费里德曼双向方差分析
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| 费里德曼双向方差分析 [2024/04/19 10:15] – [3.假设检验 Hypothesis Test] hant_g._cavendish | 费里德曼双向方差分析 [2024/04/19 10:17] (当前版本) – [3.假设检验 Hypothesis Test] hant_g._cavendish | ||
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| **Step 4: 代入检测统计量公式求出统计量观测值，根据题目比较观测值与临界值 Substitute the formula for the test statistic to find the observed value of the statistic, and compare the observed value with the critical value according to the topic** | **Step 4: 代入检测统计量公式求出统计量观测值，根据题目比较观测值与临界值 Substitute the formula for the test statistic to find the observed value of the statistic, and compare the observed value with the critical value according to the topic** | ||
| * 只有当✘< | * 只有当✘< | ||
| + | * The null hypothesis H0 can be rejected only if the observed value of ✘2r is greater than the critical value. | ||
| ===== 4.大样本情况下的费里德曼双向方差分析 The case of Large Samples ===== | ===== 4.大样本情况下的费里德曼双向方差分析 The case of Large Samples ===== | ||
| 与克-瓦氏单向方差分析一样，当样本数和样本容量较大时费里德曼双向方差分析的✘< | 与克-瓦氏单向方差分析一样，当样本数和样本容量较大时费里德曼双向方差分析的✘< | ||
| * As with the Kerr-Watt one-way ANOVA, the sampling distribution of the Freedman two-way ANOVA approximates the chi-square distribution with k-1 degrees of freedom when the number of samples and the sample size are large. | * As with the Kerr-Watt one-way ANOVA, the sampling distribution of the Freedman two-way ANOVA approximates the chi-square distribution with k-1 degrees of freedom when the number of samples and the sample size are large. | ||
费里德曼双向方差分析.1713521754.txt.gz · 最后更改: 2024/04/19 10:15 由 hant_g._cavendish