1. There is no answer to this question, because
blockchain software is only a large field, which needs specific analysis. Asking this question is like asking how much to buy a house
blockchain software is divided into several categories
for example, exchanges do the most, from tens of thousands to hundreds of thousands with complete functions
and blockchain games, depending on the game settings, generally from hundreds of thousands to hundreds of thousands
the rest depends on the category
2. If bitcoin represents blockchain version 1.0, it proves to the world that digital currency can be realized. Then Ethereum represents blockchain version 2.0, which lets you know more about the play of blockchain, decentralized applications and smart contracts. In 2018, blockchain will enter the stage of 3.0, application and scenario landing will be the core of blockchain, and blockchain based applications in various instries will spring up, which will be the early dividend period of the last stage of blockchain development. The demand for talents driven by the application of blockchain technology has become increasingly large. Blockchain technical talents have become the new professional talents, which are highly competitive, and also have enviable high salaries
whether to form a meaningful large-scale consensus: one of the outstanding advantages of blockchain is that it can effectively form a large-scale consensus by opening account books and notifying the whole network. At present, there is no need to form a network wide consensus for many projects. Some projects are just for crowdfunding and token issuance. The blockchain consensus of projects is meaningless. Does this model have network effect: network effect means that a project will become more and more valuable with more and more users of the project, because the value of the project is not in the users themselves, but in the connection network between users. Bitcoin is a typical example. The more user nodes there are, the more links between nodes will grow exponentially, and the whole ecosystem of bitcoin will be more valuable
of course, in addition to these points, there are many basic principles to judge the project, such as effective incentive mechanism, strong team, project solving pain points, and so on
3. Blockchain apps include: Netease star base, digital chain app, chain to finance, time forest blockchain trading platform, blockchain e-wallet, ostrich blockchain, GXS wallet, coin bag wallet and ordered wallet
fifthly, Youling wallet
Youling app creates a personal centered value exchange network and ecology, and creates a decentralized national interactive entertainment and blockchain new economic platform
Youling app adopts the decentralized open mode, allowing indivials (third-party developers) to develop all kinds of applications based on Youling open platform, and each user can freely choose the application to build his own home page, including but not limited to: souvenirs, live broadcast, sharing, community, service sale, commodity sale
4. Consensus, circulation and creation happen to be the core values of Wen ecology.
5. The content of blockchain is comprehensive.
6. Can see clouded leopard network company, the team is experienced
7. Regulatory variables can be qualitative or quantitative. In the analysis of regulatory effect, the independent variable and regulatory variable should be transformed centrally. Brief model: y = ax + BM + CXM + E. The relationship between Y and X is characterized by regression coefficient a + cm, which is a linear function of M, and C measures the size of the moderating effect. If C is significant, it means that the regulatory effect of M is significant. 2. Analysis method of regulatory effect analysis method of significant variable: divided into four cases. When the independent variable is a category variable and the moderating variable is also a category variable, the analysis of variance of two factor interaction effect is used, and the interaction effect is the moderating effect; When the regulatory variable is a continuous variable, the independent variable uses the pseudo variable, centralizes the independent variable and the regulatory variable, and does the hierarchical regression analysis of y = ax + BM + CXM + e: 1. Do the regression of y to X and m, and get the determination coefficient R1 2. 2. The regression of y to x, m and XM yielded R2 2. If R2 2 was significantly higher than R1 2, the regulatory effect was significant. Or, XM regression coefficient test, if significant, the regulatory effect is significant; When the independent variable is a continuous variable, the regulating variable is a category variable, grouping regression: grouping according to the value of M, doing y to x regression. If the difference of regression coefficient is significant, the regulation effect is significant. When the regulation variable is a continuous variable, the hierarchical regression analysis of y = ax + BM + CXM + e is done as above. There are two ways to analyze the moderating effect of latent variables: one is that the moderating variable is the category variable and the independent variable is the latent variable; The second is that both regulatory variables and independent variables are latent variables. When the moderator is a class variable, group structural equation analysis is performed. The method is to limit the regression coefficients of the two groups of structural equations to be equal, and get one χ 2 and the corresponding degrees of freedom. Then remove this restriction, re estimate the model, and get another one χ 2 and the corresponding degrees of freedom. ahead χ 2 minus the following χ 2 get a new one χ 2, the degree of freedom is the difference between the two models. If χ If the test result is statistically significant, the moderating effect is significant; When regulatory variables and independent variables are latent variables, there are many different analysis methods. The most convenient one is the unconstrained model proposed by marsh, Wen and Hau. 3. The definition of intermediary variable is the influence of independent variable x on dependent variable y. if x influences y by influencing variable m, then M is called intermediary variable. Y=cX+e1, M=aX+ e2 , Y= c′X+bM+e3 Where C is the total effect of X on y, AB is the mediating effect through M, and C 'is the direct effect. When there is only one mediating variable, there is C = C ′ + AB between the effects, and the mediating effect is measured by C-C ′ = ab. 4. Mediating effect analysis method mediating effect is indirect effect, regardless of whether the variables involve latent variables, structural equation model can be used to analyze mediating effect. The first step is to test system C. if C is not significant and the correlation between Y and X is not significant, stop the mediating effect analysis, and if it is significant, proceed to the second step; 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. The statistic of Sobel test is Z = ^ A ^ B / SAB, in which ^ A and ^ B are estimates of a and B respectively, SAB = ^ a2sb2 + b2sa2, SA and Sb are standard errors of ^ A and ^ B respectively. 5. 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, Third, regression analysis To choose linear regression in regression, we should first centralize the independent variable and m, that is, subtract their respective mean. 1. Now, we 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 x independent variable (intermediary variable so far). 3 In order to do the adjustment variable analysis, it is necessary to input the opportunity of X and m in the next for further regression. The test mainly depends on whether f is significant
8. Mandela effect (English Name: the Mandela effect) is a psychological effect, which refers to the public's collective memory of history is inconsistent with historical facts
in addition, some Mandela effects sometimes involve another theory: the matrix failure effect (gitm)
in 2013, Mandela effect was confirmed and explained by experts in the same year
CERN, a research laboratory composed of the world's top scientists, carried out experiments on the Large Hadron Collider and quantum computer in 2009, and "Mandela effect" came into being
many people say that in their memory, South African President Mandela "should have died in prison in the 1980s", but the reality is that Mandela did not die in the 1980s, was later released and became president of South Africa, and was still alive until 2010 (Mandela died in 2013)
however, as early as 2010, someone suggested that Mandela had died in prison in the 1980s. The proponents were able to present the reports they had seen, the TV footage of the funeral, and even the tearful speech of Mandela's widow. When this statement was put forward, a large number of netizens responded that they had the same memory
since then, similar events have occurred all over the world, peaking in 2015 and 2018, and the aftershocks continue
since the news of Mandela's death was released in 2013, people all over the world have found that their memories of Mandela are confused, with different memories from the time of death to the cause of death
this phenomenon was well-known before because of "wrong impression of things". In fact, Mandela effect is just a new name. However, there are also many cases where the reality is not consistent with people's collective memory, it will be labeled as "Mandela effect", and we don't know whether it is true or can be classified into the category of Mandela effect.
