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Mar 12, 2017 6:03 PM
(9151 views)

For the following set of data,

X | Y |

7.9 | 115 |

13 | 154 |

10.8 | 156 |

11.6 | 182 |

10.5 | 124 |

11.1 | 157 |

8.8 | 129 |

11.9 | 181 |

11.1 | 164 |

9 | 122 |

7.4 | 100 |

6.9 | 86.2 |

9.4 | 118.9 |

7.2 | 94.2 |

8.8 | 114.3 |

7.1 | 104.9 |

12.4 | 181.5 |

12.1 | 166.1 |

15.1 | 166.1 |

14.2 | 157.7 |

one can generate a linear fit model as shown below:

Linear Fit

Y = 24.522739 + 11.068566*X

Summary of Fit

RSquare 0.743712

RSquare Adj 0.729474

Root Mean Square Error 16.17783

Mean of Response 138.695

Observations (or Sum Wgts) 20

The question I have is: how should I go about and generate the 99% confidence intervals for the projected Y when X = 11.8 and 12.7? Or even better if you could elaborate the details on how JMP calculates the confidence intervals (such as, 95%, 99%) for a linear fit model.

Many thanks in advance!

Solved! Go to Solution.

1 ACCEPTED SOLUTION

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Mar 13, 2017 11:46 AM
(12511 views)

Solution

Second answer:

- No, it is not more appropriate to use the prediction interval if you are estimating or testing the expected value (or mean) of the response with an interval.
- Yes, it is more appropriate to use the prediction interval if you are estimating a proportion of the population.

They are both correct and both appropriate, depending on what you want to estimate or test. They answer different questions. They are not used for the same purpose. The former is for the statistic and the latter is for the data.

BTW, you can easily visualize either one or both of these intervals when you fit the line in Bivariate. Use same menu under the plot and select **Confid Shaded Fit** or **Confid Shaded Indiv**. One is related to the uncertainty in the sample statistic (regression line) and the other is related to the uncertainty of individual observations.

Learn it once, use it forever!

16 REPLIES

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Mar 13, 2017 2:45 AM
(9135 views)

Hi!

to get the predicted values and confidence intervals you would do the following:

- Use the hotspot (red triangle) next to "Linear Fit".
- You will find a menu entry "Set alpha Level". Set this to 1% if you are interested in 99% confidence.
- Now use the same menu and choose "Save predicteds" and "Mean Confidence Limit formula" (or "Indiv Confidence Limit Formula).
- As a result you will find 3 new columns in the data table.
To see how jmp calculates the prediction/CIs you could just klick on the "+"-symbol on the left hand side (next to the column names).

To get the prediction and CI for specific values of X you would just add new rows (like it did in the picture above: Row 21 and 22). JMP will automatically generate the predictions and CI-values for the X-values you enter.

Hope that helps.

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Mar 13, 2017 10:51 AM
(9111 views)

Thanks for the reply! This was what I did. However, I was puzzled by the Vec Quadratic term in the formula as shown belown and also I was expecting 2.576 multiplying the standard error (for 99% confidence interval calculation instead of 2.878 or 1.96 for 95% confidence interval calculation)

(24.5227390655596 + 11.0685662563684 * :X) - 2.87844047273861 *

Sqrt(

Vec Quadratic(

[1.00352196297906 -0.0924403260280234, -0.0924403260280234

0.00896173786020585],

[1] || :X

) * 261.722077754035

)

2nd quesition: is it more appropriate to use Indiv Confidence Limit Formula than Mean Confidence Limit Formula? If not, why so?

Thanks!

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Mar 13, 2017 11:08 AM
(9101 views)

The **Vec Quadratic()** function is an optimized way to perform the matrix computations before applying the scalar multipliers.

You want a two-sided 99% confidence interval, so the probability cutoff on each tail is 0.005%. This calculation with the given 18 degrees of freedom for the error indicates the correct multiplier:

**t Quantile( 0.995, 18 ) -> 2.87844047273861**

Note that you can select Graph > Profiler and enter all three of the formula columns. You can now change X by dragging or typing over the current value (in red) and entering an exact value. The profilers will give the point estimates, lower and upper bounds.

Learn it once, use it forever!

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Mar 13, 2017 11:23 AM
(9097 views)

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Aug 15, 2017 8:17 AM
(7105 views)

I can't answer your second question about which kind of interval is appropriate because you have not stated how you would use or interpret the interval. They are both appropriate but for different questions. It depends on the alternative hypothesis you are trying to test.

Learn it once, use it forever!

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Aug 15, 2017 5:52 AM
(7117 views)

Can you show how JMP performs Vec Quadratic calculation? I am looking for a generic equation that JMP uses to calculate prediction interval for OLS. Thanks

Ram

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Aug 15, 2017 8:25 AM
(7102 views)

Help->Scripting Index will tell you that Vec Quadratic(S, X) is a evaluated as Vec Diag(X *S * X`).

Vec Diag(X) returns the diagonal elements of the square matrix as a vector.

-Jeff

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Aug 15, 2017 8:58 AM
(7095 views)

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Aug 15, 2017 9:14 AM
(7085 views)

Taken directly from the description and explanation I just posted, it is a "symmetric matrix," the "inverse covariance matrix." It refers to the covariance of the parameter estimates, of course.

Learn it once, use it forever!