Book 1_Quan_Estimation and interferce Flashcards
- Probability sampling
refers to selecting a sample when we know the probability of each sample member in the overall population
random sampling
each item is assumed to have the same probability of being selected.
Probability Sampling Methods
+ Simple random sampling
+ systematic sampling
+ Stratified random sampling
+ Cluster sampling
Nonprobability Sampling Methods
+ Convenience sampling
+ Judgmental sampling
The central limit theorem
for simple random samples of size n from a
population with a mean μ and a finite variance σ
2, the sampling distribution of the
sample mean approaches a normal probability distribution with mean μ and a variance equal to as the sample size becomes large.
The central limit theorem characteristics
+ The sample size is
sufficiently large, which usually means n ≥ 30.
+ The mean of the population, μ, and the mean of the distribution of all possible sample means are equal.
+ The variance of the distribution of sample means is , the population variance divided by the sample size
The standard error of the sample mean
the standard deviation of the distribution of the sample means
The standard error of the sample mean calculation
When the standard deviation of the population, σ, is known, the standard error of the
sample mean is calculated as:
Sigma x = Sigma/Căn n
- When population’s standard diviation is unknow, take the number of a sample
Sx = S/Căn n
- Point estimate
Sample mean is a point estimate of a population mean
- Confidence intervals:
The range of confidence that Ha true
- Compare T-statistic and Z-statistic
- Normal distribution
- Known variance: Z
- Unknow variance:
+ Population variance is unknow and a small sample size => T-statistic
+ Population variance is unknow and a large sample size => Z and T-statistic
- Unnormal distribution
- Small sample size: NA
- Lange sample size:
+ Known variance: Z
+ Unknow variance: T
