Sentence examples for departures from assumptions from inspiring English sources

Causes of departures from assumptions underlying computational programs are addressed by methods that involve theoretical analysis, experimental measurement, and axisymmetric computation.

Though the data available to measure the impact of wages on work fall short of what would be ideal, recent research has derived estimates that appear robust with respect to departures from assumptions underlying their derivation.

1. Vectors of Random Variables -- 2. Multivariate Normal Distribution -- 3. Linear Regression: Estimation and Distribution Theory -- 4. Hypothesis Testing -- 5. Confidence Intervals and Regions -- 6. Straight-Line Regression -- 7. Polynomial Regression -- 8. Analysis of Variance -- 9. Departures from Underlying Assumptions -- 10. Departures from Assumptions: Diagnosis and Remedies -- 11.

The statistical differences are most likely due to the more noisy character of the ground-based CHPs, especially high in the canopy where ground-based sightings are rare resulting in an underestimate of canopy surface area and height, and to departures from assumptions of canopy uniformity, particularly regarding lack of clumping and vertically constant canopy reflectance, which bias the CHPs.

Thus, there were no significant departures from assumptions, allowing the use of CMR statistics.

Using simulation, we examined whether the KS test could reliably identify departures from assumptions made in the imputation model.

These results suggest either a greater intensity of selection chromosome wide against more frequent unpreferred mutations than for preferred changes or a departure from assumptions of the model.

18 We have instead suggested that authors should make their most plausible assumptions the basis for their primary analysis and then provide conservatism by assessing sensitivity to departures from those assumptions.

Even though the optimization scheme assumes independent genes and known variances, simulation results show that this approach is robust to moderate departures from those assumptions.

These are more conservative than normal standard errors (i.e., they are larger) and allow for departures from the assumptions of the statistical model.

The use of residuals for detecting departures from the assumptions of the linear model with full-rank covariance, whether the design matrix is full rank or not, has long been recognized as an important diagnostic tool.