What is the relationship and difference between a parameter and a statistic

1. Parameters and statistics are two different concepts. The parameter is an object, or a variable that affects the object. Statistics is the quantization of parameters.

2. Parameters, also called parameter variables.

is a variable.

When we study the current problem, we are concerned with the changes in certain variables and the interrelationships between them, one or some of which are called independent variables.

The other or others are called dependent variables.

If we introduce one or more variables to describe the change between the independent variable and the dependent variable, the introduced variable is not the variable that must be studied in the current problem, and we call such a variable a parameter or parameter.

3. Statistics are the variables used in statistical theory to analyze and test data. A macroquantity is a statistical average of a large number of microquantities.

It has the significance of statistical averaging, and for a single microscopic particle, the macroscopic quantity is meaningless. Macroscopic quantities of statistical average nature relative to microscopic quantities are also called statistics. It should be pointed out that the physical quantities that describe the macroscopic world, such as velocity and kinetic energy, can actually be said to be macroscopic quantities, but macroscopic quantities do not all have the nature of statistical average, so macroscopic quantities are not all statistics.

A parameter is a generalized numerical measure used to describe the characteristics of a population, and a statistic is a generalized numerical measure used to describe the characteristics of a sample.

Since the population data is usually unknown, the parameter is an unknown one.

A statistic is a quantity calculated from sample data, which is a function of a sample. Since the sample has been drawn, the statistic is known.

<> parameters are statistical indicators that describe the overall situation, and the eigenvalues of the sample are called statistics.

Differences: 1. The parameter is the quantity calculated from the population, representing the overall characteristics, a constant. A statistic is a quantity calculated from a sample, which describes the condition of a set of data, is a variable, and changes with the sample;

2. The parameters are often represented by Greek letters, and the sample statistics are often represented by English letters.

Contact: Parameters are usually derived from sample eigenvalues.

The differences are as follows:

1. The applicable data types are different.

Parametric statistics are often used for fixed-distance or fixed-ratio data, while non-parametric statistics are often used for data consisting of only some grades, or the data to be analyzed does not meet the assumptions required by the parametric test, so the parametric test cannot be applied.

2. The assumptions about the parameters are different.

Parametric statistics requires people to estimate or test the parameters in the question; The questions asked by non-parametric statistics do not contain parameters and cannot be tested with parameters.

3. The degree of dependence on the whole is different.

In parameter statistics, the distribution form or distribution family of the population needs to be given in order to estimate and test the parameters. In non-parametric statistics, no assumptions are made about the population distribution or only very general assumptions, and the dependence on the population is low, but the characteristic distribution of the population is inferred from the sample is not a parameter value.

4. The scope of application is different.

Since each specific parameter statistics method is based on a specific theoretical distribution, the parameter statistics have certain requirements and limitations on the data to be analyzed and processed. Because non-parametric statistics do not rely on a specific theoretical distribution, the requirements for data conditions are relatively loose and have a wide range of applications.

In statistics, the two most basic forms of statistical inference are: parameter estimation and hypothesis testing, most of which are related to normal theory, which is called parametric statistics. In parametric statistics, the form of distribution or family of distributions of a population is often given, while parameters such as mean and variance are unknown.

The task is to estimate or test these parameters. When the distribution is assumed, the inference has a high precision.

Non-ginseng bai

Mathematics is one of the important branches of applied statistics. Non-parametric statistics are different from traditional ones.

The basic characteristics of DAO parametric statistics are that the modular weight type of non-parametric statistical analysis is usually more lenient in the assumptions of the model and data. Generally speaking, non-parametric statistics is a method that does not make detailed assumptions about the specific form of data distribution, tries to obtain the structural relationship of data from the data itself, and gradually establishes a mathematical model and statistical model of the research object.

r-squared: coefficient of determination, the proportion of the total variation of the dependent variable that can be explained by the independent variable through the regression relation. If r squared is 0 8, it means that the regression relationship can explain the variation of the dependent variable 80.

In other words, if we can control the independent variable to remain unchanged, the degree of variation of the dependent variable will be reduced by 80

In statistics, the r-squared value is calculated as follows:

r-squared value regression sum of squares (ssreg) total sum of squares (sstotal), where regression sum of squares, sum of total squares, and sum of squares of residuals (ssresid).Extended Materials1. The meaning of p does not indicate the size of the difference between the two groups, and p reflects whether the difference between the two groups is statistically significant, and does not indicate the size of the difference. Therefore, compared with the control group, the acquisition of P < for drug C and P for drug D does not mean that the efficacy of D is stronger than that of C.

2. When p >, the difference is not significant, and according to the statistical principle, it can be seen that the invalid hypothesis cannot be denied, but it is not considered that the invalid hypothesis is definitely valid. In the statistical analysis of drug efficacy, it does not indicate the equivalence of the two drugs. There is no statistical basis for which "the difference between the two groups is not significant" and "the two groups are basically equivalent".

For example, there are 50 female students, and 50 female students are the statistical value. The parameters are sample mean, total value, variance, standard deviation, proportion, population mean, total value, variance, standard deviation, proportion, etc.

Statistics and parameters are two corresponding concepts, parameters describe the population (such as population proportion, population mean, population variance, etc.), while statistics describe the sample (such as sample proportion, sample variance, sample mean, etc.). As for the statistical value, I think it is just the final statistical result of a certain problem, not a statistical term.

Statistics are variables used in statistical theory to analyze and test data.

A parameter, also called a parameter, is a variable.

Parameters are estimated by statistical.

1. Statistics: statistical indicators of sample characteristics.

After studying the sample, some indicators will be obtained, such as what is the level of flat dust staring and what is the degree of dispersion, and this description of the sample is the statistic. We often use statistics.

2. A parameter, also called a parameter, is a variable. When studying the problem at hand, it is concerned with the changes in certain variables and their interrelationships, one or some of which are called independent variables and the other or others are called dependent variables.

The difference between the two: 1. The object is different.

The difference between statistics and population parameters is that the objects are different, the object of statistics is a sample, and the object of population parameters is a population.

Statistical analysis, the final hope is to get the overall analysis, that is, the overall side parameters, but in fact, due to various reasons, such as technology, cost, time, etc., are used for statistical analysis, and the analysis statistics are intended to extrapolate the overall parameters.

2. The application field is different

Parameters: Mathematics, Physics, Computer.

Statistics: Statistical theory.

3. The numerical characteristics of the reaction are different:

Parameter: A numerical feature that reflects the overall characteristic.

Statistic: A numerical feature that reflects the characteristics of a sample.