CHAPTER-6 Sampling error and confidence intervals
CHAPTER-6 Sampling error and confidence intervals
Parameter population statistic sample
population sample statistic Parameter error
Section 1 sampling error of mean Section 2 t distribution Section 3 confidence intervals for the population mean
Section 1 sampling error of mean Section 2 t distribution Section 3 confidence intervals for the population mean
Section 1 sampling error of mean
Section 1 sampling error of mean
a simple random sample is a sample of size n drawn from a population of size n in such a way that every possible random samples n has the same probability of being selected. Variability among the simple random samples drawn from the same population is called sampling variability, and the probability distribution that characterizes some aspect of the sampling variability, usually the mean but not always, is called a sampling distribution. These sampling distributions allow us to make objective statements about population parameters without measuring every object in the population
A simple random sample is a sample of size n drawn from a population of size N in such a way that every possible random samples n has the same probability of being selected. Variability among the simple random samples drawn from the same population is called sampling variability, and the probability distribution that characterizes some aspect of the sampling variability, usually the mean but not always, is called a sampling distribution. These sampling distributions allow us to make objective statements about population parameters without measuring every object in the population
[EXample 11 The population mean of DBP in the Chinese adult men is 72mmhg with standard deviation 5mmHg 10 adult participants was chosen randomly from the chinese adult men here we can calculate the sample mean and sample standard deviation Supposing sampling 100 times What's the result?
[Example 1] The population mean of DBP in the Chinese adult men is 72mmHg with standard deviation 5mmHg. 10 adult participants was chosen randomly from the Chinese adult men, here we can calculate the sample mean and sample standard deviation. Supposing sampling 100 times, what’s the result?
X.S =72,=5 N X2, S, X3. S X 100 linkage
linkage = 72, = 5 N 1 X 1, S 2 X 2 , S 3 X 3, S 100 X100, S
If random samples are repeatedly drawn from a population with a mean u and standard deviation σ, We can find: 1 the sample means are different from the others 2 The sample mean are not necessary equal to population mean H 3 The distribution of sample mean is symmetric about HOW TO EXPLORE THE SAMPLING DISTRIBUTION FOR THE MEAN?
If random samples are repeatedly drawn from a population with a mean μ and standard deviation σ , we can find: 1 the sample means are different from the others 2 The sample mean are not necessary equal to population mean μ 3 The distribution of sample mean is symmetric about μ HOW TO EXPLORE THE SAMPLING DISTRIBUTION FOR THE MEAN?
The difference between sample statistics and population parameter or the difference among sample statistics are called sampling error
The difference between sample statistics and population parameter or the difference among sample statistics are called sampling error
In real life we sample only once, but we realize that our sample comes from a theoretical sampling distribution of all possible samples of a particular size. The sampling distribution concept provides a link between sampling variability and probability Choosing a random sample is a chance operation and generating the sampling distribution consists of many repetitions of this chance operation
❖ In real life we sample only once, but we realize that our sample comes from a theoretical sampling distribution of all possible samples of a particular size. The sampling distribution concept provides a link between sampling variability and probability. Choosing a random sample is a chance operation and generating the sampling distribution consists of many repetitions of this chance operation