- In all likelihood, your model will not perfectly predict Y.
- The SEE can be extended to determine the confidence interval for a predicted Y value. A common CI to test for a predicted value is 95%.
- Your regression parameters, the y-intercept (b
_{0}) and slope coefficient (b_{1}) will need to be tested for significance before you can generate a confidence interval around your model’s project Y value around an expected X value. - H
_{0}= 0 is the null hypothesis when testing either parameter and you will look to reject this in significance, (note: typically the greater emphasis is on the slope coefficient, as b_{1}value not statistically different from zero indicates no relationship between Y and X). - t
_{calc}= the standard script for the output of your significance test on the regression model’s parameters and its absolute value must exceed the designated t_{critical}on a two tailed significance test.

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