ANACOVAAnalysis of Covariance (statistical method)
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ANACOVA enabled simultaneous comparisons of the three dust types using their least square means instead of their arithmetic means to account for other effects in the model caused by having an unbalanced design.
ANACOVA is a method the objective of which is to minimize the effect of these quantitative variables and improve the precision of the analysis.
According to the ANACOVA model, the quantitative variable that affects the response variable is called the covariate.
The second section describes the basic concepts of covariance analysis and the ANACOVA model.
According to Montgomery[11], the basic assumptions of the ANACOVA model described above are as follows:
Among them, the ANOVA, ANACOVA and regression are the most commonly used statistical methods.
The assumptions presented in the second section need be tested before performing ANACOVA and ANOVA analysis.
The ANACOVA results generated by the SAS package are presented in Tables IV and V and the results can be interpreted as follows:
Second, the effect of the air pollution source is not significant based on ANOVA (P-value = 0.0610), but significant based on ANACOVA (P-value = 0.0002).
Tukey's studentized range (HSD) test Tukey grouping Mean N Environ(a)/material(b) A 0.037974 24 a3(a) A B A 0.032587 24 a2(a) B A B A 0.030662 24 a1(a) B B 0.026541 24 a4(a) B B 0.025208 24 a5(a) A 0.034858 60 b1(b) B 0.026331 60 b2(b) Note: Means with the same letter are not significantly different According to the analysis of ANACOVA, some suggestions for the case study can be made as follows: