What Are Dependent Variables?

A dependent term in a dependent variable function, also called a function value. In the functional relationship, some specific numbers will change with the change of another (or several) numbers, which is called the dependent variable. For example: Y = f (X). This formula is expressed as: Y changes with X. Y is the dependent variable and X is the independent variable.

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How to understand what the dependent and independent variables are is actually simple. To put it plainly, the independent variable is "cause" and the dependent variable is "outcome". For example, the market usually sells 10 yuan per pound of pork, which has increased by 2 yuan because of heavy rain these days. Set the money I buy for pork to Y, the general price of pork is 10, if the price increases X yuan. This can be written as:

. It means how much (X) the price increases, which affects how much (Y) I have when buying pork. Here, X is the independent variable and Y is the dependent variable. ^[2]

Limited dependent variable means that the observations of the dependent variable are continuous, but subject to some limitation, the obtained observations do not fully reflect the actual state of the dependent variable. For example, in an epidemiological survey, we Take an indicator that can represent human health as the dependent variable to study various factors that affect human health.The level of this indicator is now measured, but due to the detection limit of the instrument, it is above or below a certain level We can't observe the value of, we usually use this limit level value to replace those we can't observe in practical applications. ^[3]

OLS research

The ordinary least squares method is improved, and the least squares method based on the mean of the dependent variable is proposed. The example proves that the improved model better meets the assumptions of regression analysis, reduces the estimation error of the univariate linear regression model, and improves The estimation accuracy and goodness of fit of the model improve the quality of statistical inference. ^[4]

Constrained Estimation for Linear Regression Models

This article mainly studies the constraint estimation of the linear regression model in the absence of dependent variables, based on the full data method and the single-point interpolation method. We give two constraint estimates of model coefficients and study the asymptotic normality of the estimator. Finally, we validate the effectiveness of the proposed method through numerical simulation. ^[5]