统计学_Wilcoxon signed-rank test(python脚本)
python机器学习-乳腺癌细胞挖掘(博主亲自录制视频)
https://study.163.com/course/introduction.htm?courseId=1005269003&utm_campaign=commission&utm_source=cp-400000000398149&utm_medium=share
机器学习,统计项目联系QQ:231469242
两个配对样本,均匀分布,非正太分布
Wilcoxon signed-rank test
曼-惠特尼U检验Mann–Whitney Test
两个独立样本,均匀分布,非正太分布
https://en.wikipedia.org/wiki/Frank_Wilcoxon
ranking method
这是a=0.05对应得样本数
critical value 对应样本数,20样本对应的关键值为52
positive sum:
差异为正值的排名和
negative sum:
差异为负值的排名和
如果negative sum小于52,表明两组数有显著差异,推翻原假设
http://blog.sina.com.cn/s/blog_4bcf5ebd0101422n.html
SPSS学习笔记之——两配对样本的非参数检验(Wilcoxon符号秩检验)
一、概述
非参数检验对于总体分布没有要求,因而使用范围更广泛。对于两配对样本的非参数检验,首选Wilcoxon符号秩检验。它与配对样本t检验相对应。
二、问题
为了研究某放松方法(如听音乐)对于入睡时间的影响,选择了10名志愿者,分别记录未进行放松时的入睡时间及放松后的入睡时间(单位为分钟),数据如下笔。请问该放松方法对入睡时间有无影响。
本例可以采用配对样本t检验,但由于样本量少,数据可能不符合正太分布,所以考虑用非参数检验。
三、统计操作
数据视图
菜单选择
打开如下的对话框
该对话框有三个选项卡,第一个选项卡会根据第三个选项卡的设置自动设置,故一般不用手动设定。点击进入“字段”选项卡。将“放松前”、“放松后”均选入右边“检验字段”框中。
点击进入“设置”对话框,选择检验方法,切换为“自定义检验”,选择“Wilcoxon匹配样本对符号秩(二样本)”复选框。“检验选项”可以设定显著性水平。
点击“运行”按钮,输出结果
四、结果解读
这就是输出结果。原假设示放松前好放松后差值的中位数等于0,P=0.015<0.05,拒绝原假设,认为放松前后有统计学差异。
双击该表格,会弹出如下的“模型浏览器”窗口,可以看到更详细的信息。如下图。
# -*- coding: utf-8 -
import scipy.stats as stats
data1=[21,12,12,23,19,13,20,17,14,19]
data2=[12,11,8,9,10,15,16,17,10,16]
stats.wilcoxon(data1,data2)
'''
Out[2]: WilcoxonResult(statistic=2.0, pvalue=0.01471359242280415)
p值小于0.05,两组数据有显著差异
'''
https://study.com/academy/lesson/non-parametric-inferential-statistics-definition-examples.html
Normal
What is normal? At least in the world of statistics, this has nothing to do with how someone dresses, acts, or what their beliefs are. Normal data comes from a population with a normal distribution. A normal distribution is a distribution that has a symmetrical bell-shaped curve to it, which you're probably well aware of.
Keep this concept in mind as we go over the major differences between parametric and non-parametric statistics.
Parametric Methods
First, let's define our terms really simply. When we talk about parameters in statistics, what are we actually hinting at? Parameters are descriptive measures of population data, such as the population mean or population standard deviation.
When the variable we are considering is approximately (or completely) normally distributed, or the sample size is large, we can use two inferential methods that are concerned with parameters - appropriately called parametric methods - when performing a hypothesis test for a population mean. For instance, if we find that the distribution of the average salary of a sample looks like a bell curve, then parametric methods may be used.
These two methods are probably ones you've heard of before. They are the z-test, which we'd use when the population standard deviation is known to us; or the t-test, which we'd use when the population standard deviation is not known to us.
Non-Parametric Methods
Inferential methods that are not concerned with parameters are known, easily enough, as non-parametric methods. However, this term is also more broadly used to refer to many methods that are applied without assuming normality. So, for instance, if we find that the distribution of the average salary of a sample looks like the histogram you see on the screen now [see video], which is nothing close to that of a bell curve, then we will have to turn to non-parametric methods.
Such non-parametric methods have their pros and cons. On the pro side, these methods are usually simpler to compute and are more resistant to extreme values when compared to parametric methods. On the con side, if the requirements for the use of a parametric method are actually met, non-parametric methods do not have as much power as the z-test or t-test.
By power, I simply mean the probability of avoiding a type II error, which is an error where we fail to reject the false null hypothesis.
Example of a Non-Parametric Method
One example of a non-parametric method is the Wilcoxon signed-rank test. This is a test that assumes the variable under consideration does not need a specific shape and doesn't have to be normally distributed, but is symmetric in its distribution nonetheless. This means that it can be sliced in half to produce two mirror images.
So, for example, a right-skewed or left-skewed distribution would not be appropriate for this test since it's not symmetric. But a normal, symmetric bimodal, triangular, or uniform distribution would be a fit for this test since any one of those can be sliced in half to produce two mirror images of one another.
Other non-parametric tests include the likes of:
- The Kruskal-Wallis test
- The Mann-Whitney U test
- The sign test
Lesson Summary
Normal data comes from a population with a normal distribution. A normal distribution is a distribution that has a symmetrical bell-shaped curve to it, which you're probably well aware of.
Inferential methods that are concerned with parameters are appropriately called parametric methods, and include the z-test and t-test. Parameters are descriptive measures of population data.
Inferential methods that are not concerned with parameters are known as non-parametric methods. This term is also more broadly used to refer to many methods that are applied without assuming normality.
While non-parametric methods are easier to compute than parametric ones, they do not have as much power as parametric methods if the requirements for the use of a parametric method are met. By power, I simply mean the probability of avoiding a type II error, which is an error where we fail to reject the false null hypothesis.
An example of a non-parametric method is the Wilcoxon signed-rank test. This is a test that assumes the variable under consideration does not need a specific shape and doesn't have to be normally distributed, but is symmetric in its distribution nonetheless.
https://study.163.com/provider/400000000398149/index.htm?share=2&shareId=400000000398149( 欢迎关注博主主页,学习python视频资源,还有大量免费python经典文章)
统计学_Wilcoxon signed-rank test(python脚本)的更多相关文章
- hive使用python脚本导致java.io.IOException: Broken pipe异常退出
反垃圾rd那边有一个hql,在执行过程中出现错误退出,报java.io.IOException: Broken pipe异常,hql中使用到了python脚本,hql和python脚本最近没有人改过, ...
- 如何手动写一个Python脚本自动爬取Bilibili小视频
如何手动写一个Python脚本自动爬取Bilibili小视频 国庆结束之余,某个不务正业的码农不好好干活,在B站瞎逛着,毕竟国庆嘛,还让不让人休息了诶-- 我身边的很多小伙伴们在朋友圈里面晒着出去游玩 ...
- Wilcoxon Signed Rank Test
1.Wilcoxon Signed Rank Test Wilcoxon有符号秩检验(也称为Wilcoxon有符号秩和检验)是一种非参数检验.当统计数据中使用“非参数”一词时,并不意味着您对总体一无所 ...
- 2.3 Hive的数据类型讲解及实际项目中如何使用python脚本对数据进行ETL
一.hive Data Types https://cwiki. apache. org/confluence/display/HiveLanguageManual+Types Numeric Typ ...
- freeswitch嵌入python脚本
操作系统:debian8.5_x64 freeswitch 版本 : 1.6.8 python版本:2.7.9 开启python模块 安装python lib库 apt-get install pyt ...
- python脚本后台运行
问题描述: 环境: CentOS6.4 一个用python写的监控脚本test1.py,用while True方式一直运行,在ssh远程(使用putty终端)时通过以下命令启动脚本: python t ...
- 某互联网后台自动化组合测试框架RF+Sikuli+Python脚本
某互联网后台自动化组合测试框架RF+Sikuli+Python脚本 http://www.jianshu.com/p/b3e204c8651a 字数949 阅读323 评论1 喜欢0 一.**Robo ...
- 动态执行python脚本
前言 存在许多独立的python脚本,这些脚本可能会增加,也可能会减少,现在需要按照某种顺序调度这些程序.在python的standard library中,有一个模块imp可以实现动态的调用ptho ...
- 一个获取指定目录下一定格式的文件名称和文件修改时间并保存为文件的python脚本
摘自:http://blog.csdn.net/forandever/article/details/5711319 一个获取指定目录下一定格式的文件名称和文件修改时间并保存为文件的python脚本 ...
- SecureCRT中python脚本编写
SecureCRT中python脚本编写学习指南 SecureCRT python 引言 在测试网络设备中,通常使用脚本对设备端进行配置和测试以及维护:对于PE设备的测试维护人员来说使用较多是Secu ...
随机推荐
- LVS DR模型RS端修改配置脚本
#!/bin/bash vip=x.x.x.x in start) > /proc/sys/net/ipv4/conf/all/arp_ignore > /proc/sys/net/ipv ...
- linux下sendmail邮件系统安装详情
介绍 sendmail是linux系统中一个邮箱系统,如果我们在系统中配置好sendmail就可以直接使用它来发送邮箱.sendmail的配置文件/etc/mail/sendmail.cf ...
- 使用Django的ORM详细操作
1.自己动手创建数据库 create database 数据库名; 2.在Django项目中设置连接数据库的相关配置(告诉Django连接哪一个数据库) #在数据库相关的配置 DATABASES = ...
- vue 内存数组变化监听
watch: { carts: { handler(val, oldVal) { subtotal(this.carts); console.log(this.carts) }, deep: true ...
- Darknet版YOLO安装与配置
Darknet配置和安装 1. 安装显卡驱动 首先查看一下自己的电脑需要怎样的驱动,我们可以先到 http://www.nvidia.com/Download/index.aspx 查询下我们需要的是 ...
- Ansible批量远程管理Windows主机(部署与配置)
一.测试环境介绍 Ansible管理主机: 系统: CentOS6.8 IP Addr: 172.16.10.22 Linux管理服务器需安装pip.pywinrm插件 Windows客户端主机: ...
- C#将html table 导出成excel实例
public void ProcessRequest (HttpContext context) { string elxStr = "<table><tbody>& ...
- [CF#592 E] [二分答案] Minimizing Difference
链接:http://codeforces.com/contest/1244/problem/E 题意: 给定包含$n$个数的数组,你可以执行最多k次操作,使得数组的一个数加1或者减1. 问合理的操作, ...
- nginx第六天
nginx正向代理 反向代理 Nginx正向代理配置 Nginx正向代理使用场景并不多见. 需求场景1: 如果在机房中,只有一台机器可以联网,其他机器只有内网,内网的机器想用使用yum安装软件包,在能 ...
- http协议和i/o模型
http协议----基于请求报文和响应报文完成一次http事务 应用层协议格式有两种: 文本(开发容易,但交互解析困难如http smtp),二进制(交互解析容易,但理解起来困难memocache) ...