study gives an in depth review on the different variations of the Mann-Kendall (M-K) trend test and how they can be applied, regression techniques (Simple and Multiple), the Angstrom-Prescott model for solar radiation, etc. The study then goes ahead to apply some of them with data obtained from the Nigerian Meteorological Agency (NiMet), and applying tools like the python programming language and Wolfram Mathematica. Results show that the maximum ambient temperature for Calabar is increasing (Z = 2.52) significantly after the calculated p-value <0.05 (significant level). The seasonal M-K test was also applied for the dry and wet seasons and both were found to be increasing (Z = 3.23 and Z = 4.04 respectively) after their calculated p-values <0.05. The relationship between refractivity and other meteorological parameters relating to it was discerned using partial differential equations giving the gradient of each with refractivity; this was compared with results from the correlation matrix to show that the water vapor contents of the atmosphere contributes significantly to the variation of refractivity. Multiple linear regression has also been adopted to give an accurate model for the prediction of refractivity in the region after the residual error between the calculated refractivity and predicted refractivity was minimal.
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Nechval, Nicholas A., Konstantin N. Nechval, Maris Purgailis, and Uldis Rozevskis. "Selection of the Best Subset of Variables in Regression and Time Series Models." In Cybernetics and Systems Theory in Management, 303–20. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-668-1.ch016.

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The problem of variable selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables or predictors, but there is uncertainty about which subset to use. Several papers have dealt with various aspects of the problem but it appears that the typical regression user has not benefited appreciably. One reason for the lack of resolution of the problem is the fact that it is has not been well defined. Indeed, it is apparent that there is not a single problem, but rather several problems for which different answers might be appropriate. The intent of this chapter is not to give specific answers but merely to present a new simple multiplicative variable selection criterion based on the parametrically penalized residual sum of squares to address the subset selection problem in multiple linear regression analysis, where the objective is to select a minimal subset of predictor variables without sacrificing any explanatory power. The variables, which optimize this criterion, are chosen to be the best variables. The authors find that the proposed criterion performs consistently well across a wide variety of variable selection problems. Practical utility of this criterion is demonstrated by numerical examples.
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Fokides, Emmanuel, and Apostolos Kostas. "Pre-Service Teachers and Computers." In Handbook of Research on Diverse Teaching Strategies for the Technology-Rich Classroom, 15–31. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0238-9.ch002.

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The chapter presents the results of a longitudinal study (years 2013-2018) in which pre-service teachers' actual and perceived competence in computers were examined, together with factors that were hypothesized to affect both. Participants were 1070 senior students, studying at the Department of Primary School Education, University of the Aegean, Greece, and an online questionnaire was used for recording their views. Results analysis, using multiple linear regression, indicated that pre-service teachers do not actually know a lot about computers, while at the same time, they view themselves as average users. Moreover, ICT courses' impact on actual knowledge and skills in computers was minimal; more significant was the impact of additional ICT training and ownership of both a PC and a laptop. On the other hand, participants based their perceived competence in computers almost exclusively on how competent they thought they were in basic computer use. On the basis of the results, recommendations are made in order pre-service teachers to be more adequately prepared to meet the challenges of using ICT as in-service teachers.
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Conference papers on the topic "Multiple regression – minitab"

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Olmschenk, Greg, Hao Tang, and Zhigang Zhu. "Crowd Counting with Minimal Data Using Generative Adversarial Networks for Multiple Target Regression." In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00131.

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Zhu, Xiaojin, Ji Liu, and Manuel Lopes. "No Learner Left Behind: On the Complexity of Teaching Multiple Learners Simultaneously." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/502.

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We present a theoretical study of algorithmic teaching in the setting where the teacher must use the same training set to teach multiple learners. This problem is a theoretical abstraction of the real-world classroom setting in which the teacher delivers the same lecture to academically diverse students. We define a minimax teaching criterion to guarantee the performance of the worst learner in the class. We prove that the teaching dimension increases with class diversity in general. For the classes of conjugate Bayesian learners and linear regression learners, respectively, we exhibit corresponding minimax teaching set. We then propose a method to enhance teaching by partitioning the class into sections. We present cases where the optimal partition minimizes overall teaching dimension while maintaining the guarantee on all learners. Interestingly, we show personalized education (one learner per section) is not necessarily the optimal partition. Our results generalize algorithmic teaching to multiple learners and offer insight on how to teach large classes.
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Lall, Pradeep, Aniket Shirgaokar, and Jeffrey Suhling. "Models for Component Selection and Thermo-Mechanical Reliability Trade-Offs to Address Component Obsolescence in Military Electronics." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68274.

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Product miniaturization trends in microelectronics industry are driving the need for smaller, faster, more reliable, less expensive IC’s. Area array packages have been increasingly targeted for use in harsh environments such as automotive underhood, military and space applications but system-level decision support and part-selection tools and techniques for thermo-mechanical reliability trade-offs while addressing part obsolescence in extreme environments are scarce. The models presented in this paper provide decision guidance for smart selection and substitution to address component obsolescence by perturbing product designs for minimal risk insertion of new packaging technologies. It is conceivable for commercial off the shelf parts to become unavailable during the production-life of a product. Typical Commercial-of-the-Shelf parts are manufactured for a period of two to four years, and IC manufacturing processes are available for five to six years. It is envisioned that the reliability assessment models will enable turn-key evaluation of geometric architecture, material properties, and operating conditions effects on thermo-mechanical reliability. The presented approach enables the evaluation of qualitative parameter interaction effects, which are often ignored in closed-form modeling, have been incorporated in this work. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages [1, 2], flip-chip packages [3, 4] and ceramic BGA packages [5]. In this paper, principal component regression models (PCR) have been investigated for reliability prediction and part selection of area package architectures under thermo-mechanical loads in conjunction with stepwise regression methods. Package architectures studied include, BGA packages mounted on CU-CORE and NO-CORE printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Solder alloys examined include SnPb and SAC Alloys.
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Vanem, Erik, and Sam-Erik Walker. "Time Series Analysis of Significant Wave Height Data for Identification of Trends in the Ocean Wave Climate." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10024.

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Reliable return period estimates of sea state parameters such as the significant wave height is of great importance in marine structural design and ocean engineering. Hence, time series of significant wave height have been extensively studied in recent years. However, with the possibility of an ongoing change in the global climate, this might influence the ocean wave climate as well and it would be of great interest to analyze long time series to see if any long-term trends can be detected. In this paper, long time series of significant wave height stemming from the ERA-40 reanalysis project, containing 6-hourly data over a period of more than 44 years are investigated with the purpose of identifying long term trends. Different time series analysis methods are employed, i.e. seasonal ARIMA, multiple linear regression, the Theil-Sen estimator and generalized additive models, and the results are discussed. These results are then compared to previous studies; in particular results are compared to a recent study where a spatio-temporal stochastic model was applied to the same data. However, in the current analysis, the spatial dimension has been reduced and spatial minima, mean and maxima have been analysed for temporal trends. Overall, increasing trends in the wave climate have been identified by most of the modelling approaches explored in the paper, although some of the trends are not statistically significant at the 95% level. Based on the results presented in this paper, it may be argued that there is evidence of a roughening trend in the recent ocean wave climate, and more detailed analyses of individual months and seasons indicate that these trends might be mostly due to trends during the winter months.
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Suryani, Lilis. "Birthing Ball Therapy on the Long-Term Maternal and Labor Pain Among Primigravida Mother in Private Practice Midwives, Madiun, East Java." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.03.08.

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ABSTRACT Background: Childbirth is a physiological process which is the process of releasing the product of conception from the uterus. Normal delivery occurs at 37 – 42 weeks of gestation, where the fetus is mature. Labor cannot be separated from labor pain. Several studies have shown that in primitive societies, labor is longer and painful, whereas in advanced societies 7-14% give birth with minimal pain and most (90%) are accompanied by pain and unavoidable pain. The birth ball is a physical therapy ball that helps the delivery of the first stage to help labor progress. A physical therapy ball that helps labor progress and can be used to share positions. One of the movements is to sit on the ball and shake it to comfort and help labor progress. This study aimed to examine the birthing ball therapy on the long-term maternal and labor pain among primigravida mother in private practice midwives, Madiun, East Java. Subjects and Method: This was a cross sectional study conducted in Madiun, East Java. The total of 40 pregnant women were divided into 20 mothers with birthing ball therapy and 20 mothers without birthing ball therapy. The dependent variables were length of the first period of labor and intensity of labor pain. The independent variables were birthing ball therapy. Data were collected using an observation sheet. The data were analyzed using a multiple logistic regression. Results: The use of birth ball was positively associated with the length of the first period of labor (OR = 5.19; 95% CI= 1.06 to 25.4; p = 0.042) and intensity of labor pain (OR = 7.57; 95% CI= 1.53 to 37.3; p = 0.013) and they were statistically significant. Conclusion: The use of birth balls is proven to be effective in shortening stage 1 and reducing pain intensity. Keywords: birthing ball therapy, labor pain, maternal delivery Correspondent: Lilis Suryani. Academy of Midwifery Muhammadiyah Madiun. Jl. Lumbung Life No. 2A Ex. Ngegong Kec. Manguharjo, Madiun City. Email: lsuryani784@gmail.com. Mobile: 08125954726. DOI: https://doi.org/10.26911/the7thicph.03.08
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