第11章 非参数统计法.ppt

第11章非参数统计法
Nonparametric Statistics
本章概要
Testing with Rank Sum
Z Test for Differences in Two Proportions (Independent Samples)
2 Test for Differences in Two Proportions (Independent Samples)
2 Test for Differences in c Proportions (Independent Samples)
2 Test of Independence
常见非参数法
Statistical Procedures for Hypothesis Testing that do Not Require a Normal Distribution
Because they are based on Counts or Ranks
A Random Sample is still required
The Nonparametric Approach Based on Counts
Count the number of times some event occurs
Use the binomial distribution to decide whether this count is reasonable or not under the null hypothesis
The Nonparametric Approach Based on Ranks
Replace each data value with its rank (1, 2, 3, …)
Use formulas and tables created for testing ranks
参数法及其效率Parametric Methods, Efficiency
Parametric Methods
Statistical procedures that require pletely specified model
., t tests, regression tests, F tests
Efficiency
A measure of the effectiveness of a statistical test
Tells how well it makes use of the information in the data
A more-efficient test can achieve the same results with a smaller sample size
优、缺点
Advantages of Nonparametric Testing
No need to assume normality
Avoids problems of transformation (., interpretation)
Can be used with ordinal data
Because ranks can be found
Can be much more efficient than parametric methods when distributions are not normal
Disadvantage of Nonparametric Testing
Less statistically efficient than parametric methods when distributions are normal
Often, this loss of efficiency is slight
中位数的检验( Median)
Without assuming a normal distribution
Note: The number of sample data values below a continuous population’s median follows a binomial distribution where p = and n is the sample size
The Sign Test(符号检验)
the modified sample size m, the number of data values different from the reference value q0
the limits in the table for this modified sample size
how many data