Decentralized adjustment model

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. Please help me. I'm sure I'll give you the most. He had a car accident. Could it be the cause of hyperopia? How to correct it Hello! First of all, I wish your nephew good health! Let me tell you something about

3. 1. The dependent variable does not need to be centralized; 2. The first step is that the independent variable enters the regression equation; The second step is that independent variables and regulatory variables enter together; The third step is that the independent variable, regulatory variable and interaction item enter together;

4. 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.

5. 1. If x is a real 0 and 1 variable, such as gender, treat it as continuous. 4 m # @ + s # N8] 4 E 2. If x is an artificial 0 and 1 variable, such as above average vs. below average, then there is a problem. Because the artificial dichotomy can use any artificial standard. Different methods will seriously affect the results.

6. The mediating and moderating effects can be realized by hierarchical regression in SPSS, that is, in which dialog box of multiple linear regression analysis, there is a
Block dialog box, you can move the independent variables and moderating variables to which dialog box one by one, and the regression results will show the changes of moderating effects

7. 1. The dependent variable does not need to be centralized
2. The first step is for the independent variables to enter the regression equation; The second step is that independent variables and regulatory variables enter together; The third step is that the independent variable, regulatory variable and interaction item enter together
3. Divide the regulatory variables into high and low groups, do the regression analysis of the independent variables and the dependent variables, and then compare the influence coefficient of the independent variables of the high and low groups on the dependent variables, and carry out the slope test.

8. First of all, let's answer your question:
1. The nonstandardized coefficient is the slope of the regression equation, which means that each independent variable changes by one unit, and how many units the dependent variable changes correspondingly. The coefficient is related to the unit of the independent variable, and is generally not used to measure the influence of the independent variable< In general, the greater the absolute value of the standardization coefficient, the greater the influence of the independent variable on the dependent variable< Secondly, I will give you some analysis and suggestions (the premise of 2-4 items is that the sample size is large enough):
1. The sample is too small, there are only five groups of data, and the results are often unreliable. It is strongly recommended to increase the sample size, otherwise the statistical analysis may be meaningless, or even cause errors
2. From the results of independent variable t-test, the sig values corresponding to the content of tourmaline and the density of tourmaline neck exceeded 0.05, which means that at the significance level of 0.05, the two independent variables are not significantly correlated with the dependent variable. Generally speaking, under the premise of the existence of the average pore diameter of the independent variable, the sig values of tourmaline content and tourmaline neck density are higher than 0.05, These two variables can be basically excluded from the equation

3. From the perspective of partial correlation, there is a strong correlation (or collinearity) among the three independent variables, because the strongly correlated independent variables often lead to unreasonable statistical analysis results, so they can not be put into the equation together in theory
4. It is suggested that you use multiple stepwise regression when doing multiple linear regression analysis. In this way, you can automatically exclude strongly related variables according to the influence of independent variables, and you can also automatically exclude independent variables that have no significant influence on dependent variables, so as to get more reliable analysis results.