How to deal with the decentralization of interaction items

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. The so-called Internet platform is to develop and utilize the information resources of the Internet. I personally understand that the instry should be concentrated together and one key solution should be taken to save the middle cumbersome steps. The 16 year Internet plus summit, which is the latest Internet plus summit held in Beijing in 16, will be held in the 16 month of. This will further promote the development of Internet +. To promote the integration of "Internet plus" with government, people's livelihood and all walks of life, we can learn about this area.

3.

Center

1. Click "start" in the menu bar above word

2. Select the words you want to put in the middle

3. Click the center alignment in "paragraph"

4. First, the goal of refined operation. For example, if your proct is just a tool, I'm afraid it can't be said that there are too many refined operations. Generally, it's enough to do a good job in routine user behavior analysis, and then cooperate with user qualitative research to guide proct design; If it is a content-based proct, or a proct with both function and content, it really needs to be considered. 2. Design statistical framework suppose that users will frequently interact and use functions on your app, and browse or generate content at the same time, so you need to design your statistical framework well while designing procts. 2、 Data collection first lists the data items you need, then evaluates which parts need to be reported by app and which parts can be counted in the background, and then adds them on the front and back platforms. Generally speaking, the collected data reported by app must be carefully checked and tested before release, because once the version is released and the data collection goes wrong, not only the previous efforts are wasted, but also a lot of dirty data will be brought. At the same time, the running efficiency of the client may be reced, and the gain is not worth the loss. 2. After data collation and data collection, all kinds of original data need to be processed into intuitive and visible data needed by proct managers. Here, we need to do some basic data logical association and display, so we won't repeat it. 3. Data analysis according to the statistical framework designed at the beginning, you can clearly see the data you need. Of course, the above is just a more basic analysis. If you get these data, you can analyze that users who use a function also like B function. The two are closely related. Can you consider more integration or interface adjustment in front-end design; For example, by analyzing the click stream, what are the paths for most users to visit or use the app, and do they hide the core functions too deeply? For another example, we can analyze different user attributes, such as male users and female users. Do they have significant differences in user behavior? wait. There is a big gap between the data analysis methods and models of different procts, which cannot be explained at once. So the above are more examples. 3、 Some principles need to be noted: 1. The data itself is objective, but the data interpreted must be subjective. The same data analyzed by different people is likely to draw completely opposite conclusions, so we must not analyze it with opinions in advance (for example, if we have hypotheses, we can use the data to demonstrate them); 2. Data collection by app must be of low priority. It can't affect proct performance and user experience because of data collection, and it can't collect user's privacy data (although many domestic apps don't do this); 3. Data is not omnipotent. You should trust your own judgment.

5. Not necessarily, centralization is only for the convenience of explanation, and does not affect the regression coefficients Central treatment of regulatory effect of South Heart Network

6. After centralizing the independent variable and the adjusting variable, a new variable (that is, the proct of interaction terms) is obtained by multiplying the centralized values, and then put into regression
there are several methods of centralization. Here are the two most commonly used, one is to subtract the average value, and the other is Z-score
subtract the average value: first perform a description statistics to get the descriptive statistical results, with the average and standard deviation. Then use the compute command to create a new variable = original variable average
the Z-score is similar to the above result, except that the new variable is divided by the standard deviation to get a score.