Respuesta :
The most important measure used in regression diagnostics is the simple residual.
What is linear rigression?
In statistics, a scalar response and one or more explanatory factors are modeled using a linear approach called linear regression. Simple linear regression is used when there is only one explanatory variable; multiple linear regression is used when there are numerous explanatory variables.
Hypothesis testing is used to confirm if our beta coefficients are significant in a linear regression model. Every time we run the linear regression model, we test if the line is significant or not by checking if the coefficient is significant.
If there is a significant linear relationship between the independent variable X and the dependent variable Y, the slope will not equal zero.
The null hypothesis states that the slope is equal to zero, and the alternative hypothesis states that the slope is not equal to zero.
The most important measure used in regression diagnostics is the simple residual (e). This is the difference between each observed and predicted value of Y.
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