- Simple linear regression
- If the p-value for the model is less than alpha, then it is believed the single
independent variable is helpful in predicting/understanding the dependent
variable.
- If the p-value for the model is greater than alpha, then it is believed the
single independent variable is not helpful in predicting/understanding the
dependent variable and that β1 = 0.
- Multiple linear regression
- If the p-value for the model is less than alpha then it is believed at least
some of the independent variables are helpful in predicting/understanding
the dependent variable.
- If the p-value for the individual independent variable is less than
alpha this implies that the particular independent variable is helpful in
predicting/understanding the dependent variable.
- if the p-value for the independent variable, xi, is greater than or equal to
alpha it is believed that βi = 0, and thus the independent variable can be
removed from the model.
- General linear model
- If the p-value for the model is less than alpha then it is believed
that at least some of the independent variables are helpful in
predicting/understanding the dependent variable.
- If the p-value for the individual independent continuous variable is less
than alpha this implies that the particular independent variable is helpful
in predicting/understanding the dependent variable.
- If the p-value for the individual independent categorical variable is less than
alpha this implies that the particular independent variable is helpful in
predicting/understanding the dependent variable.
- If some categories of a categorical variable are significant (p-value less
than alpha) but others are not, use the categorical variable as it
is.
- Handling this situation in a better manner goes beyond the scope
of this text.
- If the p-value for the independent variable, xi, is greater than or equal to alpha
it is believed that βi = 0, and thus the independent variable can be removed
from the model.