Ross F. ColleryKerry N. VethAdam M. DubisJoseph CarrollBrian A. Link
我们可以进一步挖掘。Tukey检验提供“多重检验”,它将成对地查看均值的差异, Tukey multiple comparisons of means 95% family-wise confidence level $PART diff lwr upr p adj non_vol.-non_part. 0.60416 -0.04784 1.2561 0.07539 volontaire-non_part. 6.66379 5.92912 7.3984 0.00000 volontaire-non_vol. 6.05...
我们可以进一步挖掘。Tukey检验提供“多重检验”,它将成对地查看均值的差异, Tukey multiple comparisons of means95%family-wise confidence level $PARTdiff lwr upr p adjnon_vol.-non_part. 0.60416 -0.04784 1.2561 0.07539volontaire-non_part. 6.66379 5.92912 7.3984 0.00000volontaire-non_vol. 6.05962 5.5407...
Pairwise comparisons using t tests with pooled SD data: NOTE and PART non_part. non_vol. non_vol. 0.03 - volontaire <2e-16 <2e-16 如果我们将“非自愿”和“非参与”这两种方式结合起来,并将这种方式与“自愿”方式进行比较,我们最终将对平均值进行检验, Welch Two Sample t-test data: NOTE[PA...
我认为R语言一定有相关办法,查询之后,收获如下:使用agricolae包,有便捷的函数能够直接对两两比对然后分组进行实现。以Tukey为例,示例代码如下: library(agricolae) data(sweetpotato) model<-aov(yield~virus, data=sweetpotato) out <- HSD.test(model,"virus", group=TRUE,console=TRUE, ...
athe effects are short term. Rachal & Hoffman (1986) reported that “students appear to have considerably fewer difficulties with grade-level basic skills when they are both retained and provided remediation as soon as inadequate basic skills performance is noted (p. 25).” 作用是短期的。 Rachal...
One-way ANOVA calculator includes the Tukey HSD test. Calculates the effect size and checks the assumptions: normality, equality of variances, test power.
我们可以进一步挖掘。Tukey检验提供“多重检验”,它将成对地查看均值的差异, Tukey multiple comparisons of means 95% family-wise confidence level $PART diff lwr upr p adj non_vol.-non_part. 0.60416 -0.04784 1.2561 0.07539 volontaire-non_part. 6.66379 5.92912 7.3984 0.00000 ...
ANOVA with Tukey’s multiple-comparison test. c, Percentage body weight change. Data are mean ± s.e.m. n = 10 mice per group. P values were calculated using one-way ANOVA with Tukey’s multiple-comparison test. d, Average energy expenditure. Data are mean ± s.e.m. ...
We show that the One-way ANOVA and Tukey-Kramer (TK) tests agree on any sample with two groups. This result is based on a simple identity connecting the Fisher-Snedecor and studentized probabilistic distributions and is proven without any additional assumptions; in particular, the standard ANOVA...