What are the issues that need to be paid attention to when performing regression and correlation ana

1) Straight-line regression analysis is being performed.

Previously, a scatter plot should be drawn.

2) When doing regression analysis, it is necessary to pay attention to whether there is practical significance between the two variables.

3) When there is a linear relationship between two variables, it does not necessarily mean that there is a causal relationship between them.

4) After the regression equation is established, the regression coefficient must be hypothetically tested.

5) When using the regression equation for estimation and **, it is generally only applicable to the original observation range, that is, the range of values of the independent variables, and the range cannot be expanded at will.

1.Define the difference.

2.Regression analysis should be of practical significance, and regression analysis should not be carried out arbitrarily on two unrelated phenomena, ignoring the internal relationship and laws between things and phenomena; For example, regression analysis of children's height and tree growth data is neither reasonable nor useful. In addition, even if there is a regression relationship between two variables, it is not necessarily a causal relationship, and reasonable explanations and conclusions must be made based on professional knowledge.

Extended information: 1. Regression analysis should be of practical significance, and regression analysis should not be carried out arbitrarily on two unrelated phenomena, ignoring the internal relationship and laws between phenomena; For example, regression analysis of children's height and tree growth data is neither reasonable nor useful. In addition, even if there is a regression relationship between two variables, it is not necessarily a causal relationship, and reasonable explanations and conclusions must be made based on professional knowledge.

2. The data of linear regression analysis generally require that the variable y is a random variable from the normal population, and the independent variable x can be a normal random variable or a value that is accurately measured and tightly controlled. If it deviates slightly, it generally has little impact on the estimation of the parameters in the regression equation, but it may affect the estimation of the standard deviation and the authenticity of the p-value during hypothesis testing.

3. When performing regression analysis, you should first draw a scatter plot. If there is a linear trend, a linear regression analysis can be performed. If there is no obvious linear trend, the curvilinear modal should be selected according to the type of scatter distribution, and the data should be transformed into a linear regression solution. Generally speaking, it is meaningless to calculate the regression equation without satisfying the linear condition, and it is best to use the method of nonlinear regression equation for analysis.

4. After drawing the scatter plot, if there are some large and small outliers (outliers), they should be reviewed and checked in time, and the wrong data due to measurement, recording or computer entry should be corrected and eliminated. Otherwise, the existence of outliers will have a great impact on the estimation of coefficients a and b in the regression equation.

The reasons why correlation analysis is necessary before regression analysis are: if it is a practical problem, it is not necessary to do regression, and the regression coefficient itself reflects the correlation between variables, and it is more accurate than the ordinary Pearson correlation; However, if you are doing scientific research, the relevant analysis step cannot be omitted.

In big data analysis, regression analysis is a modeling technique that studies the relationship between the dependent variable (target) and the independent variable (**device). This technique is commonly used for analysis, time series modeling, and to discover causal relationships between variables. For example, the relationship between reckless driving by drivers and the number of road accidents is best studied by regression.

Generally speaking, regression analysis is to determine the causal relationship between variables by specifying the dependent variable and the independent variable, establish a regression model, and solve the parameters of the model according to the measured data, and then evaluate whether the regression model can fit the measured data well. If it fits well, it can be further based on the independent variables.

Regression analysis refers to the use of data statistics principle, a large number of statistical data mathematical processing, and determine the correlation between the dependent variable and some independent variables, establish a regression equation with good correlation, and extrapolate it for future changes in the dependent variable. The main problem solved by regression analysis is to determine whether there is a correlation between variables, and if so, to find out the mathematical expression; Controlling the value of one or more variables based on the value of another variable or variables, and estimating the degree of precision that can be achieved by such control or variables. In order to make the regression equation more realistic, firstly, we should qualitatively judge the possible types and number of independent variables as much as possible, and qualitatively judge the possible types of regression equations on the basis of observing the development law of things. Secondly, we should strive to grasp sufficient high-quality statistical data, and then use statistical methods to calculate or improve qualitative judgments from the quantitative aspect by using mathematical tools and related software.

1. "Regression analysis" refers to the analysis of the relationship between the dependent variable and the independent variable, and the basic idea of regression analysis is: Although there is no strict and deterministic functional relationship between the independent variable and the dependent variable, it is possible to find the mathematical expression that best represents the relationship between them.

2. Regression analysis has a wide range of applications, such as the general processing of experimental data, the acquisition of empirical formulas, factor analysis, product quality control, meteorological and forecasting, the formulation of mathematical models in automatic control, etc.

3. Regression analysis mainly deals with the statistical correlation of variables.

Gradually return to the method of splitting the bad sails.

It is to introduce variables into the model one by one, test each explanatory variable that has been introduced, and test the explanatory variables that have been selected one by one, and delete the original explanatory variables when they become no longer significant due to the introduction of subsequent explanatory variables. To ensure that only significance is included in the regression equation each time a new variable is introduced.

Methods of variables.

Stepwise regression analysis is multiple regression analysis.

One of the methods. Regression analysis is used to study the interdependence of multiple variables, while stepwise regression analysis is often used to build optimal or appropriate regression models.

This allows for a deeper study of dependencies between variables. At present, stepwise regression analysis is widely used in various disciplines, such as medicine, meteorology, humanities, economics, etc.

Extended Materials: Interpretation of Stepwise Regression Analysis Results The basic principle of a stepwise regression model is to introduce each explanatory variable into the model in turn for f-test.

At the same time, the t-test was performed on the introduced explanatory variables one by one. When a new explanatory variable is introduced and the correlation between the original explanatory variable and the explanatory variable is no longer significant, the non-significant explanatory variable is eliminated. By analogy, stepwise regression analysis ensures that only significant variables are included in the regression equation before each new explanatory variable is introduced, until no more significant explanatory variables are added to the regression equation and no sub-significant explanatory variables are eliminated.

In this case, the obtained regression equation is the combination of explanatory variables with the best significance, which not only completes the comparison of significance between explanatory variables, but also solves the multicollinearity.

Issue. The above models and data were regressed stepwisely.

Finance refers to economic activities such as the issuance, circulation and withdrawal of currency, the issuance and recovery of loans, the deposit and withdrawal of deposits, and the exchange of foreign exchange. The essence of finance is the circulation of value. There are many types of financial products, including banks, insurance, trusts, etc.

Finance involves a wide range of academic fields, including accounting, finance, and investment.

Banking, ** science, insurance, trust science, etc. The genuine goods of the financial futures sedan are a kind of ** transaction. A transaction is a transaction of a standardized contract by way of open auction between two parties in a centralized market.

The contract is the object or subject matter of the transaction, which is a standardized contract formulated by the exchange and stipulates a specific time and place for the delivery of a certain quantity and quality of goods. The underlying instruments of a financial** contract are various financial instruments.

or financial variables), such as forex, bonds, indices, etc. In other words, finance is a transaction in which a financial instrument (or financial variable) is the underlying instrument.

1) Determine the correlation and the factors that affect the relationship. To determine the correlation or dependence between phenomena and the various factors that affect the change of the relationship, a qualitative analysis should be carried out first. That is, when performing correlation and regression analysis, qualitative analysis and quantitative analysis must be organically combined.

2) Regression equations and correlation coefficients are used in combination. When performing correlation and regression analysis, try to use the regression equation in conjunction with the correlation coefficient or estimated standard error.

3) Pay attention to the scope of the correlation. When using the regression equation of the match to estimate or **, pay attention to the range of the correlation relationship, such as beyond this range The quantity gauge between the two variables may change, and then use the model to extrapolate, it is not very suitable, to re-establish the mathematical model, at least after the correction can be used.

4) Pay attention to the complexity of socio-economic phenomena. The factors influencing the relationship between socio-economic phenomena are diverse, including political, economic, natural, technological and even moral and psychological factors. There are many situations that cannot be estimated by regression and correlation analysis methods, which requires a variety of methods to be used for analysis.

This paper discusses the problems that should be paid attention to in regression analysis.

A:First, quantitative analysis on the basis of qualitative analysis is necessary to ensure the correct use of regression analysis. In other words, before determining which variable is the independent variable and which variable is the dependent variable, it is necessary to have a sufficient and correct understanding of the problem under study.

Second, in the return and return equations, the absolute value of the regression coefficient can only indicate the degree of connection between the independent variable and the dependent variable, and the proportion of change between the two variables. Because the size of its value directly depends on the size of the computational unit used by the variable.

Third, in order to make the estimation and ** more accurate, the correlation coefficient, the regression equation and the estimated standard error should be used in combination with slide talk.

Fourth, it is necessary to analyze specific problems in detail. The regression equation is calculated based on the data, which is an empirical number of information, if the conditions change, the estimation or ** will not be accurate. Therefore, it should not be copied mechanically, so as not to cause mistakes.

Answer]: cThis question examines regression analysis. When performing regression analysis, the dependent and independent variables need to be determined first.

In regression analysis, the variable that is ** or explained is called the dependent variable, which is generally represented by y; The variable that explains the dependent variable is called an independent variable, and this is generally denoted by x.