Question

The issues of validity and reliability are critical when evaluating your regression model. For reliability the most significant issues are serial correlation, heteroscedasticity and collinearity. What are these issues and how do they affect the reliability of our regression model? And how can we spot them if they are there?

Answer 1

The main purpose of the regression analysis was to isolate the relationship between each independent variable and the dependent variable. The interpretation of the regression coefficient is that it represents the average change of the dependent variable for each transformation of 1 unit into independent variables when you hold the other independent variables. This last section is important to the discussion of plurality.

The idea is that you can change the value of one independent variable and another. However, when the independent variables are related, it appears that the change in one variable is related to the displacement in another variable. The stronger the relationship, the harder it is to change one variable without changing another. For the model, it is difficult to assess the relationship between each of the independent variables and the dependent variable, since the independent variable tends to change unilaterally.

Multi-Structural Functions: This type occurs when we create templates with other words. In other words, it is a product of the model we refer to and does not exist in the data itself. For example, if the word square X is to make a model curved, there is clearly a correlation between X and X2.
    Specific multidimensional data: This type of multifunctionality exists in direct data, not an artifact of our model. Observational experiments are likely to show such multidimensionality.