Which software can be used for multiple choice models

The mathematical model (with relevant assumptions) used for regression analysis becomes a regression model, and a regression model with only one regression variable is called a univariate regression model, otherwise it is called a multiple regression model.

Enter is to enter all, that is, all the factors selected enter the model, which is only used to understand the interpretation of the corresponding variables of the independent variables.

stepwise, also known as stepwise regression, uses the least number of variables to achieve the maximum explanatory power of the corresponding variables, that is, some variables that are not statistically significant are excluded step by step, so you have only one factor here into the final model, which is also the regression model required in general. You can write y=value)*x.

It's just that your model is indeed a little less, only one factor enters the final model, and then you use backword and forword to see the results, whether there is a difference, if not, you can only use this.

It doesn't make sense to enter that forced entry to solve a real problem or **.

Hehe, we provide detailed answers.

There are many ways to choose a variable.

The most old-fashioned is f-statistics, which should be the ** of the p-value of the answer.

This is followed by a series of penalize variables, including adjusted r2 and mallow'In principle, variables can be selected by exhausting all 2 P combinations, and in practice, the Forward Backward method is usually used. If the data is multivariate, the amount of computation is still very large. The above indicators should also be replaced by MSE for Cross Validation.

The above method can be regarded as some form of L-0 regular, of course, you can also use L-1 regular, that is, lasso, this calculation is relatively small, so it is more popular.

That's probably all I know.

Select the variables that have an impact on the explanatory variables.

For example, to build a consumption function model, the relevant explanatory variables can include: income, price index, savings index, etc.

Of course, you can't find all the explanatory variables, as long as the regression coefficient of the regression model is significant.

There is no best answer to the regression equation, only a relatively better one.

First of all, not all data need to be stationarity tested, only time series data need to be secondly, which has nothing to do with the correlation coefficient Again, an independent variable with multiple independent variables can be cointegrated analysis is regression, but with the addition of a stationarity test, the rest is no different from general regression.

SPSS generally recommends choosing the last model.

This is the basic common sense that is gradually returned.

I often help others with this kind of data analysis.

Generally speaking, it is the last one, of course, the stepwise regression of SPSS has errors, and it is better to analyze the whole subset.

There are many kinds of regression, regression studies the influence of x on y, as for the choice of regression method, the key lies in the data type of the dependent variable y, if y is discrete data, then logistic regression should be used.

If the dependent variable Y is a continuous data (and often when Y is normally distributed), a linear regression (sometimes called OLS least squares regression) should be used.

There is also a more special and less used regression called Poisson regression, which should be used if Y is in line with the Poisson distribution.

Regression analysis is essentially the study of the effect of one or more independent variables x on a dependent variable y (quantitative data).

When there is one independent variable, it is univariate linear regression, also known as simple linear regression. When there are two or more independent variables, it is called multiple linear regression.

In SPSSAU, the analysis is carried out using the [Linear Regression] in the [General Method].