multivariate linear regression model
Building models for agricultural land fire prediction using remote sensed environmental data: a case study in Dien Bien Province, Vietnam (2003 (2016)
... this model not comprehensive enough to accurately predict agricultural land fires because data for independent variables across fuel, topographic and anthropogenic conditions are also ...FFDRS model, is ...
14
Response of economic growth to domestic borrowing, governance and market reforms in Kenya
... A multivariate linear regression model was used to analyse the response of economic growth to domestic debt in Kenya while dummies and interaction terms were used to capture the moderati[r] ...
105
BANK CREDIT AND AGRICULTURAL SECTOR GROWTH IN NIGERIA, 1990-2016: INVESTIGATING THE NEXUS
... the model as well as the a priori expectation about the sign and size of the parameters of the function (Ibenta, ...2012). Multivariate linear regression model is used to test the null ...
12
Clinical risk and patient safety: a multicenter cross-sectional study to explore knowledge, attitudes and practice of hospital nurses
... multivariable linear and logistic regression models(27). Three multivariate linear and logistic regression models have been constructed: knowledge about the definition of an adverse ...
9
Prediction distribution for linear regression model with multivariate Student-t errors under the Bayesian approach
... same regression coefficients vector of order k × 1 and e f is the error term associated with y f and e f has a univariate Student-t distribution as e f ~ t 1 (0, σ , ν ...
5
Multivariate logistic regression analysis using multilevel model
... generalized linear model format. A generalized linear model is composed from three components, including random, systematic and link ...
5
Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)
... study, multivariate linear regression and neural network model (feed-forward back- propagation network) were employed to develop a pedotransfer function for predicting soil cation exchange ...
8
Flexible Partially Linear Single Index Regression Models for Multivariate Survival Data
... How to choose the cut points is an important question. Normally they are chosen based on prior assumptions about the marginal distributions of the survival time. If we know the hazard function slope changes at some ...
118
Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables
... of multivariate statistical techniques such as stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) for vegetation water content prediction using the ...
22
The association between metabolic syndrome and sleep symptoms and sleep hygiene in the elderly in Northern Taiwan
... the multivariate linear regression models (Table 4), which included each sleep symptom in model 1 (adjusted for age, gender, living status, smoking status, alcohol consumption, sedentary ...
7
Modeling for Prediction of Characteristic Deflection of Flexible Pavements- Comparison of Models Based on Artificial Neural Network and Multivariate Regression Analysis
... a linear or nonlinear ...physical model to solve the ...ANN model, an individual node accounting for each input ...ANN model has one output node representing the value of characteristic ...
6
EXAMINATION OF MULTIVARIATE MULTIPLE LINEAR REGRESSION ANALYSIS
... The regression analysis was used to determine the functional relation between two or more variables that have a cause-effect relation, and to be able to make estimations about the topic by using this ...
8
Analysing various Regression Models for Data Processing
... a multivariate model produces tables of 3, 4, 5 or more ...log linear models to examine ...only linear regression, but there is a need to develop non-linear ...
6
Integration of precision farming data and spatial statistical modelling to interpret field scale maize grain yield variability in New Zealand
... were multivariate linear regression (MLR) and Cubist tree ...Cubist regression model is a technique primarily used in remote sensing studies for handling large datasets and has, in the ...
8
The Fundamental Determinants of Economic Performance
... multiple regression analyses and additionally includes an external war dummy, Western Europe dummy, Sub-Saharan Africa dummy and “other” geographic region dummy as ...Every model differs slightly through ...
79
Market competition and price of disease management programmes: an observational study
... DMPs, linear regression analyses were ...and multivariate linear regression analyses were ...the multivariate linear regression analyses all independent variables ...
7
Brachial–ankle pulse wave velocity, cardio-ankle vascular index, and prognosis
... a multivariate Cox proportional hazards model or multivariate logistic regression model were defined as ...a multivariate Cox proportional hazards model were defined as ...
28
Wavelet based feature extraction methods for the discrimination and regression of spectral data
... The multivariate methods which were applied for the regression of spectral data include: • SMLR: stepwise multiple linear regression til SPCR: stepwise principal component regression • P[r] ...
270
An NMR crystallographic investigation of the relationships between the crystal structure and 29Si isotropic chemical shift in silica zeolites
... This function is called using the syntax “model = glmfit(pred,resp)” where pred is a (n × m) matrix of predictor variables (i.e., the structural parameters, where each row represents a different site in the ...
16
Prediction of Vertical Handover Using Multivariate Regression Model
... p-value: The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p- value (< 0.05) indicates that the null hypothesis can be rejected. In other words, a predictor ...
8