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| That's because your table values for `i` are the confidence levels and the table should be constructed in the following manner: Table[{i, 13/147 - InverseCDF[NormalDistribution[0, 1], 1 - (1 - i)/2]*Sqrt[(13/147 (1 - 13/147))/147], ... |
| If you type `meanPath = Mean[simData]` you get the global mean, just one number. You need to map the mean to the values at the various times: meanPath = Map[Mean, simData["ValueList"]] Similarly for `Quantile`: lowerBoundPath =... |
| Hi Piotr, I wanted to let you know that I figured it out. Thank you very much for your time and help. Best regards, Mitchell Sandlin |
| Hello Mitch, I wanted to confirm that we were unable to reproduce what you were seeing, as noted in your report. If you find anything else, please email wolfram-u@wolfram.com. Christine Owens Project Manager Wolfram U |
| Hi Glanluca, Michael & Jim; I want to thank all of you for the assistance you gave me. With your help, I finally figured out the solution, and the journey was certainly no walk in the park. However, now I have a much better understanding of the... |
| Thank you. I really appreciate all of the follow-up help with (applying) the course content |
| Thanks! I'll just point out that Gianluca's method is basically the standard way you'd do this. The only reason to use linear combinations of eigenvectors is to turn matrix multiplication into scalar multiplication (which could give performance... |
| Sure, you can think of `Eigenvalues` as a pure function, but that's beside the point in terms of an explanation. `Eigenvalues` returns a list, and `Part` (I'm using the `[[]]` syntax for `Part`) extracts elements from a list. So, when you said you... |
| You've misplaced the brackets. In the first plot you used {{6/10, 1/2}, {- 7, 6/5}} . {x, y} but in the later ones you used {{6/10, 1/2}, {-pred, 6/5} . {x, y}} |
| Thanks Again, your answer is exactly what I needed. |