Sentence examples for variable regression and from inspiring English sources
However, we repeated the analyses that produced the key findings using continuous variable regression and obtained similar results.
The magnitude and significance of impacts on read mappability and variant calling were determined using multiple variable regression and ANCOVA in R v3.0.2 (http://www.r-project.org/).org/
The number of candidate variables is generally consolidated to a tractable number through expert knowledge or single variable regression, and then various combinations of models are tested until one finds the best model in terms of a validation statistic like R or Akaike Information Criterion (AIC).
Topics covered include: descriptive statistics, graphical displays of data, introduction to probability, expectations and variance of random variables, confidence intervals and tests for means, differences of means, proportions, differences of proportions, chi-square tests for categorical variables, regression and multiple regression, an introduction to analysis of variance.
Using the Classification and Regression Trees (CART) method [ 37] the decision making process will subsequently be reconstructed into a decision tree for predicting continuous dependent variables (regression) and categorical predictor variables (classification).
BMA algorithms estimate posterior model probabilities, inclusion probabilities for each variable, regression coefficients and their standard deviations.
density (DEN), porosity (PORO) and compressional wave velocity (P-WV) using multiple variable regression analysis (MVRA) and adaptive neuro-fuzzy inference system (ANFIS).
We will use three methods to overcome confounding: multivariable adjusted Cox regression, propensity score matched Cox regression, and instrumental variable regression.
There are analytic methods to adjust for endogeneity bias in cross-sectional data (e.g., propensity score matching, bivariate probit regression, multivariate probit regression and instrumental variable regression) [ 68].
To test the effect of each independent variable on each dependent variable, linear regression and logistic regression were used to test their effects.
Also, there was a wide range of interception (-3.2 to -30.5) and a variable regression coefficient (1.0⌓3.1).
