Confidence Intervals (CI) for Dependent Variable Prediction

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 (b0) and slope coefficient (b1) 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.

H0 = 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 b1 value not statistically different from zero indicates no relationship between Y and X).
tcalc = the standard script for the output of your significance test on the regression model’s parameters and its absolute value must exceed the designated tcritical on a two tailed significance test.

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 (b0) and slope coefficient (b1) 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.

H0 = 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 b1 value not statistically different from zero indicates no relationship between Y and X).
tcalc = the standard script for the output of your significance test on the regression model’s parameters and its absolute value must exceed the designated tcritical on a two tailed significance test.