Question 1: What is the difference between sample statistics and sample estimation? The concept of sample statistics is very broad (such as sample mean, sample median, sample variance, etc. ). However, not all the relationships between sample statistics and population distribution can be confirmed, but the relationships between some common statistics and population distribution have been proved.

For example, according to the central limit theorem, no matter what the population distribution is (whether normal or abnormal, known or unknown), it will approximately obey the normal distribution (provided that the sample size is large enough), and the mean value is equal, and the standard deviation of the sample is n times the root of population standard deviation.

Point estimation uses sample statistics to estimate the overall parameters. Because sample statistics are the values of a certain point on the number axis and the estimation results are also expressed by the values of a certain point, it is called point estimation. Point estimation and interval estimation belong to the problem of overall parameter estimation. What is demographic statistics? When a set of data is obtained from samples in the study, how to estimate the overall characteristics through this set of information, that is, how to infer the overall from local results, is called overall parameter estimation.

Question 2: Is the parameter estimator of the sample a definite value or a random variable? For a population, parameters are unique and certain, but they are usually unknown. Common parameters include population mean, population proportion and population standard deviation.

Question 3: What are the requirements for estimating the sample size reference mean (1)? The degree of change of the research object; (2) Required or allowable error (i.e. accuracy requirement); (3) Infer the required confidence. That is to say, when the phenomenon studied is more complex and the difference is greater, the requirement for sample size is greater; When the required precision is high and the requirement for inferability is high, the sample size is large. Therefore, if different cities infer separately, it is wrong in principle that big cities smoke more and small cities smoke less. Too much sampling in big cities is a waste, and too little sampling in small cities has no inferential value.

Question 4: Unbiased estimation refers to (). The value of the sample estimator is exactly equal to the overall parameter to be estimated. B, the sample estimator surrounds the overall parameter to be estimated, so that the expected value of the sample estimator of the parameter is equal to the real value of the parameter. The mathematical expectation of the estimator is equal to the estimated parameter, so it is called unbiased estimation. So, the answer is C.

Question 5: What does it mean to estimate the population with samples? How to write? Multiply the total by the frequency.