How does Stata decentralize cross multiplicative regression

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. 2006-08-07 11:22 strategically using general purpose Statistics Packages: a look at Stata, SAS and SPSS. It can be thought that each software has its own unique style, has its own advantages and disadvantages. This paper makes an overview of this, but it is not a comprehensive comparison. People often have a special preference for the statistical software they use. I hope most people can agree that this is a real and fair comparative analysis of these software. SAS general usage. SAS is very popular with advanced users because of its powerful function and programmability. Based on this, it is one of the most difficult software to master. When using SAS, you need to write SAS program to process data and analyze. If an error occurs in a program, it will be difficult to find and correct it. Data management. SAS is very powerful in data management. It allows you to process your data in any possible way. It contains SQL (Structured Query Language) process, which can be used in SAS dataset. But it takes a long time to learn and master the data management of SAS software. In Stata or SPSS, the commands used to complete many complex data management tasks are much simpler. However, SAS can process multiple data files at the same time, making this work easier. It can handle 32768 variables and the maximum number of records allowed by your hard disk space. Statistical analysis. SAS can do most statistical analysis (regression analysis, logistic regression, survival analysis, ANOVA, factor analysis, multivariate analysis). The advantages of SAS may lie in its ANOVA, mixed model analysis and multivariate analysis, while its disadvantages are mainly ordered and multivariate logistic regression (because these commands are difficult), and robust methods (it is difficult to complete robust regression and other robust methods). Although it supports the analysis of survey data, the comparison with Stata is still quite limited. Drawing function. Among all the statistical software, SAS has the most powerful drawing tool, which is provided by SAS / graph mole. However, the learning of SAS / graph mole is also very professional and complex, and the proction of graphics mainly uses programming language. SAS 8 can draw interactively by clicking the mouse, but it is not as simple as SPSS. Summary. SAS is suitable for advanced users. Its learning process is hard, and the initial stage can be frustrating. However, it is still a powerful data management and processing a large number of data files at the same time, which is favored by advanced users. Stata is generally used. Stata is popular among beginners and advanced users for its simplicity and powerful functions. When using, you can only input one command at a time (suitable for beginners), or you can input multiple commands at a time through a Stata program (suitable for advanced users). In this way, even if errors occur, it is easier to find out and correct them. Data management. Although Stata's data management ability is not as powerful as SAS, it still has many powerful and simple data management commands, which can make complex operations easier. Stata is mainly used to operate one data file at a time, so it is difficult to process multiple files at the same time. With the introction of Stata / SE, the number of variables in a Stata data file can reach 32768, but when a data file exceeds the range allowed by computer memory, you may not be able to analyze it. Statistical analysis. Stata can also perform most statistical analysis (regression analysis, logistic regression, survival analysis, ANOVA, factor analysis, and some multivariate analysis). Stata's greatest advantages may lie in regression analysis (it contains easy-to-use regression analysis feature tools) and logistic regression (it has additional proceres to explain the results of logistic regression and is easy to be used for ordinal and multivariate logistic regression). Stata also has a series of good robust methods, including robust regression, robust standard error regression, and other commands including robust standard error estimation. In addition, Stata has obvious advantages in the field of survey data analysis, which can provide regression analysis, logistic regression, Poisson regression, probability regression and other survey data analysis. Its disadvantages lie in the analysis of variance and traditional multivariate methods (multivariate analysis of variance, discriminant analysis, etc.). Drawing function. Just like SPSS, Stata can provide some commands or mouse click interface for drawing. Unlike SPSS, it has no graphical editor. Among the three kinds of software, its syntax of drawing command is the simplest, but its function is the most powerful. The quality of graphics is also very good, which can meet the requirements of publishing. In addition, these figures play a very good role in supplementing statistical analysis. For example, many commands can simplify the making of scatter diagram in the process of regression discrimination. Summary. Stata realizes the combination of easy to use and powerful function. Although it is easy to learn, it is very powerful in data management and many frontier statistical methods. Users can easily download other people's existing programs, or write their own, and make it closely combined with Stata. General usage of SPSS. SPSS is very easy to use, so it is most accepted by beginners. It has a clickable interactive interface, and can use the drop-down menu to select the command to be executed. It also has a way to learn its "syntactic" language by ing and pasting, but these syntax are usually very complex and not very intuitive. Data management. SPSS has a friendly data editor similar to excel, which can be used to input and define data (missing values, numeric labels, etc.). It is not a powerful data management tool (although some commands to enlarge data files have been added in SPS 11, its effect is limited). SPSS is also mainly used to operate on one file, which is not competent for processing multiple files at the same time. Its data file has 4096 variables, and the number of records is limited by your disk space. Statistical analysis. SPSS can also do most statistical analysis (regression analysis, logistic regression, survival analysis, ANOVA, factor analysis, multivariate analysis). Its advantages lie in ANOVA (SPSS can complete the test of many special effects) and multivariate analysis (multivariate ANOVA, factor analysis, discriminant analysis, etc.), and the mixed model analysis function is added in SPSS 11.5. Its disadvantages are that there is no robust method (unable to complete robust regression or get robust standard error), and lack of survey data analysis (spss12 version added a mole to complete part of the process). Drawing function. The interactive interface of SPSS drawing is very simple. Once you draw a graph, you can modify it by clicking as needed. The graphics are of excellent quality and can be pasted into other files (word documents or PowerPoint, etc.). SPSS also has programming statements for drawing, but it can't proce some effects of interactive interface drawing. This statement is more difficult than Stata statement, but simpler than SAS statement. Summary. SPSS is committed to simplicity (its slogan is "true statistics, true simplicity") and has been successful. But if you're an advanced user, you'll lose interest in it over time. SPSS is a strong hand in cartography. Due to the lack of robust and survey methods, it is weak to deal with the frontier statistical process. Overall evaluation each software has its own unique, but also inevitably has its weaknesses. In general, SAS, Stata and SPSS are a set of tools that can be used in a variety of statistical analysis. Through stat / transfer, different data files can be converted in seconds or minutes. Therefore, you can choose different software according to the nature of the problem you are dealing with. For example, if you want to analyze with a hybrid model, you can choose SAS; Stata was selected for logistic regression; If we want to do ANOVA, the best choice is SPSS. If you are often engaged in statistical analysis, it is strongly recommended that you collect the above software into your toolkit for data processing.

3. Of course, it's the one who wants to know the meaning of convenience. If not, I have to open the University-

4. Is it invincible? The pipe on banye's back is directly inserted into the magic statue. Why doesn't he live forever? I don't think that a bad man-made in the magic statue can win four generations with the dirt. In the final analysis, it's the credit of Shenwei. But even the kaleidoscope needs to be practiced, just like Kakashi didn't hit Didala for the first time. If he only got his hand, he couldn't be completely familiar with the dirt and won't be blind, So in the end, it's better to teach the local people their own experience and skills

6. If there is in the cartoon and there is no

in the animation, the animation will not come out

7. Yes, there are Hisense R & D centers