INTERPRETATION OF INTERACTION IN THE ANALYSIS OF VARIANCE OF A FACTORIAL EXPERIMENT

1958 ◽  
Vol 38 (2) ◽  
pp. 181-186
Author(s):  
G. C. Ashton ◽  
J. C. Rennie ◽  
E. Etter
Keyword(s):  
Analysis Of Variance ◽  
Treatment Effects ◽  
Study Data ◽  
Factorial Experiment ◽  
Biological Research ◽  
Feeding Experiment ◽  
Experimental Treatments

Frequently imposed experimental treatments in biological research produce results which require evaluation in terms of interdependence between the factors under study. Data from a 2-factor calf-feeding experiment have been used to illustrate the inadequacy of over-all means in describing the treatment effects where factor interdependence existed. It was shown that, to make accurate appraisal of the treatment effects under these conditions, it is necessary to restrict consideration to the subtreatment means.

scholarly journals Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes

2020 ◽  
pp. 107699862095198
Author(s):  
Youmi Suk ◽  
Jee-Seon Kim ◽  
Hyunseung Kang
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Latent Class ◽  
Finite Mixture Models ◽  
Likelihood Estimation ◽  
Study Data ◽  
Latent Class Models ◽  
Finite Mixture ◽  
Latent Classes ◽  
Heterogeneous Treatment Effects

There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by well-established finite mixture/latent class models. This article proposes a hybrid method, a combination of finite mixture modeling and ML methods from causal inference to discover effect heterogeneity in latent classes. Our simulation study reveals that hybrid ML methods produced more precise and accurate estimates of treatment effects in latent classes. We also use hybrid ML methods to estimate the differential effects of private lessons across latent classes from Trends in International Mathematics and Science Study data.

Absence of persistent methane emission differences in three breeds of dairy cows

2008 ◽  
Vol 48 (2) ◽  
pp. 77 ◽  
Author(s):  
A. Münger ◽  
M. Kreuzer
Keyword(s):  
Dairy Cows ◽  
Analysis Of Variance ◽  
Methane Emission ◽  
Study Data ◽  
Open Circuit ◽  
Minor Importance ◽  
Genetic Determination ◽  
Apparent Lack ◽  
Jersey Cows

In the present study, data from an experiment with 10 purebred Holstein, Simmental and Jersey cows each were analysed to test the assumption that there are genetically low methane-producing animals. Methane emission of cows offered forage ad libitum and some concentrate was measured for 3 days in open-circuit respiration chambers in weeks 8, 15, 23, 33 and 41 of lactation. Individual cow data were analysed in five different ways: (i) plotting the trend of methane per unit of dry matter intake (DMI) and milk yield over time; relating measured methane production to estimates derived from equations based on either; (ii) DMI or (iii) nutrient intake; (iv) relating residual feed intake (RFI) to methane emission; and (v) analysis of variance of cow × measurement interactions. The Holstein, Simmental and Jersey cows emitted on average 25, 25 and 26 g methane/kg DMI, respectively. There was no indication of individual cows with persistently low or high methane yield per kg DMI and per kg milk. Measured methane emissions differed from estimated values without a clear pattern, and the relationship between RFI and methane emission of the cows was weak. Finally, analysis of variance failed to show distinct patterns of methane per unit of DMI and milk for individual animals. The apparent lack of persistence of individual animal differences in methane yields suggests that genetic determination of this trait is of minor importance in dairy cows.

scholarly journals An Alternative Method of Construction and Analysis of Asymmetrical Factorial Experiment of the type 6x22 in Blocks of Size 12

2018 ◽  
Vol 40 (1) ◽  
pp. 137-150
Author(s):  
Dipa Rani Das ◽  
Sanjib Ghosh
Keyword(s):  
Analysis Of Variance ◽  
Interaction Effect ◽  
Factorial Experiment ◽  
Loss Of Information ◽  
Order Interaction ◽  
Alternative Method ◽  
Ordinary Type

This paper focuses on the construction and analysis of an extra ordinary type of asymmetrical factorial experiment which corresponds to fraction of a symmetrical factorial experiment as indicated by Das (1960). For constructing this design, we have used 3 choices and for each choice we have used 5 different cases. Finding the block contents for each case we have seen that there are mainly two different cases for each choice. In case of analysis of variance, we have seen that, for the case where the highest order interaction effect is confounded in 4 replications, the loss of information is same for all the choices. Again for the case where the highest order interaction effect is confounded in 3 replications, the loss of information is also same for all the choices and one effect which is confounded due to fractionation has the same loss of information for all the choices. The Chittagong Univ. J. Sci. 40 : 137-150, 2018

The assessment of net dry matter partitioning by stepwise multivariate analysis of variance

1988 ◽  
Vol 66 (4) ◽  
pp. 805-808 ◽  
Author(s):  
Peter A. Jolliffe ◽  
John Hoddinott
Keyword(s):  
Multivariate Analysis ◽  
Plant Growth ◽  
Analysis Of Variance ◽  
Dry Matter ◽  
Multivariate Analysis Of Variance ◽  
Dry Matter Partitioning ◽  
Experimental Treatments ◽  
Plant Components

A serial procedure is described involving the analysis of variance, multivariate analysis of variance, and stepwise multivariate analysis of variance. The procedure identifies significant contributions by different plant components to changes in net dry matter partitioning induced by experimental treatments. The procedure was applied to results from a study of plants grown at different atmospheric CO2 concentrations. It confirmed that CO2 treatments caused significant changes in dry matter partitioning and that the set of plant components that best expressed the effects of CO2 treatments changed during plant growth.

Effect of stubble cover on the efficacy of some soil-incorporated herbicides

1985 ◽  
Vol 25 (4) ◽  
pp. 927
Author(s):  
RJ Bateman ◽  
SR Walker
Keyword(s):  
Weed Control ◽  
Analysis Of Variance ◽  
Treatment Effects ◽  
Field Experiments ◽  
South Eastern ◽  
Plant Soil ◽  
Conventional Methods ◽  
Linear Correlations

Three field experiments were conducted near Kingaroy in south-eastern Queensland to determine the effect of sorghum stubble on the efficacy of the pre-plant, soil-incorporated herbicides pendimethalin, trifluralin and vernolate. Herbicides were applied to three stubble ranges (1 1 to 36% cover) in experiment 1, to stubble levels of 10, 20, 30, 40, and 50% cover in experiment 2, and approximately 0, 25 and 50% in experiment 3. In experiment 3 the effectiveness of incorporation with stubble mulch machinery consisting of chisel plough with attached rod weeder was compared with that achieved by conventional incorporation methods of coronet tines with mounted harrows and offset discs followed by tines and harrows. Treatment effects were examined by analysis of variance and by linear correlations between stubble cover and herbicide efficacy. Good to excellent weed control was achieved by all herbicides at stubble levels up to 50% cover with incorporation by stubble mulch machinery. Trifluralin efficacy was aided by stubble cover in one experiment. Incorporation by stubble mulch machinery was at least as effective as conventional methods.

scholarly journals Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis

2019 ◽  
Author(s):  
Robin Gomila
Keyword(s):  
Linear Regression ◽  
Fixed Effects ◽  
Treatment Effects ◽  
Causal Model ◽  
Nonlinear Modeling ◽  
Causal Effects ◽  
Binary Outcomes ◽  
Interaction Terms ◽  
Unbiased Estimates ◽  
Experimental Treatments

When the outcome is binary, psychologists often use nonlinear modeling strategies such as logit or probit. These strategies are often neither optimal nor justified when the objective is to estimate causal effects of experimental treatments. Researchers need to take extra steps to convert logit and probit coefficients into interpretable quantities, and when they do, these quantities often remain difficult to understand. Odds ratios, for instance, are described as obscure in many textbooks (e.g., Gelman & Hill, 2006, p. 83). I draw on econometric theory and established statistical findings to demonstrate that linear regression is generally the best strategy to estimate causal effects of treatments on binary outcomes. Linear regression coefficients are directly interpretable in terms of probabilities and, when interaction terms or fixed effects are included, linear regression is safer. I review the Neyman-Rubin Causal Model, which I use to prove analytically that linear regression yields unbiased estimates of treatment effects on binary outcomes.Then, I run simulations and analyze existing data on 24,191 students from 56 middle-schools (Paluck, Shepherd, & Aronow, 2016) to illustrate the effectiveness of linear regression. Based on these grounds, I recommend that psychologists use linear regression to estimate treatment effects on binary outcomes.

scholarly journals Analysis of Means by Ranks: Modification and Application in Contract Acquisition Analysis

2021 ◽  
pp. 47-60
Author(s):  
Saad T. Bakir
Keyword(s):  
Analysis Of Variance ◽  
Test Procedure ◽  
Nonparametric Tests ◽  
Study Data ◽  
Test Procedures ◽  
Test Analysis ◽  
Normality Assumption ◽  
Analysis Of Means ◽  
Normal Populations ◽  
Significant Difference

Analysis of Means by Ranks is a nonparametric statistical test procedure that was developed in Bakir (1989) but has rarely been applied in practice. This paper modifies and applies Analysis of Means by Ranks to a case study data involving the comparison of three contract proposals. For comparison purposes, we analyze the same data using the well-known Analysis of Variance, Analysis of Means, and the Kruskal-Wallis test. Analysis of Variance and Analysis of Means are two parametric (assume data to be samples from normal populations) test procedures whereas Kruskal-Wallis and Analysis of Means by Ranks are two nonparametric (or distribution-free) procedures. This paper shows that the parametric tests fail to detect a significant difference among three contract proposals, while the nonparametric tests do.  The conclusions of the parametric tests are in doubt because a descriptive statistics analysis indicates that the required normality assumption is in doubt; the nonparametric conclusions are more trustful because the normality assumption is not required by nonparametric procedures.

scholarly journals Biting Lice Control on Angora Goats in Northern Minnesota, 1994

1995 ◽  
Vol 20 (1) ◽  
pp. 325-325
Author(s):  
D. R. Brown ◽  
R. D. Moon
Keyword(s):  
Body Weight ◽  
Organic Matter ◽  
Analysis Of Variance ◽  
Treatment Effects ◽  
Hair Length ◽  
Sale Price ◽  
Commercial Formulation ◽  
Angora Goats ◽  
Biting Lice

Abstract Goats were 82 naturally infested animals divided into 3 groups based on sex and age. Barn 1 contained 39 adult does, barn 2 had 18 bucks and aged whethers, and barn 3 had 25 kid does and whethers. Within each barn goats were assigned to four treatments, balanced by weight and initial louse density. Treatments were control (untreated); Ectrin 1, Ectrin® pour-on at a rate of 1 oz solution per 25 lb body weight; Ectrin-2, Ectrin® pour-on at a rate of 1.5 oz solution per 25 lb body weight; and Malathion 57% EC spray commercial formulation applied at 40 psi to saturate 1.5-inch long fleeces. Treatments were applied at day 0 and day 14. Goats were weighed and examined for lice on day 0, (25 Jan 1994), and reexamined, reweighed, and shorn on days 49 and 234. Additional louse exams were made on days 33, 69, 105, 138, 164 and 202. Louse densities were judged by averaging counts of lice in five, 3-inch hair-parts on each animal’s right shoulder and fore-rib area. Fleeces were evaluated for features affecting sale price; hair length, organic matter and “slippage” (shedding and matting). Barns were treated as blocks and results were analyzed by 2-way analysis of variance (ANOVA); if treatment effects were significant (P < 0.05), means were compared by LSD test (P < 0.05).

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