Analyzing Herbicide Interactions: A Statistical Treatment of Colby's Method

1988 ◽  
Vol 2 (3) ◽  
pp. 304-309 ◽  
Author(s):  
Jerry L. Flint ◽  
Paul L. Cornelius ◽  
Michael Barrett
Keyword(s):  
Experimental Data ◽  
Statistical Tests ◽  
Statistical Treatment ◽  
Measurement Scale ◽  
Growth Data ◽  
Significance Tests ◽  
Anova Model ◽  
Heterogeneity Of Variance ◽  
Original Measurement ◽  
Data Heterogeneity

A model and a proposed method for testing herbicide interactions were modified from an analysis of variance (ANOVA) model for a 2 by 2 factorial experiment. Statistical tests for either synergism, antagonism, or additivity of herbicide combinations were developed through transforming growth data to logarithms followed by significance tests of 2 by 2 contrasts of the form μii- μi0- μ0i+ μ00with respect to the log-transformed data. Using actual experimental data, heterogeneity of variance was less severe on the log scale compared to the original measurement scale. An expedient SAS(R)program for obtaining the desired significance tests was developed.

MODELLING OF 226Ra ACTIVITY CONCENTRATIONS ALONG OGUN RIVER, SOUTH WESTERN NIGERIA

2018 ◽  
Vol 14 (2) ◽  
pp. 1-14
Author(s):  
I C OKEYODE ◽  
N N JIBIRI ◽  
R BELLO
Keyword(s):  
Experimental Data ◽  
Gamma Ray ◽  
Statistical Tests ◽  
Least Square ◽  
Approximation Technique ◽  
Sediment Samples ◽  
Gamma Ray Spectrometer ◽  
Activity Concentrations ◽  
226Ra Activity ◽  
Ogun River

This work was aimed at generating a model using least square approximation technique to predict values of activity concentrations of 226Ra in any location along Ogun river in Nigeria using experimental data. Sediment samples were collected in thirty two locations along the river of about 400 km in length. NaI(Tl) gamma-ray spectrometer system was used to obtain activity concentrations of 226Ra.The aver-age value of activity concentration of 226Ra in the sediment samples from the upper region through the middle to the lower region of the river was found to be 12.65 ± 3.48 Bq/kg, having values ranging from 5.57 ± 2.36 Bq/kg (at Ekerin) to 20.40 ± 4.52 Bq/kg (at Sokori). From this work, it was observed that the generated model and experimental data could be used to predict values of activity concentrations of 226Ra in any location along the river once the latitude and longitude (position) are known. Statistical tests on the model also showed that there were no significant differences between the experimental and predicted data of 226Ra and that 98.70% of the experimental data were predicted by the model.

scholarly journals Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

2018 ◽  
Author(s):  
Jukka Intosalmi ◽  
Adrian C. Scott ◽  
Michelle Hays ◽  
Nicholas Flann ◽  
Olli Yli-Harja ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Course ◽  
Model Development ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Data Driven ◽  
Coarse Grained ◽  
Intercellular Signaling ◽  
Growth Data ◽  
Metabolic Coupling

AbstractMotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell–cell and metabolic coupling produce functionally optimized structures is still limited.ResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.AvailabilityExperimental data and a computational implementation to reproduce the results are available athttp://research.cs.aalto.fi/csb/software/multiscale/[email protected],[email protected]

Statistical Treatment of Experimental Data

1964 ◽  
Vol 32 (4) ◽  
pp. 322-322 ◽  
Author(s):  
Hugh D. Young ◽  
Chas Williamson
Keyword(s):  
Experimental Data ◽  
Statistical Treatment

Assessment of the Accuracy of TD-DFT Absorption Spectra: Substituted Benzenes

2008 ◽  
Vol 73 (6-7) ◽  
pp. 898-908 ◽  
Author(s):  
Jean-Marie André ◽  
Denis Jacquemin ◽  
Eric A. Perpete ◽  
Daniel P. Vercauteren ◽  
Valérie Wathelet
Keyword(s):  
Experimental Data ◽  
Statistical Treatment ◽  
Hybrid Functional ◽  
Average Deviation ◽  
Substituted Benzenes ◽  
Pcm Model ◽  
Donor And Acceptor ◽  
Td Dft ◽  
Specific Difficulty ◽  
The Impact

Using the parameter-free PBE0 hybrid functional in conjunction with the conducting PCM model, we compute the UV/VIS spectra of a series of solvated phenol and nitrobenzene chromogens. For the first series, the average deviation with respect to experiment is large (about 0.5 eV) but the auxochromic shifts are very accurately and consistently predicted. Therefore, after a statistical treatment, the TD-DFT values are within 0.02 eV of the experimental data. For nitrobenzenes, the average discrepancy is smaller than for phenols, though the impact of individual substitution is much less consistent with experimental trends. We also confirm that push-pull compounds with donor and acceptor groups in meta positions are especially problematic for TD-DFT calculations relying on conventional hybrids, and we unravel the origin of this specific difficulty.

Experimental data analysis: A guide to the selection of simple statistical tests

1997 ◽  
Vol 10 (3) ◽  
pp. 275-286 ◽  
Author(s):  
M.A. Jordan ◽  
N. Powell ◽  
C.V. Phillips ◽  
C.K. Chin
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Statistical Tests ◽  
Experimental Data Analysis ◽  
Selection Of
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