Evaluation of Moving Mean and Border Row Mean Covariance Analysis for Error Control in Yield Trials

Fernandez, George

doi:10.21273/jashs.115.2.241

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The basic design of the experiment or the way in which the analysis is performed must be changed to deal with this problem. A cyclic pattern in the residual plot is an indication for auto-correlation, nonindependent error (Fernandez, 1990a;Gomez and Gomez, 1984). If auto-correlated errors are observed in residual plots in special experimental layouts, a repeated measure of ANOVA (Fernandez, 1991) or moving mean covariance analysis (Fernandez, 1990a) may be appropriate to make adjustments for auto-correlation.…”

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confidence: 99%

Residual Analysis and Data Transformations: Important Tools in Statistical Analysis

Fernandez¹

1992

HortSci

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109

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Analysis of variance (ANOVA) is a commonly used statistical analysis in agricultural experiments. Additivity, variance homogeneity, and normality are often considered prerequisites for ANOVA (Cochran, 1943; Eisenhart, 1947). The interpretation of ANOVA is valid when the random errors are independently distributed according to a normal distribution with zero mean and an unknown but fixed variance (Kempthorne, 1952; Scheffe, 1959; Steel and Torrie, 1980). Failure to meet one or more of these assumptions affects the significance levels and the sensitivity of the F test (Gomez and Gomez, 1984; Kempthorne, 1952; Little and Hills, 1978) Thus, strong deviations from one or more of the assumptions must be checked and corrected before the statistical analysis and interpretation of the results. Discrepancies of many kinds between an assumed model and the data can be detected by studying the error component or residuals (Anscombe and Tukey, 1963; Emerson and Stoto, 1983). The residuals are the deviation from the observed and the predicted values according to the assumed model. If the assumptions about the validity of the model are valid, a residual plot (scatter plot between the residuals and the predicted values) will have a random distribution. If the residual plot has an unexplained systematic pattern, then the ANOVA model is not appropriate. Residual plots can be used to detect the violation of assumptions in ANOVA, such as variance heterogeneity (unequal variance], auto-correlated error (nonindependence), and the presence of outliers. Thus, it is crucial to examine the residuals before interpreting the data. Violation of assumptions in ANOVA Nonadditivity, variance heterogeneity, and nonnormality. The additivity requirement implies that the block and treatment effects should be additive. For example, in a randomized complete-block design, the differ

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confidence: 99%

Residual Analysis and Data Transformations: Important Tools in Statistical Analysis

Fernandez¹

1992

HortSci

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109

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“…Individual homogeneous field plots with more than single plants are commonly used as experimental units in annual and perennial crop studies. Blocking across the field variability gradient or measuring additional continuous covariates from each experimental unit and using analysis of covariance (Fernandez, 1990) are highly recommended in reducing experimental errors in designed comparative experiments. In tree crop studies, individual trees are usually treated as the experimental units.…”

Section: Statistical Issues Related To Designing Horticultural Experimentioning

confidence: 99%

Design and Analysis of Commonly Used Comparative Horticultural Experiments

Fernandez¹

2007

horts

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“…A repeated-measures analysis (Fernandez, 1991) was performed on ET cum on different days. For each ET cum , the average pan evaporation on either side was used as the covariate in the analysis of covariance (Fernandez, 1990a) to adjust for ET microvariation in the greenhouse.…”

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Comparing Turfgrass Cumulative Evapotranspiration Curves

Fernandez¹,

Love²

1993

HortSci

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Twenty-five commercially available turfgrass cultivars were evaluated for cumulative evapotranspiration (ETcum) attributes under progressive water stress for 0 to 21 and 0 to 24 days using the gravimetric mass balance method in two greenhouse studies. At the end of the water-stress treatment, the cultivars were scored visually for their green appearance on a 0 (no green) to 10 (100% green) scale. The Gompertz nonlinear model gave a best fit to ETcum vs. days adjusted for pan evaporation variation in the greenhouse compared with monomolecular and logistic nonlinear regression models. Two ETcum attributes—maximum evapotranspiration rates (ETmax) and inflection time (ti) (the time when the change in ET becomes zero)—were estimated for each cultivar using the Gompertz model. Based on final ETcum, ETmax, ti, and greenness score, `Bristol', `Challenger', and `Wabash' Kentucky bluegrass (Poa pratensis L.); `Shademaster' creeping fescue (Festuca rubra L.); `FRT-30149' fine fescue (F. rubra L.); and `Aurora' hard fescue (F. ovina var. duriuscula L. Koch.) were identified as low water-use cultivars.

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Evaluation of Moving Mean and Border Row Mean Covariance Analysis for Error Control in Yield Trials

Cited by 3 publications

References 7 publications

Residual Analysis and Data Transformations: Important Tools in Statistical Analysis

Residual Analysis and Data Transformations: Important Tools in Statistical Analysis

Design and Analysis of Commonly Used Comparative Horticultural Experiments

Comparing Turfgrass Cumulative Evapotranspiration Curves

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