Non-Linear Regression Analysis
Artificial Neural Network and Non-Linear Regression: A Comparative Study
... nonlinear regression and classification problems in ...the linear and non-linear regression ...series analysis of the All India Rainfall Index using the ANN model and the ...
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Water Coning in Horizontal Wells: Prediction of Post-Breakthrough Performance
... sensitivity analysis was conducted to determine how important coning para meters affect coning behaviour in horizontal we ...formation. Non Linear regression analysis was then carried ...
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Eleventh industrial mathematical and statistical modeling workshop for graduate students
... a regression model for each time period of the day ...data analysis indicated that such partitioning would not create difficulties in fitting a regression model since it is reasonable to assume that ...
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Learning from data to predict future symptoms of oncology patients
... Vector Regression (SVR) and Non-linear Canonical Corre- lation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points ...
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Development Of Computational Intelligence Algorithms For Modelling The Performance Of Humanin And Its Derivatives In HPLC Optimization Method Development
... comparative analysis of the result demonstrated that ANFIS as a promising non-linear artificial intelligence based model found to be more reliable and suitable for predicting the performance of ...
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Comparative Evaluation of Multiple Linear Regression and Support vector Machine aided Linear and Non
... -linear regression in QSAR models. Models obtained from SVM aided non-linear regression were found statistically fit and more predictive than models obtained from multiple linear ...
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Linear and Non-Linear Regression: Powerful and Very Important Forecasting Methods
... Regression Analysis is at the center of almost every Forecasting technique, yet few people are comfortable with the Regression ...a Regression Forecasting function for both the linear ...
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QSAR Study of the octanol/water partition coefficient of organophosphorus compounds: The hybrid GA/MLR and GA/ANN approaches
... relationship analysis was performed on the series of 43 insecticides using two different methods, linear (multiple linear regression, MLR) and non- -linear (artificial neural ...
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Non Linear Text Regression with a Deep Convolutional Neural Network
... i.e. we process “Hong Kong” without zeroing “Hong” or “Kong”. About 95% of phrases re- sult in no output change, including common sen- timent words, which shows that text regression is a different problem to ...
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<p>Threshold Effects in the Relationship Between Serum Non-High-Density Lipoprotein Cholesterol and Metabolic Syndrome</p>
... the non-MetS group based on the criteria of the National Cholesterol Education Program Adult Treatment Panel ...serum non-HDL-C and the risk of MetS was evaluated using multivariate regression ...
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Medium Term Electric Load Forecasting Using Multivariable Linear and Non Linear Regression
... of days or for the weeks ahead. With this information it can be decided to whether take certain facilities/plants for maintenance or not during a given period of time. This will also help to plan major tests and ...
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Paratuberculosis sero-status and milk production, SCC and calving interval in Irish dairy herds
... multiple linear regression analysis was initially used to determine the association between herd paratuberculosis status (negative, positive, non-negative), herd identifier number (number of ...
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QSAR study and rustic ligand-based virtual screening in a search for aminooxadiazole derivatives as PIM1 inhibitors
... algorithm analysis (GFA) to reduce the number of the variables of the data set and choose the pertinent ones, in which, the muta- tion probability and smoothing parameter were set to ...multiple linear ...
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Geotechnical Investigation of Different Soil Samples using Regression Analysis
... The project was conducted to find an equation between CBR, OMC, MD and the percentage of highway materials within the scope of study. Accordingly, the required data base was obtained from different locations. Using the ...
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Linear Regression Analysis for Symbolic Interval Data
... Billard and Diday [2] used the center point of each interval-value to fit the li- near regression model. Carvalho et al. [3] used the center point and range of each interval-value to fit two linear ...
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On the Detection of Influential Outliers in Linear Regression Analysis
... Abstract: In this paper, we propose a measure for detecting influential outliers in linear regression analysis. The performance of the proposed method, called the Coefficient of Determination Ratio ...
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EXAMINATION OF MULTIVARIATE MULTIPLE LINEAR REGRESSION ANALYSIS
... The most frequently used measure of association in determining the relation between the dependent and independent variables is the canonic correlation. The canonic correlation coefficient between the dependent and ...
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Knowledge management in the semi public sector : prerequisite organizational and human conditions for a successful implementation of knowledge management in a semi public organization in the Netherlands
... 24 training, manager’s attitude and sanctions, on knowledge sharing. Their empirical research failed to find a significant relationship between organizational support and knowledge sharing. However, they did not measure ...
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APPLICATION OF A LINEAR REGRESSION ANALYSIS TO AIRTEST PM2500.
... The IAQ is critical not only in buildings, but also in underground areas and public transportation systems. Much effort has been made for the improvement of the IAQ in subway stations [2-5]. Among the various types of ...
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A simplified procedure of linear regression in a preliminary analysis
... Moreover, our purpose is to evaluate the median applications of the different subsets of observed values, instead of the location index usually represented by the mean. This kind of procedure is useful in case of ...
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