The analysis and prediction of some two factor interactions in grass breeding

1971 ◽  
Vol 76 (2) ◽  
pp. 301-306 ◽  
Author(s):  
A. J. Wright
Keyword(s):  
Regression Analysis ◽  
Binary Mixtures ◽  
Strongly Correlated ◽  
Regression Parameter ◽  
Grass Breeding ◽  
Factor Interactions ◽  
Main Effects

SUMMARYA method of analysis of two factor experiments is given. This involves a development of the regression analysis used by Finlay & Wilkinson (1963) and others, and allows for the regression of interaction components onto both main effects. The usefulness of the single joint regression parameter for prediction is outlined.The applicability of the analysis to three situations commonly found in grass breeding is illustrated by means of examples. It is concluded that the model may frequently describe variation due to genotype-environment interactions, and among diallel arrangements of binary mixtures of genotypes, but is likely to be of little utility for genetic diallel or other mating schemes unless the genes have a strongly correlated distribution among the parent plants.

Forms, Importance, and Ineffability of Factor Interactions to Define Personality Disorders: Comment on Lilienfeld et al. (2019)

2019 ◽  
Author(s):  
Stephen D Benning ◽  
Edward Smith
Keyword(s):  
Personality Disorders ◽  
False Positive Rate ◽  
Empirical Work ◽  
Design Studies ◽  
Linear Threshold ◽  
Positive Rate ◽  
Saturation Effects ◽  
Factor Interactions ◽  
Main Effects ◽  
Criterion Variables

The emergent interpersonal syndrome (EIS) approach conceptualizes personality disorders as the interaction among their constituent traits to predict important criterion variables. We detail the difficulties we have experienced finding such interactive predictors in our empirical work on psychopathy, even when using uncorrelated traits that maximize power. Rather than explaining a large absolute proportion of variance in interpersonal outcomes, EIS interactions might explain small amounts of variance relative to the main effects of each trait. Indeed, these interactions may necessitate samples of almost 1,000 observations for 80% power and a false positive rate of .05. EIS models must describe which specific traits’ interactions constitute a particular EIS, as effect sizes appear to diminish as higher-order trait interactions are analyzed. Considering whether EIS interactions are ordinal with non-crossing slopes, disordinal with crossing slopes, or entail non-linear threshold or saturation effects may help researchers design studies, sampling strategies, and analyses to model their expected effects efficiently.

scholarly journals Characterization of Noise Level Inside a Vehicle under Different Conditions

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2471 ◽  
Author(s):  
Daniel Flor ◽  
Danilo Pena ◽  
Luan Pena ◽  
Vicente A. de Sousa ◽  
Allan Martins
Keyword(s):  
Regression Analysis ◽  
Noise Level ◽  
Acoustic Noise ◽  
Experimental Setup ◽  
Strongly Correlated ◽  
Noise Levels ◽  
Noise Sources ◽  
Statistical Tools ◽  
Window Position

Vehicular acoustic noise evaluations are a concern of researchers due to health and comfort effects on humans and are fundamental for anyone interested in mitigating audio noise. This paper focuses on the evaluation of the noise level inside a vehicle by using statistical tools. First, an experimental setup was developed with microphones and a microcomputer located strategically on the car’s panel, and measurements were carried out with different conditions such as car window position, rain, traffic, and car speed. Regression analysis was performed to evaluate the similarity of the noise level from those conditions. Thus, we were able to discuss the relevance of the variables that contribute to the noise level inside a car. Finally, our results revealed that the car speed is strongly correlated to interior noise levels, suggesting the most relevant noise sources are in the vehicle itself.

scholarly journals Two-Level Designs to Estimate All Main Effects and Two-Factor Interactions

Technometrics ◽  
2017 ◽  
Vol 59 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Pieter T. Eendebak ◽  
Eric D. Schoen
Keyword(s):  
Factor Interactions ◽  
Main Effects

An Experiment on Anonymity and Multi-User Virtual Environments

2016 ◽  
pp. 80-93
Author(s):  
Richard N. Landers ◽  
Rachel C. Callan
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Virtual Environments ◽  
Second Life ◽  
Online Discussion ◽  
Interactive Effect ◽  
Hierarchical Multiple Regression ◽  
Learner Identity ◽  
Hierarchical Multiple Regression Analysis ◽  
Main Effects

Little prior research has empirically examined anonymity in learning. In this study, we manipulated learner identity by experimentally assigning learners to participate in online discussion either anonymously or using their actual name, crossed with learning medium (OpenSim/Second Life vs. real-time chat), with the goal of determining if anonymous discussion in multi-user virtual environments (MUVE) provides unique value to learning (a 2x2 between-subjects design). Results from a quantitative hierarchical multiple regression analysis revealed both main effects: participants who were anonymous scored lower (d = -0.46) and participants discussing in a MUVE scored lower (d = -0.47) on the learning measure without interactive effect, suggesting that anonymizing participants during content-related discussion may reduce learning under certain circumstances. We suggest instructors encourage learners to represent themselves authentically in any VEs to maximize learning and also discourage instructors from adopting MUVEs if their only reason to do so is to host synchronous discussion.

An Experiment on Anonymity and Multi-User Virtual Environments

2014 ◽  
Vol 6 (2) ◽  
pp. 53-64 ◽  
Author(s):  
Richard N. Landers ◽  
Rachel C. Callan
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Virtual Environments ◽  
Second Life ◽  
Online Discussion ◽  
Interactive Effect ◽  
Hierarchical Multiple Regression ◽  
Learner Identity ◽  
Main Effects ◽  
Do So

Little prior research has empirically examined anonymity in learning. In this study, we manipulated learner identity by experimentally assigning learners to participate in online discussion either anonymously or using their actual name, crossed with learning medium (OpenSim/Second Life vs. real-time chat), with the goal of determining if anonymous discussion in multi-user virtual environments (MUVE) provides unique value to learning (a 2x2 between-subjects design). Results from a quantitative hierarchical multiple regression analysis revealed both main effects: participants who were anonymous scored lower (d = -0.46) and participants discussing in a MUVE scored lower (d = -0.47) on the learning measure without interactive effect, suggesting that anonymizing participants during content-related discussion may reduce learning under certain circumstances. The authors suggest instructors encourage learners to represent themselves authentically in any VEs to maximize learning and also discourage instructors from adopting MUVEs if their only reason to do so is to host synchronous discussion.

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