Mediating decentralized data with regulation

1. According to Hou Jietai: the so-called centralization refers to subtracting the mean value of a variable from its expected value. For sample data, each observation value of a variable is subtracted from the sample average value of the variable, and the transformed variable is centralized
for your question, subtract the mean from each measurement.

2.

De flow means that all social resources can be aggregated and distributed with one click

in a system with many nodes, each node has a high degree of autonomy. Nodes can connect with each other freely to form a new connection unit. Any node may become the stage center, but it does not have the mandatory central control function. The influence between nodes will form a nonlinear causal relationship through the network

This kind of open, flat and equal system phenomenon or structure is called decentralization

extended materials:

compared with the previous Internet (WEB 1.0) era, today's Internet (Web 2.0) content is no longer proced by professional websites or specific groups, but by the participation of the whole Internet users and the creation of equal power levels. Anyone can express their views on the Internet or create original content to proce information together

with the diversification of network service shape, the decentralized network model becomes more and more clear and possible. After the rise of Web2.0, the services provided by Wikipedia, Flickr, blogger and other network service providers are decentralized. Any participant can submit content, and Internet users can create or contribute content together

3. Decentralization is a phenomenon or structure, which can only appear in a system with many users or nodes, and each user can connect and influence other nodes. Generally speaking, everyone is the center, and everyone can connect and influence other nodes. This flat, open-source and equal phenomenon or structure is called "decentralization"< Br > at the same time, "decentralization" is one of the typical features of blockchain, which uses distributed storage and computing power. The rights and obligations of the whole network nodes are the same, and the data in the system is jointly maintained by the whole network nodes, so that the blockchain no longer relies on the central processing node to realize the distributed storage, recording and updating of data. Each blockchain follows a unified rule, which is based on a cryptographic algorithm rather than a credit certificate, and the data update process needs to be approved by the user, so that the blockchain does not need the endorsement of intermediaries and trust institutions< The characteristics of br> de centralization:
centralization is first reflected in diversification. In the Internet world, there are no more than several portals has the final say. Various websites have begun to voice their own voice, express different choices and different hobbies, and these websites are distributed in every corner of the Internet world. Br > decentralisation is followed by the centralization of people, and decentralisation of content has become a trend, and people have become the key force to determine the survival of websites. It's a great change to build a website with indivials who lack interaction to gather talents and contribute their wisdom in the form of a circle. That is user-oriented, humanized< < br > decentralized content: < br > decentralization is the form of social relationship and content generation formed in the development of the Internet, which is a new network content proction process relative to "centralization"< Br > compared with the early Internet (Web1.0) era, today's Web (Web2.0) content is no longer proced by professional websites or specific groups of people, but is created by all Internet users with equal rights. Anyone can express their views or create original content on the Internet to proce information together< Br > with the diversification of network service forms, the decentralized network model is becoming clearer and more possible. After the rise of Web2.0, the services provided by Wikipedia, Flickr, blogger and other network service providers are decentralized. Any participant can submit content, and Internet users can create or contribute content together< After that, with the emergence of more simple and easy-to-use decentralized network services, the characteristics of Web2.0 became more and more obvious. For example, the birth of services more suitable for ordinary Internet users, such as twitter and Facebook, made it easier and more diversified to proce or contribute content to the Internet, thus enhancing the enthusiasm of Internet users to participate in the contribution and recing the threshold of procing content. Eventually, every netizen becomes a tiny and independent information provider, making the Internet more flat and content proction more diversified.

4. What's gongxinbao? I don't know once about decentralized data trading. Go ahead. Good luck.

5. bitcoin, Leyte coin, dogcoin, Diandian coin, American card coin, bitstock, dark coin, black coin, Ruibo coin, Malaysian coin, Mediterranean coin, Yuanbao coin, Zhaocai coin, sand coin, network gold

6.

On June 14, the first native application of blockchain - "Du universe" app was launched online. This is another important action of network in the field of blockchain

it is reported that Du universe is currently recruiting content procers. The earlier you join, the greater the profit. In the future, "Du universe" will also open the third-party developer platform, introce more applications and play methods, complete value transfer and interaction through their own passes, further improve the ecological construction, and better serve users

since 2018, the network has been acting frequently in the field of blockchain. In April, it released the totem of blockchain original image service platform, which adopts the blockchain right registration network independently developed by the network to provide one-stop services for image rights confirmation, monitoring and protection. Then in May, the blockchain network operating system super chain was released. The operating system is compatible with the developer ecology of bitcoin and Ethereum. It can not only plug in and pull out the consensus mechanism to solve the current energy consumption problem, but also support 100000 concurrent transactions in a single chain

7. 1. The mediating effect analysis does not need data centralization and standardization
2. Forced centralization or centralization, only the non standardization coefficient is different, the standardization system is the same

(provided by Nanxin)

8. A theory in the development of organic molecular structure theory. Mediation effect refers to that the influence of X on y is realized by m, that is to say, M is a function of X and Y is a function of M (y-m-x). Considering the influence of independent variable x on dependent variable y, if x influences variable y through M, then M is called intermediary variable. For example, the research on the attribution of the boss: the performance of the subordinates - the attribution of the boss to the performance of the subordinates - the response of the boss to the performance of the subordinates, in which "the attribution of the boss to the performance of the subordinates" is the intermediary variable. Suppose that the variables have been centralized or standardized, where C is the total effect of X on y, AB is the mediating effect through the mediating variable m, and C 'is the direct effect. When there is only one mediating variable, the relationship between the effects is as follows: C = C '+ AB, the mediating effect is C-C & # x27= It's not measured by A.B.

9. The relationship between Y and X is described by regression coefficient a + cm, which is a linear function of M, and C measures the moderating effect, When the independent variable is a category variable and the regulatory variable is also a category variable, the analysis of variance of two factor interaction effect is used, and the interaction effect is the regulatory effect; When the regulatory variable is continuous, the independent variable uses pseudo variable, centralizes the independent variable and regulatory variable, and does the hierarchical regression analysis of y = ax + BM + CXM + e: 1. Do the regression of y to X and m to get the measurement coefficient r 12.2, do the regression of y to x, m and XM to get r 22, if R 22 is significantly higher than R 12, then the regulatory effect is significant; When the independent variable is a continuous variable, the regulating variable is a category variable. Grouping regression: group according to the value of M, and do the regression of y to X. if the difference of regression coefficient is significant, then the regulating effect is significant. When the regulating variable is a continuous variable, do the hierarchical regression analysis of y = ax + BM + CXM + E, Independent variable is latent variable; When the moderator is a category variable, the group structural equation analysis is done. The method is to limit the regression coefficients of the two groups of structural equations to be equal, and get one χ Then we remove this restriction, re estimate the model and get another one χ 2 and the corresponding degrees of freedom χ 2 minus the following χ 2 get a new one χ 2, the degree of freedom is the difference between the degrees of freedom of the two models χ If the test result is statistically significant, the moderating effect is significant; There are many different analysis methods when both the regulatory variable and the independent variable are latent variables. The most convenient one is the unconstrained model proposed by marsh, Wen and Hau. 3. The definition of the intermediate variable is the influence of the independent variable x on the dependent variable y. if x affects y by influencing the variable m, then M is called the intermediate variable. Y = CX + E1, M = ax + E2, y = C ′ x + BM + E3, When there is only one mediating variable, there is C = C ′ + AB between the effects, and the size of the mediating effect is measured by C-C ′ = ab. 4. Mediating effect analysis method mediating effect is indirect effect, no matter whether the variables involve latent variables or not, we can use structural equation model to analyze the mediating effect, If C is not significant and y has no significant correlation with X, the mediating effect analysis will be stopped. If C is significant, the second step will be carried out; The second step is to test a and B once. If they are all significant, then test C ′, C ′, and the mediating effect is significant. If C ′ is not significant, then the complete mediating effect is significant; If at least one of a and B is not significant, do Sobel test, significant mediating effect is significant, not significant mediating effect is not significant, ^5. The comparison between moderator and mediator moderator m moderator m research purpose when does x affect y or when does x have a greater impact? How does x affect the moderating effect, interaction effect, mediating effect of y-related concepts When the influence of X on y is considered, the influence of strong x on y is strong and stable. The typical model is y = am + BM + CXM + e, M = ax + E2, y = C ′ x + BM + E3. The position of m in the model is x, M is in front of Y, M can be in front of X, the function of m after X and before y affects the direction (positive or negative) and strength of the relationship between Y and X. x influences the relationship between Y, m and X, X and X through it The correlation between M and X, y can be significant or not (the latter is ideal) the correlation between M and X, y is significant effect regression coefficient C regression coefficient proct AB effect estimate ^ C ^ A ^ B effect test whether C is equal to zero, AB is equal to zero test strategy do hierarchical regression analysis, test the significance of partial regression coefficient C (t test); 6. SPSS operation method of mediating effect and moderating effect. First, descriptive statistics, including M SD and internal consistency reliability (a) are used. Second, all variables are correlated, including statistical variables and hypothetical x, y, y, The independent variable and m should be centralized first, that is, subtracting their respective mean 1. Now, input m (regulatory variable or intermediary variable), y dependent variable, and demographic variable related to any of the independent variable, dependent variable, and M regulatory variable into independent 2 Then press next to input the independent variable of X (the intermediate variable ends here). 3. To do the analysis of the adjustment variable, we should also input the opportunity of X and m in next for further regression. The test mainly depends on whether f is significant