An independent variable has only two levels of comparison, and ANOVA can not be used

In a one-way bivariate ANOVA, the degrees of freedom of the sum of squares are n-1.

The degrees of freedom of the sum of squares of reversion are 1, and the degrees of freedom of the sum of squares of the residuals are the degrees of freedom of the sum of the total squares minus the degrees of freedom of the sum of squares.

Linear regression models often fit with least squares approximations, but they may also fit in other ways, such as minimizing "fitting defects" in some other specification (such as least absolute error regression) or minimizing the penalty for least squares loss functions in bridge regression.

Regression coefficients. In general, this value is required to be greater than 5%. For most behavioral researchers, the most important thing is the regression coefficient.

With an increase of one unit in age, the quality of the document decreases by one unit, indicating that older people rate the quality of the document lower. The corresponding t-value of this variable is , the absolute value is greater than 2, and the p-value is also <, so it is significant.

The conclusion is that older people rate the quality of documents lower, and this effect is significant. Conversely, people with more domain knowledge will have a higher assessment of the quality of the document, but this effect is not significant.

Summary. In a one-way ANOVA, the dependent variable is usually a continuous numerical variable, while the independent variable is a categorical variable. This is because the main purpose of one-way ANOVA is to compare the difference in means between two or more groups to determine if they are significantly different.

In some cases, a categorical variable can also be used as a dependent variable for one-way ANOVA, such as when a researcher wants to compare the sales of different types of products in the market, sales can be used as the dependent variable, and product type can be used as a categorical independent variable. However, in this case, the results of the analysis are usually f-values and p-values, rather than means and standard deviations. In summary, in one-way ANOVA, the dependent variable is usually a continuous numerical variable, while the independent variable is a categorical variable, which is the most commonly used method of this analysis.

Can the dependent variable of one-way ANOVA be a categorical variable and the independent variable be a numerical variable?

In a one-way ANOVA, the dependent variable is usually a continuous numerical variable, while the independent variable is a categorical variable. This is because the primary purpose of one-way ANOVA is to compare the mean differences between two or more groups of field surveys to determine if they are significantly different. In some cases, a one-way ANOVA can also be performed with a categorical variable as the dependent variable, such as when a researcher wants to compare the sales of different types of stupid products in the market, sales can be used as the dependent variable, and product type can be used as a categorical independent variable.

However, in this case, the results of the analysis are usually f-values and p-values, rather than means and standard deviations. In summary, in one-way ANOVA, the dependent variable is usually a continuous numerical variable, while the independent variable is a categorical variable, which is the most commonly used method of this analysis.

In other words, if I just want to see what the result of the p-value is, I can also use the categorical variable as the dependent variable and the numerical variable as the independent variable.

In a one-way ANOVA, the dependent variable must be numeric and the independent variable must be categorical. This is because one-way ANOVA aims to compare whether there is a significant difference in the mean of a dependent variable between different categories (also known as levels), so the dependent variable must be of the numeric type for the mean comparison to be enviable. The independent variable has to be categorical, because you're looking to compare the difference between the dependent variables in different categories, and the categorical variables are the variables that define those categories.

Therefore, it is incorrect to classify the dust stop variable as the dependent variable and the numerical variable as the independent variable. If you want to see what the result of the p-value is, you need to choose the right analysis method. If you have a categorical variable and a numeric variable and want to know if there are significant differences between them, you can use an independent samples t-test or a paired samples t-test (depending on your data type and study design).

These analyses can tell you if there is a significant difference between the two groups and provide a p-value.

In one-way ANOVA, two variables are involved: a numerical dependent variable and a subtyped independent variable.

The index to be examined in the test is called the test index, the condition that affects the test index is called the factor, the state of the factor is called the level, if there is only one factor change in the test, it is called the single factor test, if there are two factors change, it is called the buried two-factor test, and if there are multiple factors change, it is called the multi-factor test.

ANOVA is to analyze the test data, test whether the means of multiple normal populations with equal variance are equal, and then judge whether the influence of each factor on the test index is significant, according to the number of conditions affecting the test index, it can be divided into one-way ANOVA, two-way ANOVA and multi-factor ANOVA.

Basic concept: in ANOVA, we call a certain characteristic of the object to be examined as the test index, and the conditions that affect the test index are called factors, and the factors can be divided into two categories, one is that people can control (such as raw materials, equipment, education, profession and other factors); The other is beyond people's control (such as employee quality and opportunities, etc.). The factors discussed below are all controllable factors.

Each factor has several states to choose from, and each state that the factor can choose from is called the level of that factor.

ANOVA can be used to determine whether there is a significant difference between groups of observed data or results of processing. ANOVA starts from the variance of the observed variables, and studies which of the many control variables are the variables that have a significant impact on the observed variables.

How does ANOVA work if it needs to be determined? Take a one-way ANOVA as an example

First of all, the data is sorted into the correct format, generally x is a column, y is an example, and the analyzed data has a data label, you need to add another ** for description, the data grid cavity cover is as follows:

<> analyzed whether there was a difference in the survival time of mice between three different doses of tulip gavage, and the one-way ANOVA was performed with "group" as the independent variable and "survival time" as the dependent variable, and the results were as follows

As can be seen from the table above, the mean for group A is, and the standard deviation is; The mean of group B is that the accuracy of the spid code is; The mean for group c was , and the standard deviation was. It can be seen that there are differences between the three, and the survival time of the mice in group C is relatively longer, and the f-value of the one-way variance model is much less than the p-value, which has a significant difference, which also indicates that there is a significant difference between the three. It is also possible to use a graphical method to describe the comparison of the means of the three:

As can be seen from the line chart, the mean value of "group C" is the largest, followed by "group B" and finally "group A", which means that the mice in "group C" survived longer, then "group B" and finally "group A".

Univariate multivariate ANOVA is suitable for testing (two) factors and more than (two) observed variable rate beams.

Univariate statistics: one-factor pot experiment; If the data obtained in the experiment are equal in the number of replicates of each treatment, the analysis of variance of the one-factor data with the same number of replicates is used, and if the data obtained in the experiment are unequal in the repeated buried ingenuity, the analysis of variance of the one-factor data with the unequal number of replicates is used.