BartlettTest | R Documentation |
Conducts Bartlett's test to evaluate whether multiple groups have equal variances, based on a formula interface and raw data vectors, without requiring a fitted model. This implementation provides flexibility for exploratory variance testing in custom workflows.
BartlettTest(formula, data, alpha = 0.05)
formula |
A formula of the form |
data |
A data frame containing the variables specified in the formula. |
alpha |
Significance level for the test (default is 0.05). |
Bartlett’s test is appropriate when group distributions are approximately normal. It tests the null hypothesis that all groups have equal variances (homoscedasticity).
Advantages: - Straightforward to compute. - High sensitivity to variance differences under normality.
Disadvantages: - Highly sensitive to non-normal distributions. - Less robust than alternatives like Levene’s test for skewed or heavy-tailed data.
An object of class "homocedasticidad"
, containing:
Statistic
: Bartlett's chi-squared test statistic.
df
: Degrees of freedom associated with the test.
p_value
: The p-value for the test statistic.
Decision
: A character string indicating the conclusion ("Heterocedastic" or "Homocedastic").
Method
: A character string indicating the method used ("Bartlett").
Bartlett, M. S. (1937). "Properties of sufficiency and statistical tests." Proceedings of the Royal Society of London, Series A, 160(901), 268–282.
data(d_e, package = "Analitica")
res <- BartlettTest(Sueldo_actual ~ labor, data = d_e)
summary(res)
summary(BartlettTest(Sueldo_actual ~ as.factor(labor), data = d_e))
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