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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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Godwin, Harold C., and Uchendu O. Onwurah. "Optimal Production Inventory Policies for Operations: A Case Study of PVC Pipes Production." Advanced Materials Research 824 (September 2013): 536–43. http://dx.doi.org/10.4028/www.scientific.net/amr.824.536.
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This study focuses on solving the problem of overstocking and under stocking of production inventory in manufacturing sector. To ensure effective management of inventory in manufacturing sector, three years production data were gathered and properly analyzed using multiple linear regression analysis and time series forecasting methods. A multiple linear regression model was developed in MINITAB software to make prediction for inventory requirements. From the result, the coefficient of determination (R2) is 1.00, the adjusted R2 is 1.00, F-distribution is 4.212 x 107 which is greater than any value in F-distribution table, and all these show a very strong relationship between the dependent variable and the independent variables. Also, a Time series analysis was done to make forecast of monthly inventory requirements for both raw materials and finished products. Trend analysis and Moving Average method were used in Time series forecasting, and lower Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) were used as criteria for selecting the method that gives the best forecast. From the results obtained, Trend analysis gave MAPE 13% and MAD 2350, while Moving Average gave MAPE 14% and MAD 2574. This work adds to growing body of literatures on data driven inventory management by utilizing historical data in customized software for generation of models that can effectively make forecast of inventory requirements in manufacturing sector. Nomenclature: a = Value of yt at t = 0; b = Trend Value; MA= Moving Average; MAD = Mean Absolute Deviation MAPE =Mean Absolute Percentage Error; N = Number of periods; t = Period Yt = Forecast for period t y = Monthly Quantity of Product Produced α=regression constant β1-βk=Coefficients of the independent variables
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Muhammad, Fadli. "Patterns of Islamic Banks Financing to Increase the Number of Halal Tourism and the Local Generated Income (PAD) Growth In Indonesia." FALAH: Jurnal Ekonomi Syariah 4, no. 1 (March 26, 2019): 10. http://dx.doi.org/10.22219/jes.v4i1.7961.
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This study aims to analyze the relationship of Islamic bank financing to the increase the number of halal tourists and the growth the local generated income (PAD) period 2015-2016 in 33 provinces, Indonesia. Using the research method is granger causality and panel data multiple regression with the Minitab analytical tool. This model analyzes the influence of Islamic bank financing contributions to the number of halal tours and the local generated income. The amount of Islamic banks financing in the tourism sector such as restaurant and lodging business has contributed to increase the number of halal tourists and the growth of the local generated income (PAD). The results of this study can be used for the Islamic banking industry to support capital financing for the halal industry. In previous studies only analyzed the halal tourism potential, as well as the potential of the tourism industry in general for the local generated income. However, this study discusses the contribution of Islamic bank financing to the number of halal tourism and an increase in the local generated income.
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Muhammad, Fadli. "Patterns of Islamic Banks Financing to Increase the Number of Halal Tourism and the Local Generated Income (PAD) Growth In Indonesia." Falah: Jurnal Ekonomi Syariah 4, no. 1 (February 15, 2019): 10. http://dx.doi.org/10.22219/jes.v4i1.8492.
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This study aims to analyze the relationship of Islamic bank financing to the increase the number of halal tourists and the growth the local generated income (PAD) period 2015-2016 in 33 provinces, Indonesia. Using the research method is granger causality and panel data multiple regression with the Minitab analytical tool. This model analyzes the influence of Islamic bank financing contributions to the number of halal tours and the local generated income. The amount of Islamic banks financing in the tourism sector such as restaurant and tourism business has contributed to increase the number of halal tourists and the growth of the local generated income (PAD). The results of this study can be used for the Islamic banking industry to support capital financing for the halal industry. In previous studies only analyzed the halal tourism potential, as well as the potential of the tourism industry in general for the local generated income. However, this study discusses the contribution of Islamic bank financing to the number of halal tourism and an increase in the local generated income.
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Ahmed, Nusrat, Ikramul Hasan, Mohammad Saifuddin, Jakir Ahmed Chowdhury, and Md Selim Reza. "Formulation and Optimization of Carbamazepine Microspheres by 2 Factor 2 Level Central Composite Design." Bangladesh Pharmaceutical Journal 19, no. 2 (August 12, 2016): 152–60. http://dx.doi.org/10.3329/bpj.v19i2.29273.
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The present investigation was designed to prepare controlled release microspheres of carbamazepine using two polymers of different solubility and permeability characteristics, Ethocel standard 45 premium and Eudragit RL 100. The drug release profile was optimized with the aid of design of experiments (DoE). Microspheres of combined polymers were designed according to 22 factorial central composite design (CCD), taking drug loading and polymeric ratio as the independent variables. Total thirteen batches were prepared. The dependent variables were percentage of drug released in 3 hours and 6 hours and mean dissolution time (MDT). The regression parameters of the developed model and graphical interpretation for each response with statistical significance were calculated by using Minitab 17. The relationship between the experimental variables and responses were evaluated by generating response surface plots. Increased amount of Eudragit RL 100 had impact on surface morphology of prepared microspheres. It produced larger holes on the surface due to its higher permeability characteristics. Polynomial mathematical models generated for various response variables using multiple linear regression analysis, were found to be statistically significant (p < 0.05). One optimum formulation (O1) was selected based on USP specification and the second optimum formulation (O2) was selected for the maximization of MDT (hours). Batch O1 showed 22.85 % and 48.78 % drug release after 3 and 6 hours, respectively which were found to be in close agreement with those predicted by the mathematical model. Another optimum formulation, batch O2 showed MDT as 160.61 hours.Bangladesh Pharmaceutical Journal 19(2): 152-160, 2016
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Tharani, Gopalakrishnan, Mohamed Sameem Roshan Akther, and Nanthakumaran Ananthini. "Assessment of Women participation in Agriculture in Vavuniya district, Sri Lanka." International Journal of Social Sciences and Management 3, no. 3 (July 28, 2016): 159–62. http://dx.doi.org/10.3126/ijssm.v3i3.15137.
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An attempt was made to assess the women contribution towards agriculture in Vavuniya district, Sri Lanka. 60 farm family households' women were randomly selected from rural and urban area of Kovilkulam AI region of Vavuniya district in Sri Lanka and the data were collected by constructed questionnaire. The objectives of this study are to identify the factors contributing women participation in agriculture, to identify the constraints faced by the women in participating agriculture and to evaluate the women participation in decision making activity in agriculture. Minitab 15 and MS excel were used for data analysis. The level of women participation in agricultural activities was found out using chi-square test and the factors contributing for women participation in agricultural activities were identified using multiple regression analysis in urban and rural areas separately (α=0.05). The results revealed that 90% of the rural women respondents and 50% of the urban respondents participated in the agricultural activities which is a significant difference. The mean values of women participation in agricultural activities in urban and rural areas were 77 hours and 836 hours per annum respectively. The multiple regression model for women participation in urban area found that the participation of women negatively correlated with status of employment, age and education level (p=0.000). In rural area, age and educational level were negatively influencing on women participation in agricultural activities (p=0.000). R-square values of fitted regression models were 72 % and 91% in urban and rural area respectively 72% and 91% variation in respective women participation were explained by these models. The obstacles for the women in participation in agricultural activities were reported as lack of knowledge and training in agriculture field, family burden, cultural and social barriers and physical constraints. 60% of women from rural areas and 90% of women from urban areas were involved in decision making especially in the selection of crops and varieties for planting and livestock rearing. Enhancing the awareness and the technical knowledge to the women in the field of agriculture would contribute to increase income from agriculture at household level, district level and finally at national level. Int. J. Soc. Sc. Manage. Vol. 3, Issue-3: 159-162
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Wiens, Douglas P. "Robust minimax designs for multiple linear regression." Linear Algebra and its Applications 127 (1990): 327–40. http://dx.doi.org/10.1016/0024-3795(90)90347-f.
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Effendi, Ahmad, Samsul Bakri, and Rusita . "Nilai Ekonomi Jasa Wisata Pulau Tangkil Provinsi Lampung Dengan Pendekatan Metode Biaya Perjalanan." Jurnal Sylva Lestari 3, no. 3 (September 3, 2015): 71. http://dx.doi.org/10.23960/jsl3371-84.
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Tangkil island is one of the attractions in the province of Lampung is a relatively new development and the economic value is not known for many visitors. It is therefore necessary to research with the aim of determining the economic value of tourism services Tangkil Island and determine the effect of the characteristics of the visitors to the economic value of tourism services. Benefits of the research results can be used as a material consideration in determining management policies and the development of this attraction to the next. Research was conducted in December 2014-March 2015 with direct interview using questionnaires to 105 respondents. Determination of the results of the optimization parameters visitors travel costs were then tested by multiple linear regression method, using the software Minitab 16. The results reveal the total cost of the trip visitors Rp37.927.000/visit, the average cost of the trip visitors Rp361,200/person/visit. The economic value of travel services Rp10.888.284.096/year. The variable level of education at the level graduated from high school, the type of work to employers, additional employment, income, dependents in the family, the origin of visitors, visiting times when the Chinese New Year, Christmas and New Year, as well as the way of a visit has a Pvalue<0.1 so that the independent variables these have a significant influence on the economic value of tourism services Tangkil Island. The amount of standard deviation (S) is 77.20, while simultaneously influence the independent variable (R-Sq) is 78.6% and R-Sq (adj) is 70.2%. Keywords: Tangkil Island, cost of travel, economic value of tourism services
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Khoiriah, Anisa Awalul, Samsul Bakri, and Trio Santoso. "Pengaruh Perubahan Lahan, Tingkat Kemiskinan Dan Pendapatan Beberapa Sektor Perekonomian Terhadap Indeks Pembangunan Manusia : Studi Di Provinsi Lampung." Jurnal Sylva Lestari 5, no. 1 (January 26, 2017): 117. http://dx.doi.org/10.23960/jsl15117-127.
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In each region that are transitioning from an agricultural economic activity to industrialeconomic activity has always faced deforestation or forest cover changes in LampungProvince. Changes in forest cover can affect the value of the Human Development Index(HDI). One of the cause of forest decline is a result of poverty sourced by low access to landresources. Furthermore, revenue in the sectors of the economy if hypothesized was alsoinfluenced by changes in the value of the HDI. This study aims to determine the impact ofchanges in forest cover and land, poverty, and income in the economic sectors of the IPM. This study was conducted in September 2015-January 2016, which consists of laboratoryactivity, namely the determination of land cover change in 2002, 2009, and 2013 throughprocessing of satellite imagery data and then continued with field checks. Data of the poverty,income in the economic sectors and IPM obtained through the acquisition of data from theBPS. Multiple linear regression modelling applied to the response variable HDI withexplanatory variables such as changes in forest cover and human welfare indicators.Optimization parameters used Minitab 11. The conclusions of the results of the regressionmodelling showed that the proportion of state forests(p value = 0,037), community forests(pvalue =0,009), fields(p value = 0,040), open land(p value = 0,307), poverty kemiskinan (pvalue = 0,595), disadvantaged families(p value = 0,034)and economic growth(p value =0,146)could decrease the performance of HDI next two years. While the land up(p value =0,675), GDP in the transport sector(p value = 0,002), communications(p value = 0,071),services sector(p value = 0,067)and in others(p value = 0,066)can markedly increase thenumber IPM in Lampung province.Keywords: Forest cover, HDI, Poverty, Sector of Economy.
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Mariam, Susanti, Eddy Supriyono, and Lina Warlina. "STRATEGI BUDIDAYA IKAN KOKI BASTER (Carasius Auratus) RAMAH LINGKUNGAN DALAM UPAYAMENINGKATAN PRODUKSI." Jurnal Matematika Sains dan Teknologi 19, no. 2 (September 12, 2018): 126–37. http://dx.doi.org/10.33830/jmst.v19i2.118.2018.
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Koki baster fish (Carasius auratus) or goldfish, besides as ornamental fish are also used as fish feed and fishing bait which demand tends to increase from year to year. However, from 2012 to 2015, the production of koki baster fish has decreased. This study aims to analyze the application of the management of koki baster fish in Cisaat Subdistrict, Sukabumi Regency, so that a management strategy can be formulated in developing environmentally friendly koki baster in Sukabumi Regency. Data collection was conducted through interviews, observations, questionnaires and documentation. The questionnaires were distributed to 100 respondents who were conducting hatchery for koki baster that scattered in 13 villages in Cisaat Subdistrict. As comparative data, the hatchery of koki baster fish was conducted in the research pond. The variables used are the aquaculture stage (x) consisting of lime dosage, organic fertilizer, anorganic fertilizer, water height, female parent, parent feed, comparison of male and female parent, parent age, larvae feed and duration to number of larvae (y). The variables were analyzed by validity, reliability, multiple regression analysis and principal component analysis using SPSS and Minitab application. Based on the results of multiple regression analysis obtained the relationship between variables in the seeding stage with the number of larvae. Correlation test showed that the variable use of fertilizers had a positive correlation of 69,5% to the number of larvae. Conversely, the variable water height has a negative value on the number of larvae, which is -3.5%. The principal component analysis showed that the variable use of organic fertilizer, anorganic fertilizer and larvae feed was in the first component area. It means that the variable has a large correlation to the number of larvae. The conclusion of there search is the community does not yet have standard in managing fish hatching of koki baster fish, they only rely on experience and habits so that the obtained production is still low. Strategies to increase the production of koki baster fish are need to be standardized on how to farm koki baster fish, standarized koki baster fish and community coaching on the need to carry out the stages of koki baster fish breeding in accordance with the good and environmentally friendly method of fish hatchery.
Ikan koki baster (Carasius auratus) selain sebagai ikan hias juga digunakan sebagai pakan ikan dan umpan pancing yang permintaannnya cenderung meningkat dari tahun ke tahun. Namun demikian dari tahun 2012 hingga 2015, produksi ikan baster mengalami penurunan. Penelitian ini bertujuan menganalisa penerapan manajemen usaha budidaya ikan koki baster di Kecamatan Cisaat Kabupaten Sukabumi sehingga dapat dirumuskan strategi pengelolaan dalam pengembangan budidaya ikan koki baster di Kabupaten Sukabumi yang ramah lingkungan. Pengumpulan data dilakukan melalui wawancara, pengamatan, kuesioner dan dokumentasi. Instrumen yang digunakan adalah kuesioner yang dibagikan kepada 100 orang responden yang melakukan kegiatan pembenihan ikan koki baster di 13 desa yang tersebar di Kecamatan Cisaat. Sebagai data pembanding dilakukan kegiatan pembenihan ikan koki baster di kolam penelitian. Variabel yang digunakan dalam analisis keterkaitan produksi adalah tahapan budidaya (x) yang terdiri atas dosis kapur, pupuk organik, pupuk anorganik, tinggi air, induk betina, pakan induk, perbandingan induk jantan dan betina, umur induk, pakan larva, dan lama pemeliharaan terhadap jumlah larva (y). Variabel-variabel tersebut dianalisis dengan regresi berganda dan analisa komponen utama menggunakan aplikasi SPSS dan Minitab yang sebelumnya dilakukan uji validitas dan reliabilitas. Berdasarkan hasil analisa regresi berganda diperoleh keterkaitan antara varibael-variabel dalam tahapan pembenihan dengan jumlah larva. Uji korelasi menunjukan variabel penggunaan pupuk berkorelasi positif sebesar 69,5% terhadap jumlah larva. Sebaliknya variabel tinggi air memiliki nilai korelasi negatif terhadap jumlah larva yaitu sebesar -3,5%. Analisa komponen utama menunjukan bahwa variabel penggunaan pupuk organik, pupuk anorganik dan pakan larva berada pada wilayah first component artinya variabel tersebut memiliki korelasi yang besar terhadap jumlah larva. Kesimpulan dari penelitian ini adalah masyarakat belum memiliki standar dalam melaksanakan pengelolaan pembenihan ikan koki baster, hanya mengandalkan pengalaman dan kebiasaan saja sehingga produksi yang diperoleh masih rendah. Strategi untuk meningkatkan produksi ikan koki baster di masyarakat, perlu adanya standar tentang cara budidaya ikan koki baster, standardisasi ikan koki baster dan pembinaan kepada masyarakat tentang perlunya melaksanakan tahapan-tahapan budidaya ikan koki baster sesuai dengan cara pembenihan ikan yang baik dan ramah lingkungan.
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Naiman, Daniel Q. "Minimax Regret Simultaneous Confidence Bands for Multiple Regression Functions." Journal of the American Statistical Association 82, no. 399 (September 1987): 894–901. http://dx.doi.org/10.1080/01621459.1987.10478514.
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Woghiren, Charles O., and F. P. Brennan. "The Influence of Longitudinal Stiffeners on Weld Toe Stress Concentrations in Multi Planar Tubular KK Joints." Key Engineering Materials 348-349 (September 2007): 409–12. http://dx.doi.org/10.4028/www.scientific.net/kem.348-349.409.
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This paper reports a parametric stress analysis of various configurations of rack plate stiffened multi-planar welded KK joints using the finite element method. The KK joint finds application in the leg structure of offshore Oil & Gas jack-up platforms. The rack plate is a dual purpose element of the joint because it firstly functions as a stiffener which reduces the stress concentration at the brace/chord intersection. This could be an immense contribution to the increase in fatigue life of the joint but other hot spot sites are introduced to the joint. The rack is also used for raising and lowering of the jack-up hull which gives the jack-up platform its jacking capability. Over 120 models using a combination of shell and solid elements were built and analysed within ABAQUS. Non-dimensional joint geometric parameters; β, γ and . were employed in the study with . being defined as the ratio of rack thickness to chord diameter. Stress Concentration Factors (SCFs) were calculated under applied axial and OPB (out-of-plane-bending) loading. Three critical SCF locations were identified for each load case, with each location becoming the most critical based on the combination of the non-dimensional parameters selected for the joint. This is important as careful design can shift the critical SCF from an area inaccessible to NDT to one that can be easily inspected. The SCF values extracted from the models were used to derive six parametric equations through multiple regression analysis performed using MINITAB. The equations describe the SCF at the different locations as a function of the non-dimensional ratios. The equations not only allow the rapid optimisation of multi-planar joints but also can be used to quickly identify the location of maximum stress concentration and hence the likely position of fatigue cracks. This in itself is an invaluable tool for planning NDT procedures and schedules.
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Prenada, Ade, Samsul Bakri, and Susni Herwanti. "Penilaian Jasa Wisata Kebun Binatang Bumi Kedaton Resort Di Bandar Lampung Dengan Pendekatan Metode Biaya Perjalanan." Jurnal Sylva Lestari 5, no. 2 (May 23, 2017): 102. http://dx.doi.org/10.23960/jsl25102-112.
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Tourism object Bumi Kedaton Resort (BKR) is one of the tourist attractions that can be found in the Province of Lampung and opened to the public in 2009 so that the unknown diversity of attitude necessary for a visitor management strategy from BKR to be able to attract visitors because of competition in the world tourist attractions then increasingly need to give satisfaction to the visitor so that visitors wishing to come back. Therefore need to do research to find out the value of tourist services BKR and economic characteristics of visitors. Economic value of tourism services can be found using the method of travel expenses. The research was carried out in December 2014-February 2015 by doing the interview and questionnaire distributed directly to the respondent as much as 99 people. Sampling of respondents do with inccidental sampling methods respondents who happened to be there on site research with sampling techniques in a non random sampling because not all individuals in the population earn the same opportunities to provide samples. Sampling is done starting at 09:00am-4:00 pm WIB each day and by the time the school holidays, christmas, new year and chinese new year. The results of the determination of the cost of travel of visitors then tested with the method of multiple linear regression using software Minitab 16. The research results showed that travel costs averaged visitors amounted to Rp. 109.176,26/people/visits for all zoning are examined. Calculation based on research data obtained economic values of tourism services BKR is Rp. 24.559.199,69/year. Characteristics that give visitors a real influence against the economic value of tourism services BKR i.e. time visiting when the Sunday school holiday, Chinese new year, christmas and new year, as well as the distance that has value P Value < 0.1. The influence of the dependent variables simultaneously (R-Sq) was 96,6% and R-Sq (adj) is 95,4%.Keywords: Demographic, economic value, tourism services, travel cost.
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Zouaoui, Maroua, Aude Simongiovanni, and Marie-Pierre Létourneau-Montminy. "206 Meta-analysis of the response of piglets to dietary valine: impact of other branched chain amino acids." Journal of Animal Science 98, Supplement_3 (November 2, 2020): 18. http://dx.doi.org/10.1093/jas/skaa054.030.
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Abstract Branched chain amino acids (BCAA), valine (Val), isoleucine (Ile) and leucine (Leu) share the same metabolic pathways. An excess of Leu stimulates the catabolism of Val and Ile that may lead to a decrease in piglet growth performance. The objective of this study is to quantify the response of piglets to dietary Val and the influence of Leu and Ile on this response through a meta-analysis approach. A total of 16 articles published between 2001 and 2018, including 23 experiments and 126 treatments were used. Multiple regression models were fitted with the MIXED procedure of Minitab software with the random effect of the experiment. The Y variables were Average Daily Gain (ADG), Average Daily Feed Intake (ADFI) and Feed Conversion Ratio (FCR). The main X variable was the Standardized Ileal Digestible Val (ValSID) and the other ones were Leu (LeuSID) and Ile (IleSID). The response of ADG, ADFI and FCR to ValSID was curvilinear (P< 0.001: ADG, R2 = 0.93%; ADFI, R2= 0.97%; FCR, R2=0.93%). Results showed that increasing dietary LeuSID reduced ADG and ADFI (P< 0.05) but also that the response of piglets to ValSID was stronger in high LeuSID diet (P < 0.05; Interaction ValSIDxLeuSID; ADG and ADFI). Based on these models, increasing dietary ValSID from 7 to 8.5g/kg generates in wheat-based diets (10 g /kg of LeuSID) an improvement of ADG of 4.7% and ADFI of 2.5% compared to 7.4% for ADG and 5.2% for ADFI in corn-based diets (14 g/ kg of LeuSID). The response of ADG, ADFI and FCR to ValSID was not modified by IleSID. This study showed that ADG, ADFI and FCR are improved with increasing dietary Val and this effect was modulated by dietary Leu content except for FCR. Results can help piglet nutritionists to optimize dietary Val levels based on other BCAA content.
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Létourneau-Montminy, Marie-Pierre, and Theophane de Rauglaudre. "PSIX-18 Prediction of urinary P excretion as a tool to assess mineral status of growing pigs." Journal of Animal Science 98, Supplement_3 (November 2, 2020): 182–83. http://dx.doi.org/10.1093/jas/skaa054.323.
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Abstract Urinary losses of phosphorus (P) and calcium (Ca) are indicators of absorbed compared to amount needed for tissue growth as well as their balance for bone deposition. They can thus be a good indicator of the mineral status. This hypothesis has been validated through meta-analysis using a database of P and Ca retention in growing pigs including 43 publications published between 1969 and 2018 for a total of 51 experiments and 251 dietary treatments. P urinary excretion has been simulated using a multiple regression analysis with the proc MIXED (Minitab® 19) with trial effect as random. Average body weight of the pigs was 32kg ±18kg. Urinary P losses (urineP) was influenced by apparent digestible P (digP, recalculated for each treatment; g/kg), total Ca (Ca, g/kg), and microbial phytase (PhytM, FTU/kg). The model created explains 90% of urineP (R2 = 91%). UrineP was increased with digP from about 2 g/kg of digP (digP, P = 0.02, digP x digP, P < 0.001), before this point it was very low. Increasing dietary Ca decreased urineP linearly when exceeding the 2 g/kg of digP (Ca x digP, P < 0.001); P can then be fixed into bone as hydroxyapatite. The contribution of P is thus to manage in conjunction with dietary Ca. PhytM effect depends of both Ca and P (PhytM x Ca x digP; P = 0.03) showing that phytM addition reduces urineP depending of digP and Ca. As an example, in 6 g Ca/kg diet, 500 FTU/kg addition reduce urineP by 28% in 2 g digP/kg and by only 4% in 4 g digP g/kg. This is due to a better retention of P into bone in low digP. Next step will be urinary Ca modeling and then looking at meaning of their ratio in relation to mineral status to develop a practical tool to assess Ca and P status of pig.
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Cadre, Benoît, Nicolas Klutchnikoff, and Gaspar Massiot. "Minimax regression estimation for Poisson coprocess." ESAIM: Probability and Statistics 21 (2017): 138–58. http://dx.doi.org/10.1051/ps/2017004.
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For a Poisson point process X, Itô’s famous chaos expansion implies that every square integrable regression function r with covariate X can be decomposed as a sum of multiple stochastic integrals called chaos. In this paper, we consider the case where r can be decomposed as a sum of δ chaos. In the spirit of Cadre and Truquet [ESAIM: PS 19 (2015) 251–267], we introduce a semiparametric estimate of r based on i.i.d. copies of the data. We investigate the asymptotic minimax properties of our estimator when δ is known. We also propose an adaptive procedure when δ is unknown.
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Chu, Wei, and S. Sathiya Keerthi. "Support Vector Ordinal Regression." Neural Computation 19, no. 3 (March 2007): 792–815. http://dx.doi.org/10.1162/neco.2007.19.3.792.
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In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal optimization algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and real-world data sets, including applications of ordinal regression to information retrieval, verify the usefulness of these approaches.
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Walker, Lisa A. S., David Marino, Jason A. Berard, Anthony Feinstein, Sarah A. Morrow, and Denis Cousineau. "Canadian Normative Data for Minimal Assessment of Cognitive Function in Multiple Sclerosis." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 44, no. 5 (July 7, 2017): 547–55. http://dx.doi.org/10.1017/cjn.2017.199.
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AbstractObjective: The Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) is a consensus-based collection of neuropsychological tests that evaluate cognitive functioning in individuals with multiple sclerosis (MS). The tests are typically scored using each respective published test manual, leaving the examiner to make interpretations from norms derived from different American populations. Given demographic differences, this may lead to misinterpretation of findings in Canadians. Our goal was to establish both discrete and regression-based normative data for the MACFIMS based on a largely co-normed Canadian population to allow for improved psychometric interpretation. Methods: MACFIMS data sets were aggregated from across three different Canadian cities (Ottawa, Toronto, and London), yielding a total of 330 healthy control participants from four different studies evaluating cognition in individuals with MS. Given the variety of contributing studies, there was variability in terms of the number of participants completing each measure. Results: Both age-based discrete normative data and demographically adjusted (sex, age, and education) regression-based formulae were established. The demographic variables varied in their contribution to each MACFIMS test in the regression models, predicting 0 to 18% of the variance. Conclusions: Provision of these regression-based formulae will allow for more accurate interpretation of Canadian-derived MACFIMS scores by allowing clinicians to correct for all relevant demographic variables simultaneously, leading to improved clinical decision making for individuals with multiple sclerosis.
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F. J, Ogbozige, Toko M. A, and Arawo C.C. "Multiple Linear Regression (MLR) Model: A Tool for Water Quality Interpretation." Momona Ethiopian Journal of Science 12, no. 1 (April 30, 2020): 123–34. http://dx.doi.org/10.4314/mejs.v12i1.8.
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The lack of standard water analysis equipment as well as inadequate trained personnel especially in the developing countries has discouraged many researchers in such countries to execute water quality researches. Hence, this paper presents developed mathematical relationship among some physicochemical parameters in order to aid the determination of the concentrations of certain parameters with the use of minimal equipment. This was achieved by weekly analyzing 7 physicochemical parameters of two sources of potable water (tap water and borehole water) stored in different containers for a period of 6 weeks using standard methods. The storage containers used were black plastic tank, blue plastic tank, green plastic tank, coated steel metal tank, uncoated steel metal tank and clay pot. The parameters examined were turbidity, electrical conductivity (EC), pH, alkalinity, chloride ion (Cl-), dissolved oxygen (DO) and total hardness. Results showed that the relationship between electrical conductivity (EC), alkalinity (Alk), total hardness (TH) and chloride ion (Cl-) is given as; EC = -224.8066493 + 6.244028022(Alk) + 0.28204735(TH) + 0.000518108(Cl-). A programing language was written on the models using Visual Basic.Net (VB.Net) version 2018.
Keywords: Water, Physicochemical, Parameters, Function, Equation.
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Azen, Razia, and David V. Budescu. "Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis." Journal of Educational and Behavioral Statistics 31, no. 2 (June 2006): 157–80. http://dx.doi.org/10.3102/10769986031002157.
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Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R2 contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria for an appropriate generalization of R2 to the case of multiple response variables. The DA results obtained by univariate regression (with each criterion separately) are analytically compared with results obtained by multivariate DA and illustrated with an example. It is shown that univariate dominance does not necessarily imply multivariate dominance (and vice versa), and it is recommended that researchers who wish to account for the correlation among the response variables use multivariate DA to determine the relative importance of predictors.
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PARMENTER, BRETT A., S. MARC TESTA, DAVID J. SCHRETLEN, BIANCA WEINSTOCK-GUTTMAN, and RALPH H. B. BENEDICT. "The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS)." Journal of the International Neuropsychological Society 16, no. 1 (October 2, 2009): 6–16. http://dx.doi.org/10.1017/s1355617709990750.
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AbstractThe Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) is a consensus neuropsychological battery with established reliability and validity. One of the difficulties in implementing the MACFIMS in clinical settings is the reliance on manualized norms from disparate sources. In this study, we derived regression-based norms for the MACFIMS, using a unique data set to control for standard demographic variables (i.e., age, age2, sex, education). Multiple sclerosis (MS) patients (n = 395) and healthy volunteers (n = 100) did not differ in age, level of education, sex, or race. Multiple regression analyses were conducted on the performance of the healthy adults, and the resulting models were used to predict MS performance on the MACFIMS battery. This regression-based approach identified higher rates of impairment than manualized norms for many of the MACFIMS measures. These findings suggest that there are advantages to developing new norms from a single sample using the regression-based approach. We conclude that the regression-based norms presented here provide a valid alternative to identifying cognitive impairment as measured by the MACFIMS. (JINS, 2010, 16, 6–16.)
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Sim, Siong Fong, Min Xuan Laura Chai, and Amelia Laccy Jeffrey Kimura. "Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)." Journal of Chemistry 2018 (November 8, 2018): 1–8. http://dx.doi.org/10.1155/2018/7182801.
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Fourier-transform infrared (FTIR) offers the advantages of rapid analysis with minimal sample preparation. FTIR in combination with multivariate approach, particularly partial least squares regression (PLSR), has been widely used for adulterant analysis. Limited study has been done to compare PLSR with other regression strategies. In this paper, we apply simple linear regression (SLR), multiple linear regression (MLR), and PLSR for prediction of lard in palm olein oil. Pure palm olein oil was adulterated with lard at different concentrations and subjected to analysis with FTIR. The marker bands distinguishing lard and palm olein oil were determined using Fisher’s weights. The marker regions were then subjected to regression analysis with the models verified based on 100 training/test sets. The prediction performance was measured based on the percentage root mean square error (%RMSE). The absorption bands at 3006 cm−1, 2852 cm−1, 1117 cm−1, 1236 cm−1, and 1159 cm−1 were identified as the marker bands. The bands at 3006 and 1117 cm−1 were found with satisfactory predictive ability, with PLSR demonstrating better prediction yielding %RMSE of 16.03 and 13.26%, respectively.
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S. Al-Shamery, Eman, and Hussein A. Al – Gashamy. "Enhanced Evolutionary Sequential Minimal Optimization Model for Inflation Prediction." International Journal of Engineering & Technology 7, no. 4.19 (November 27, 2018): 788. http://dx.doi.org/10.14419/ijet.v7i4.19.27998.
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The control of inflation rate is at the core of monetary policy making. Therefore, there is very great interest in reliable inflation forecasts by central bankers to help them achieve this aim. The aim of this investigation has been to forecast inflation in case of the United States as accurately as possible. This paper proposes a new forecasting model called Sequential Minimal Organization (SMOreg-3passes) for regression predictions. SMOreg-3passes consists of four steps, they are technical indicators generation, feature selection, normalization regression and regression forecaster. The proposed model evaluated using two regression measurements (Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)). Our evidence from the SMOreg-3passes model suggests that the chronology of time series has great influence on future forecasting and the error in forecasting the past has an exponential impact on the current data. The results showed that the proposed model outperformed the traditional SMO and Multiple Layer Perception (MLP) methods.
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Salleh, Faridah Hani Mohamed, Suhaila Zainudin, and Shereena M. Arif. "Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems." Advances in Bioinformatics 2017 (January 29, 2017): 1–14. http://dx.doi.org/10.1155/2017/4827171.
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Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
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Kalajdzija, Natasa, Sanja Podunavac-Kuzmanovic, Dragoljub Cvetkovic, Lidija Jevric, and Strahinja Kovacevic. "Application of multiple linear regression analysis to predict antifungal activity of some benzimidazole derivatives using ADME parameters." Acta Periodica Technologica, no. 44 (2013): 239–47. http://dx.doi.org/10.2298/apt1344239k.
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In this study we were investigated the relationship between the antifungal activity of some benzimidazole derivatives and some absorption, distribution, metabolism and excretion (ADME) parameters. The antifungal activity of studied compounds against Saccharomyces cerevisiae was expressed as the minimal inhibitory concentration (MIC). A statistically significant quantitative structure-activity relationship (QSAR) model for predicting antifungal activity of the investigated benzimidazole derivatives against Saccharomyces cerevisiae was obtained by multiple linear regression (MLR) using ADME parameters. The quality of the MLR model was validated by the leave-one-out (LOO) technique, as well as by the calculation of the statistical parameters for the developed model, and the results are discussed based on the statistical data.
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Saleh Hussein, Ameer, Rihab Salah Khairy, Shaima Miqdad Mohamed Najeeb, and Haider Th Salim Alrikabi. "Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 05 (March 16, 2021): 24. http://dx.doi.org/10.3991/ijim.v15i05.17173.
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<p>The global online communication channel made possible with the internet has increased credit card fraud leading to huge loss of monetary fund in their billions annually for consumers and financial institutions. The fraudsters constantly devise new strategy to perpetrate illegal transactions. As such, innovative detection systems in combating fraud are imperative to curb these losses. This paper presents the combination of multiple classifiers through stacking ensemble technique for credit card fraud detection. The fuzzy-rough nearest neighbor (FRNN) and sequential minimal optimization (SMO) are employed as base classifiers. Their combined prediction becomes data input for the meta-classifier, which is logistic regression (LR) resulting in a final predictive outcome for improved detection. Simulation results compared with seven other algorithms affirms that ensemble model can adequately detect credit card fraud with detection rates of 84.90% and 76.30%.</p>
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Al-Saleh, J., M. P. Abi Saab, A. Negm, F. Balushi, R. Namas, and N. Ziade. "AB0666 A COMPARISON OF CLINICAL FEATURES AND PREDICTORS OF TREATMENT RESPONSE IN SPONDYLOARTHRITIS PATIENTS IN THE MIDDLE EAST: A CROSS-SECTIONAL MULTINATIONAL STUDY." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1628.1–1628. http://dx.doi.org/10.1136/annrheumdis-2020-eular.2493.
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Background:Spondylarthritis it is a chronic inflammatory disease with heterogenous clinical features. Its prevalence ranges between 0.2%-2%. Over the years biological therapy has improved work productivity and activity impairment in people with SpA. Unlike in rheumatoid arthritis, the concept of treat-to-target is still debatable among rheumatologist. However, there is a consensus that treatment in patient with SpA should be personalized. There are several challenges in the Middle East that might affect providing personalized medicine to patients with SpA in this region.Objectives:The of objective of the study is to explore factors that interfere with achieving clinical targets in patients with SpA clinical practice in the Middle East.Methods:We conducted a cross-sectional, multicentre study to explore the factors that interfere with achieving clinical targets in SpA patients from four countries in the Middle East (Lebanon, Oman, Qatar, and the United Arab Emirates). A total of 404 patients who attended participating centers from January 2019 to June 2019 and who met the ASAS 2010 classification criteria for axial and peripheral SpA; and were at least 18 years of age were enrolled in the study. We excluded patients with peripheral arthritis only. We extracted demographics, clinical data, and conducted patients survey. We used Compliance Questionnaire for Rheumatology (CQR) is a self-reported adherence measure created specifically for and validated in rheumatic diseases.Demographic data and disease and treatment characteristics were described as median and the 25th–75th interquartile range (IQR). Multiple regression analysis was used to investigate the impact of different factors on ASDAS-CRP in patients with SpA. Statistical analysis was performed using Minitab version 18.1 software.Results:A total of 404 patients initially enrolled in the study, we excluded 95 patients as they had peripheral involvement only. We analysed the data of 309 patients with axial only or axial and peripheral SpA. There median age was 43 years and 53.7% were females. The median disease duration was six years. At the time of the study, 72.1% patients were within the arbitrary clinical target of ASDAS < 2.1. Detail description of the studied population and subgroups outlined in table 1.Enthesitis (OR: 2.9; P value: 0.004), Psoriasis (OR: 2.74; P value: 0.007), low compliance score (OR: -4.36; P value: < 0.0001) and HLA B27 (OR: 2.12; P value: < 0.04) were independent predictors of a higher ASDAS –CRP.Conclusion:Enthesitis, psoriasis, noncompliance, and HLA B27 were independent predictors for ASDAS in our cohort.Table 1.Demographic and clinical characteristics of all patients and for achiever and non-achieversVariablesAll patients (309)Achiever (223)Non-achieves (86)Age, Median, (IQR) yrs43, (36-51)43, (35-51.5)42, (37-51)Female %53.7%54.2%51.9%Disease Duration, Median, (IQR)yrs6, (3-9)6, (2-8)7.5, (3-10)Patient has medical insurance/ Medical coverage94.5%95.0%93.1%Smoking13.9%12.1%18.6%ASDAS-CRP, Median (IQR)1.56, (1.24-2.1)1.56, (1.07-1.6)2.75, (2.36-3.3)Arthritis40.1%36.3%48.1%Dactylitis13.6%11.3%18.50%Enthesitis29.1%22.4%43.2%Family history of SPA18.4%14.3%25.9%Good response to NSAIDs21.7%18.8%22.2%HLA B2730.0%25.6%40.0%Inflammatory bowel disease7.1%7.3%6.20%Inflammatory low back pain68.6%68.1%69.8%Onycholysis10.9%10.7%11.1%Psoriasis39.4%25.1%40.7%Sacroiliitis (Radiographic)50.4%49.8%51.9%Uveitis6.1%4.0%11.1%SpA- classificationAxial49.8%55.7%35.6%Axial & peripheral50.2%44.3%64.4%Acknowledgments:ArLAR 2018 Scientific committee for initiating SpA special interest group meetingDisclosure of Interests:Jamal Al-Saleh Grant/research support from: Novartis, AbbVie, Majid Philippe Abi Saab: None declared, Ahmed Negm Speakers bureau: El-lilly, Farida Balushi: None declared, Rajaie Namas: None declared, Nelly Ziade Speakers bureau: Abbvie, Janssen, Lilly, Novartis, Pfizer, Roche, Sanofi
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Janicijevic, Danica, Ivan Jukic, Jonathon Weakley, and Amador García-Ramos. "Bench Press 1-Repetition Maximum Estimation Through the Individualized Load–Velocity Relationship: Comparison of Different Regression Models and Minimal Velocity Thresholds." International Journal of Sports Physiology and Performance 16, no. 8 (August 1, 2021): 1074–81. http://dx.doi.org/10.1123/ijspp.2020-0312.
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Purpose: To compare the accuracy of nine 1-repetition maximum (1RM) prediction methods during the paused and touch-and-go bench press exercises performed in a Smith machine. Method: A total of 86 men performed 2 identical sessions (incremental loading test until reaching the 1RM followed by a set to failure) in a randomized order during the paused and touch-and-go bench press exercises. Individualized load–velocity relationships were modeled by linear and polynomial regression models considering 4 loads (45%–60%–75%–90% of 1RM) (multiple-point methods) and considering only 2 loads (45%–90% of 1RM) by a linear regression (2-point method). Three minimal velocity thresholds were used: the general velocity of 0.17 m·s−1 (general velocity of the 1RM [V1RM]), the velocity obtained when lifting the 1RM load (individual V1RM), and the velocity obtained during the last repetition of a set to failure. Results: The 1RM prediction methods were generally valid (range: r = .96–.99, standard error of the estimate = 2.8–4.9 kg or 4.6%–8.0% of 1RM). The multiple-point linear method (2.79 [2.29] kg) was more precise than the multiple-point polynomial method (3.54 [3.31] kg; P = .013), but no significant differences were observed when compared with the 2-point method (3.09 [2.66] kg, P = .136). The velocity of the last repetition of a set to failure (3.47 [2.97] kg) was significantly less precise than the individual V1RM (2.91 [2.75] kg, P = .009) and general V1RM (3.00 [2.65] kg, P = .010). Conclusions: Linear regression models and a general minimal velocity threshold of 0.17 m·s−1 should be recommended to obtain a quick and precise estimation of the 1RM during the bench press exercise performed in a Smith machine.
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Feldmeyer, Laurence, Ildiko Szeverényi, Michèle Mandallaz, E. Birgit Lane, and Daniel Hohl. "Late-Onset Multiple Self-Healing Squamous Epithelioma Ferguson-Smith Recurrence Induced by Radiotherapy." Case Reports in Dermatology 8, no. 3 (December 1, 2016): 344–49. http://dx.doi.org/10.1159/000447481.
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We report the case of a woman in her 60s with confirmed multiple self-healing squamous epitheliomas (MSSE) Ferguson-Smith. After recurrences following surgery and radiotherapy, the patient was successfully treated with minimal surgical intervention combined with intralesional injections of triamcinolone acetate. The histological comparison between mature and regressed keratoacanthomas (KA) revealed an increased inflammatory infiltrate with numerous plasmacytoid dendritic cells in the regressed KA in comparison to the mature one, speaking in favor of an inflammation-mediated regression process. Corticosteroids injection in MSSE may have paradoxical effects with action on the proliferation phase rather than the inflammatory regression phase of the KA. Our case confirms previous reports showing that radiotherapy may exacerbate MSSE and should be avoided. Intralesional triamcinolone acetate injection is a safe and easy to use method also effective for multiple lesions. Our case underlines the difference between squamous cell carcinoma and KA, responding differently to therapies like imiquimod or radiotherapy.
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Mojid, Mohammad Abdul, and A. B. M. Zahid Hossain. "Comparative performance of multiple linear regression and artificial neural network models in estimating solute-transport parameters." SAINS TANAH - Journal of Soil Science and Agroclimatology 18, no. 1 (June 30, 2021): 27. http://dx.doi.org/10.20961/stjssa.v18i1.49207.
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Indirect estimate of solute-transport parameters through pedo-transfer functions (PTFs) is becoming important due to expensive and time-consuming direct measurement of the parameters for a large number of soils and solutes. This study evaluated the relative performance of PTFs of multiple linear regression (MLR) and Artificial Neural Network (ANN) models in predicting velocity (<em>V</em>), dispersion coefficient (<em>D</em>) and retardation factor (<em>R</em>) of CaCl<sub>2</sub>, NaAsO<sub>2</sub>, Cd(NO<sub>3</sub>)<sub>2</sub>, Pb(NO<sub>3</sub>)<sub>2</sub> and C<sub>9</sub>H<sub>9</sub>N<sub>3</sub>O<sub>2</sub> (carbendazim) in five agricultural soils. <em>V</em>, <em>D</em> and <em>R</em> of the solutes were determined in repacked soil columns under steady-state unsaturated water flow conditions. Textural class, particle size distribution, bulk density, organic carbon, relative pH, clay%, grain size, and uniformity coefficient of the soils were determined. MLR and ANN models were calibrated with the measured data of four soils and verified for another soil. Root-Mean Square Error (RMSE) is significantly smaller (0.015) and modelling efficiency (EF) is significantly larger (0.999) for ANN model than those (0.096 and 0.954, respectively) for MLR model. Negative Mean Absolute Error (MAE) (-0.0002) of MLR model indicates overestimation, while positive MAE (0.00003) of ANN model indicates minimal underestimation. The ANN model is less biased than the MLR model during prediction. Thus, the ANN model can significantly enhance pollution transport prediction through soils with good accuracy.
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Andersson, T., L. Kihlström, and M. Söderman. "Regression of a Flow-Related Ophthalmic Artery Aneurysm after Treatment of a Frontal DAVS." Interventional Neuroradiology 10, no. 3 (September 2004): 265–68. http://dx.doi.org/10.1177/159101990401000310.
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We report a case of a frontal dural arteriovenous shunt or fistula (DAVS) adjacent to the left side of the cribriform plate, with bilateral supply from multiple arteries, the most prominent being the dural branches originating from the anterior ethmoidal artery coming from the left ophthalmic artery. Before treatment there was an eight mm flow-related arterial aneurysm proximally on the left ophthalmic artery. After transarterial embolization of the DAVS with N-butyl cyanoacrylate and polyvinyl alcohol, minimal shunting still remained. At follow-up angiography six months after the treatment, the shunt was obliterated and the ophthalmic artery aneurysm had regressed completely. Our case illustrates that complete obliteration of a DAVS may be achieved even though arteriovenous shunting remains at the end of the procedure. Furthermore, a flow-related arterial aneurysm, may not warrant any specific treatment. Elimination of the high flow situation can lead to complete regression of these aneurysms.
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PIASECKI, Adam, Jakub JURASZ, and Rajmund SKOWRON. "FORECASTING SURFACE WATER LEVEL FLUCTUATIONS OF LAKE SERWY (NORTHEASTERN POLAND) BY ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION." Journal of Environmental Engineering and Landscape Management 25, no. 4 (December 21, 2017): 379–88. http://dx.doi.org/10.3846/16486897.2017.1303498.
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The aim of this study is to assess the possibility of forecasting water level fluctuations in a relatively small (<100 km2), post-glacial lake located in a temperate climate zone by means of artificial neural networks and multiple linear regression. The area of study was Lake Serwy, located in northeastern Poland. Two artificial neural network (ANN) multilayer perceptron (MLP) and multiple linear regression (MLR) models were built. The following explanatory variables were considered: maximal and minimal temperature (Tmax, Tmin) wind speed (WS), vertical circulation (VC) and water level from previous periods (WL). Additionally, a binary variable describing the period of the year (winter, summer) has been considered in one of the two MLP and MLR models. The forecasting models have been assessed based on selected criteria: mean absolute percentage error (MAPE), root mean squared error (RMSE), coefficient of determination (R2) and mean biased error. Considering their values and absolute deviations from observed values it was concluded that the ANN model using an additional binary variable (MLP_B+) has the best forecasting performance. Absolute deviations from observed values were the determining factor which made this model the most efficient. In the case of the MLP_B+ model, those values were about 10% lower than in other models. The conducted analyses indicated good performance of ANN networks as a forecasting tool for relatively small lakes located in temperate climate zones. It is acknowledged that they enable water level forecasting with greater precision and lower absolute deviations than the use of multiple linear regression models.
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Raha, Arnab, Shovan Maity, Souvik Kr Mitra, Ramesh Jana, and Mrinal K. Naskar. "A Low Complexity Multivariate Regression Based Flood Forecasting Model Using an Optimized WSN Deployment Scheme." Advanced Materials Research 403-408 (November 2011): 3484–94. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3484.
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Very recently work has been done to develop efficient disaster forecasting systems utilizing WSN technology. Such networks pose a tremendous design challenge such as the ability to cope with node failure, limited power, distributed prediction, wide variety of sensors and the need for communication over a large area. Our paper introduces a Predictive Environmental Sensor Network (PESN) Architecture which employs a minimal deployment scheme to ensure connectivity among the nodes involved within the network. On this connected network we run our distributed statistical model for forecasting. The statistical process used for this real time prediction uses multiple variable regression method providing the advantages of simplicity and robustness much needed in low power and limited ability sensor nodes. The versatility of the forecasting model is proved on its independence on the number of parameters, as it can incorporate as many variables into the algorithm as required, as long as there is sufficient positive correlation with the instantaneous river water level.
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Belayneh, A., and J. Adamowski. "Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/794061.
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Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. Data-driven models are suitable forecasting tools due to their rapid development times, as well as minimal information requirements compared to the information required for physically based models. This study compares the effectiveness of three data-driven models for forecasting drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN). SPI 3 and SPI 12 were the SPI values that were forecasted. These SPI values were forecast over lead times of 1 and 6 months. The performance of all the models was compared using RMSE, MAE, andR2. The forecast results indicate that the coupled wavelet neural network (WN) models were the best models for forecasting SPI values over multiple lead times in the Awash River Basin in Ethiopia.
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Ferreri, Andrés J. M., Maurilio Ponzoni, Massimo Guidoboni, Carlo De Conciliis, Antonio Giordano Resti, Benedetta Mazzi, Antonia Anna Lettini, et al. "Regression of Ocular Adnexal Lymphoma AfterChlamydia Psittaci–Eradicating Antibiotic Therapy." Journal of Clinical Oncology 23, no. 22 (August 1, 2005): 5067–73. http://dx.doi.org/10.1200/jco.2005.07.083.
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PurposeSome infectious agents contributing to lymphomagenesis have been considered targets for new therapeutic strategies. Chlamydia psittaci DNA has been detected in 80% of ocular adnexal lymphomas. The present pilot study was carried out to assess whether C psittaci–eradicating antibiotic therapy is associated with tumor regression in ocular adnexal lymphomas.Patients and MethodsNine patients with C psittaci–positive marginal-zone B-cell lymphoma of the ocular adnexa at diagnosis or relapse were treated with doxycycline 100 mg, bid orally, for 3 weeks. The presence of C psittaci DNA in peripheral-blood mononuclear cells (PBMCs) was also assessed before and after treatment in seven patients. Objective lymphoma regression was assessed 1, 3, and 6 months after therapy conclusion and every 6 months during follow-up.ResultsAll patients completed antibiotic therapy with excellent tolerability. At 1 month from doxycycline assumption, chlamydial DNA was no longer detectable in PBMCs of all four positive patients. Objective response was complete in two patients, partial response (> 50%) was observed in two patients, and minimal response (< 50%) was observed in three patients. Duration of response in the seven responders was 12+, 29+, 31+, 8+, 7+, 2+, and 1+ months, respectively.ConclusionC psittaci–eradicating antibiotic therapy with doxycycline is followed by objective response in patients with ocular adnexal lymphoma, even after multiple relapses of the disease. A confirmatory, large, phase II trial is warranted to confirm whether this fast, cheap, and well-tolerated therapy could replace other more aggressive strategies as first-line treatment against ocular adnexal lymphomas.
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Li, Cheng-Wei, and Bor-Sen Chen. "Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data." Disease Markers 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/4149608.
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Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects.
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Mina, Kym, Lin Fritschi, and Matthew Knuiman. "Do aggregates of multiple questions better capture overall fish consumption than summary questions?" Public Health Nutrition 11, no. 2 (February 2008): 196–202. http://dx.doi.org/10.1017/s1368980007000468.
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AbstractObjectiveTo compare intake estimates, validity and reliability of two summary questions to measure fish consumption with information from a detailed semi-quantitative food-frequency questionnaire (FFQ) on fish consumption.DesignPopulation-based, cross-sectional study. Participants completed an FFQ and provided blood samples for erythrocyte membrane eicosapentaenoic acid (EPA) analysis. Aggregate measures of consumption of fresh/frozen/canned fish (fresh fish) and smoked/salted/dried fish (preserved fish) were generated from the FFQ and were compared with responses to the summary questions regarding intakes of similar items. Both methods were tested for validity, using correlation and linear regression techniques with EPA, and retest reliability.SettingPerth metropolitan area, Western Australia.SubjectsOne hundred and nine healthy volunteers of both sexes, aged 21–75 years.ResultsThe summary fresh fish measure underestimated frequency and grams per week given by the aggregate question by about 50%, while estimates from the summary preserved fish measure were approximately three times that of the aggregate measure. Multiple linear regression analysis suggested that the aggregates accounted for more of the variation in EPA levels, but the difference was minimal. Intra-class correlations confirmed that both methods were reliable.ConclusionsOur study indicates that extensive questioning results in different absolute intakes of fish compared with brief questioning, but does not add any information if ranking individuals according to overall consumption of fish.
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Willett, John B., and Judith D. Singer. "It’s Déjà Vu All Over Again: Using Multiple-Spell Discrete-Time Survival Analysis." Journal of Educational and Behavioral Statistics 20, no. 1 (March 1995): 41–67. http://dx.doi.org/10.3102/10769986020001041.
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Multiple-spell discrete-time survival analysis can be used to investigate the repeated occurrence of a single event, or the sequential occurrence of disparate events, including: students’ and teachers’ entries into, and exits from, school; childrens’ progress through stages of cognitive reasoning; disturbed adolescents’ repeated suicide attempts; and so forth. In this article, we introduce and illustrate the method using longitudinal data on exit from, and reentry into, the teaching profession. The advantages of the approach include: (a) applicability to many educational problems; (b) easy inclusion of time-invariant and time-varying predictors; (c) minimal assumptions—no proportional-hazards assumption is invoked and so the effects of predictors can vary over time within, and across, spells; and (d) all statistical models can be fit with a standard logistic regression package.
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Hoyle, Jared A., Fred H. Yelverton, and Travis W. Gannon. "Evaluating Multiple Rating Methods Utilized in Turfgrass Weed Science." Weed Technology 27, no. 2 (June 2013): 362–68. http://dx.doi.org/10.1614/wt-d-12-00126.1.
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Turfgrass weed scientists commonly use visual ratings (VR) to assign a numerical value to a turfgrass or weed response. These ratings lack quantifiable numerical values and are considered subjective. Alternatives to VR, including line intersect analysis (LIA) and digital image analysis (DIA), have been used to varying extents in turfgrass research. Alternatives can be expensive, labor intensive, and can require extensive calibration and increased time for data acquisition. Minimal research has been conducted evaluating rating methods used in turfgrass weed science. Trials were conducted in 2007 and 2008 to evaluate ratings methods used to quantify large crabgrass populations as influenced by tall fescue mowing height (2.5, 5.1, 7.6, and 10.2 cm). Percent large crabgrass cover was assessed utilizing VR, LIA, and DIA to determine if differences existed among evaluation methods. Pairwise comparisons, Pearson's correlation, and linear regression were performed to compare evaluations. All rating methods were significantly correlated to one another. Differences of large crabgrass cover estimates existed between LIA and DIA data at all mowing heights and between VR and DIA data at the 7.6 and 10.2 cm mowing heights in 2007. Authors believe that shadows produced by the turf canopy at higher (≥ 7.6 cm) mowing heights increased DIA estimates of large crabgrass cover. At trial initiation in 2007, researchers did not capture calibration images because the methodology to eliminate a shadow influence using a standard digital image had not been published. Additional DIA calibration in 2008 corrected for canopy shadows, and no differences were observed in large crabgrass cover between all evaluation methods indicated by nonsignificance pairwise comparisons and estimated regression parameters. These data indicate VR are no different than LIA or DIA in estimating large crabgrass cover as affected by tall fescue mowing height.
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Jafar, Muh Alfian, Syarifuddin Rauf, and Dasril Daud. "Profile of Minimal and Non-Minimal Change Nephrotic Syndrome in Children: A Cross-Sectional Study." Green Medical Journal 2, no. 2 (August 30, 2020): 39–48. http://dx.doi.org/10.33096/gmj.v2i2.51.
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Introduction: Idiopathic nephrotic syndrome (INS) is the most common kidney disease in children. Histopathological features classify the disease into minimal change nephrotic syndrome (MCNS) and non-minimal change nephrotic syndrome (NMCNS). Clinical and laboratory characteristics are considered to be useful in estimating the underlying pathological changes. This study aimed to assess the spectrum of clinical and laboratory profiles in children suffering from MCNS and NMCNS.
Methods: This was a cross-sectional study using medical records of patients hospitalized at Dr. Wahidin Sudirohusodo Central General Hospital and Hasanuddin University Teaching Hospital from January 2016 to August 2018. Subjects were classified into MCNS and NMCNS groups and evaluated for age, sex, hypertension, degree of edema, degree of hematuria, proteinuria level, cholesterol level, albumin level, urea level, and creatinine level. Data were analyzed by using chi-square, fisher's exact test Mann Whitney analysis, multivariate by logistic multiple regression analysis, and diagnostic test.
Results: Of the 36 subjects enrolled, 10 (27.8%) had MCNS, and 26 (72.2%) had NMCNS. Bivariate analysis showed significant differences in hypertension (p=0.020; OR=12.3; 95% CI 1.35-111.61) and hematuria (p=0.018; OR=7.7 ; 95% CI 1.52-39.75). Multivariate analysis indicated that only hematuria is a significant predictor (p=0.014; OR=7.778; 95% CI 1.522 – 39.754). The diagnostic test of NMCNS showed hematuria sensitivity at 77% and specificity at 70% with a positive predictive value of 87% and a negative predictive value of 46%.
Conclusion: Our study showed that hematuria is a sensitive predictor of NMCNS.
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Mata, May Anne E. "A Comparison of Minimal Pharmacokinetic Models for an Anti-diabetic Agent." Journal of Health Research and Society 1 (October 19, 2018): 5. http://dx.doi.org/10.34002/jhrs.v1i0.11.
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In diabetes studies, pharmacologists rely on statistical techniques tied with experimental results to describe the plasma concentration of an anti-diabetic agent. However, these sets of statistical information only provide minimal inference to the drug’s kinetics. To understand the effect of an anti-diabetic agent to a glucose-insulin system, it is necessary to predict its movement in the system over a specific time interval. In this study, a set of simple pharmacokinetic models was formulated to describe the dynamics of the plasma concentration of an anti-diabetic agent known as metformin. The models were fitted to empirical data via nonlinear regression analysis and were compared using Akaike information criterion to determine the most reasonable model and parameter estimates. The results reveal that models considering varying absorption rate have a promising fit. These models can be extended to multiple drug dosage cases and can be used to estimate rate constants associated to other anti-diabetic agents.
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