{ll} \hat y_1 = \beta_0 + \beta_1x_{11}+ \beta_2x_{12} + ...++ \beta_mx_{1m}\\ \hat y_2 = \beta_0 + \beta_1x_{21}+ \beta_2x_{22} + ...++ \beta_mx_{2m}\\ ...\\ \hat y_n = \beta_0 + \beta_1x_{n1}+ \beta_2x_{n2} + ...++ \beta_mx_{...
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作为提醒,用于比较两个独立组的 t 统计量计算如下: t=¯X1−¯X2√S21n1+S22n2t=X1¯−X2¯S12n1+S22n2 其中¯X1X¯1和¯X2X¯2是两组的均值,S21S12和S22S22是每组的方差,n1n1和n2n2是两组的大小。在均值无差异的零假设下,这个统计量根据 t 分布分布,使用韦尔奇检验计算自由度(如前面...
<smilingsamay@gmail.com> Reviewed-by: Maciek Sakrejda <m.sakrejda@gmail.com> Reviewed-by: Lukas Fittl <lukas@fittl.com> Reviewed-by: Andres Freund <andres@anarazel.de> Reviewed-by: Justin Pryzby <pryzby@telsasoft.com> Discussion: https://postgr.es/m/20200124195226.lth52iydq2n2uilq@...
其中n1n1和n2n2是样本大小,s21s12和s22s22分别是两组的标准偏差。请注意,这在精神上与 t 统计量非常相似——主要区别在于 t 统计量的分母是基于均值的标准误差,而 Cohen's D 的分母是基于数据的标准偏差。这意味着随着样本量的增加,t 统计量会增长,而 Cohen's D 的值将保持不变。 表10.1:Cohen's D 的...
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scipy.stats.poisson_means_test(k1, n1, k2, n2, *, diff=0, alternative='two-sided')# 执行泊松均值检验,又称“E-test”。 这是对原假设的检验,即两个泊松分布的均值之间的差异为 diff。样本以在大小为 n1 和 n2 的测量间隔(例如时间、空间、观察次数)内观察到的事件 k1 和 k2 的数量提供。
t = X 1 ˉ − X 2 ˉ S 1 2 n 1 + S 2 2 n 2 t = \frac{\bar{X_1} - \bar{X_2}}{\sqrt{\frac{S_1^2}{n_1} + \frac{S_2^2}{n_2}}}t=n1S12+n2S22X1ˉ−X2ˉ 其中X ˉ 1 \bar{X}_1Xˉ1和X ˉ 2 \bar{X}_2Xˉ2是两组的均值,S 1 2 S^2_1S12和S...
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