这里会显示出您选择的修订版和当前版本之间的差别。
— | stat:3descntests [2023/04/04 18:02] (当前版本) – 创建 - 外部编辑 127.0.0.1 | ||
---|---|---|---|
行 1: | 行 1: | ||
+ | ====== 描述与假设检验 ====== | ||
+ | > | ||
+ | > | ||
+ | >R 基础[[stat: | ||
+ | > | ||
+ | > | ||
+ | > | ||
+ | > | ||
+ | > | ||
+ | > | ||
+ | >Seaborn [[https:// | ||
+ | > | ||
+ | |||
+ | > | ||
+ | |||
+ | ====CSV操作==== | ||
+ | [[http:// | ||
+ | < | ||
+ | df = pd.read_csv(' | ||
+ | df.to_csv(' | ||
+ | </ | ||
+ | R | ||
+ | < | ||
+ | df< | ||
+ | df< | ||
+ | write.table | ||
+ | </ | ||
+ | ====groupby操作==== | ||
+ | [[http:// | ||
+ | =====数据初始处理===== | ||
+ | ^ Name ^ Python | ||
+ | ^ 统计描述·describe | ||
+ | ^ 算数均数·tmean | ||
+ | ^ 方差·variance | ||
+ | ^ 标准差·standard deviation | ||
+ | ^ 标准误·sem | ||
+ | ^ 变异系数·variation | ||
+ | ^ 几何均数·gmean | ||
+ | ^ 贝叶斯均数 | ||
+ | ^ 调和平均数·hmean | ||
+ | ^ 减尾后均数·trim_mean | ||
+ | ^ 峰度·kurtosis | ||
+ | ^ 偏度·skewness | ||
+ | ^ 查找重复值 | ||
+ | ^ 双减尾 | ||
+ | ^ 单减尾 | ||
+ | ===分布检验=== | ||
+ | ^ Name ^ Python | ||
+ | ^ normaltest | ||
+ | ^ Shapiro-Wilk test for normality | ||
+ | ^ Kolmogorov-Smirnov test·KS检验 | ||
+ | | ::: | [[https:// | ||
+ | ^ Anderson-Darling test | [[https:// | ||
+ | | ::: | [[https:// | ||
+ | ^ kurtosistest | ||
+ | ^ skewtest | ||
+ | | |||| | ||
+ | ^ O’Brien transform 方差齐性 | ||
+ | ^ Bartlett’s test for equal variances | ||
+ | ^ Levene test for equal variances | ||
+ | ^ Jarque-Bera | ||
+ | ^ Fligner-Killeen test for equality of variance | ||
+ | |||
+ | =====假设检验===== | ||
+ | ^ Name ^ 名称 | ||
+ | ^ Student' | ||
+ | | ::: | ::: | [[https:// | ||
+ | | ::: | ::: | [[https:// | ||
+ | | ::: | ::: | [[https:// | ||
+ | ^ ANOVA | 方差分析 | ||
+ | | | 方差不齐的多组比较 | ||
+ | ^ chisquare | ||
+ | ^ chi2_contingency | ||
+ | ^ Fisher exact test 2x2 | ||
+ | ^ friedmanchisquare | ||
+ | ^ combine_pvalues | ||
+ | ^ pearsonr | ||
+ | ^ pointbiserialr | ||
+ | ^ linregress | ||
+ | | ||||| | ||
+ | ^ rankdata | ||
+ | ^ Wilcoxon signed-rank test | ||
+ | ^ Wilcoxon | ||
+ | ^ mannwhitneyu | ||
+ | ^ spearmanr | ||
+ | ^ kendalltau | ||
+ | ^ weightedtau | ||
+ | ^ tiecorrect | ||
+ | ^ Kruskal-Wallis H-test | ||
+ | ^ Bernoulli experiment | ||
+ | ^ Mood’s median test | 中位数检验 | ||
+ | ^ Mood’s test for equal scale parameters | ||
+ | | ||||| | ||
+ | ^ Box-Cox | ||
+ | ^ Wasserstein distance | ||
+ | ^ energy distance | ||
+ | |||
+ | ====多重比较==== | ||
+ | [[http:// | ||
+ | [[http:// | ||
+ | [[http:// | ||
+ | |||
+ | 方法比较[[http:// | ||
+ | Python post hoc包:[[https:// | ||
+ | |||
+ | < | ||
+ | |||
+ | ====方法选择==== | ||
+ | < | ||
+ | 1.连续数据,正态分布,线性关系,用pearson相关系数是最恰当,当然用spearman相关系数也可以, | ||
+ | 就是效率没有pearson相关系数高。 | ||
+ | 2.上述任一条件不满足,就用spearman相关系数,不能用pearson相关系数。 | ||
+ | 3.两个定序测量数据之间也用spearman相关系数,不能用pearson相关系数。 | ||
+ | 用pearson处理的数据,必须满足一下条件:成对数据、连续、整体是正态分布的。 | ||
+ | |||
+ | 其实, Spearman 和Pearson相关系数在算法上完全相同. 只是PEARSON相关系数是用原来的数值计算积差相关系数, | ||
+ | </ | ||
+ | ====方法选用标准==== | ||
+ | |数据是否线性| | | ||
+ | |方差齐不齐|Satterthwate Wilcoxon| | ||
+ | |分布正态否|Bonferroni法校正P值 Wilcoxon检验 Fridman| | ||
+ | |多组完全随机|Kruscal-Wallis| | ||
+ | |||
+ | < | ||
+ | Fisher最小显著差异法(Fisher' | ||
+ | 学生t检验(Student' | ||
+ | 曼-惠特尼 U 检定(Mann-Whitney U) | ||
+ | 回归分析(regression analysis) | ||
+ | 相关性(correlation) | ||
+ | 皮尔森积矩相关系数(Pearson product-moment correlation coefficient) | ||
+ | 史匹曼等级相关系数(Spearman' | ||
+ | 卡方分布(chi-square ) | ||
+ | </ | ||
+ | |||
+ | ====写作==== | ||
+ | [[stat: |