[Math Review] Statistics Basic: Estimation
作者:互联网
Two Types of Estimation
One of the major applications of statistics is estimating population parameters from sample statistics. There are types of estimation:- Point Estimate: the value of sample statistics
Point estimates of average height with multiple samples (Source: Zhihu)
- Confidence Intervals: intervals constructed using a method that contains the population parameter a specified proportion of the time.
95% confidence interval of average height with multiple samples (Source: Zhihu)
Confidence Interval for the Mean
Population Variance is known
Suppose that M is the mean of N samples X1, X2, ......, Xn, i.e.
According to Central Limit Theorem, the the sampling distribution of the mean M is
where μ and σ2 are the mean and variance of the population respectively. If repeated samples were taken and the 95% confidence interval computed for each sample, 95% of the intervals would contain the population mean. So the 95% confidence interval for M is the inverval that is symetric about the point estimate μ so that the area under normal distribution is 0.95.
That is,
Since we don't know the mean of population, we could use the sample mean instead.
Population Variance is Unknown
Dregree of Freedom
The degrees of freedom (df) of an estimate is the number of independent pieces of information on which the estimate is based. In general, the degrees of freedom for an estimate is equal to the number of values minus the number of parameters estimated en route to the estimate in question.
If the variance in a sample is used to estimate the variance in a population, we couldn't calculate the sample variace as
That's because we have two parameters to estimate (i.e., sample mean and sample variance). The degree of freedom should be N-1, so the previous formula underestimates the variance. Instead, we should use the following formula
where s2 is the estimate of the variance and M is the sample mean. The denominator of this formula is the degree of freedom.
Student's t-Distribution
Suppose that X is a random variable of normal distribution, i.e., X ~ N(μ, σ2)
is sample mean and
is sample deviation.
is a random variable of normal distribution.
is a random variable of student's t distribution.
The probability density function of T is
where is the degree of freedom, is a gamma function.
标签:population,Statistics,freedom,Review,sample,variance,estimate,Estimation,mean 来源: https://www.cnblogs.com/sherrydatascience/p/10354428.html