When conducting statistical analyses such as regression, there are certain assumptions that have to be met....

When conducting statistical analyses such as regression, there are certain assumptions that have to be met.

What are the four assumptions of regression that must be tested in order to ensure that statistical results are trustworthy?
What are some of the errors that can occur if assumptions are not met?
What is the danger of reporting regression data that has not met these assumptions?
Is it acceptable to report regression data that has met some, but not all of the assumptions? Why or why not?

Expert Solution

When conducting statistical analyses such as regression, there are certain assumptions that have to be met.

What are the four assumptions of regression that must be tested in order to ensure that statistical results are trustworthy?

There are four assumptions associated with a linear regression model:

Linearity: The relationship between X and the mean of Y is linear.
Homoscedasticity: The variance of residual is the same for any value of X.
Independence: Observations are independent of each other.
Normality: For any fixed value of X, Y is normally distributed.

What are some of the errors that can occur if assumptions are not met?

Answer: the regression line is not the best fitted regression line, ordinary lease square are not minimum, data not follows the normality.

What is the danger of reporting regression data that has not met these assumptions?

predicting the wrong estimate.

Is it acceptable to report regression data that has met some, but not all of the assumptions? Why or why not?

Not acceptable, it would lead the not well fitted regression, and it could Couse the error while the predicting the value.

orchestra answered 1 year ago

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