I never have enough time to review everything I want to, but just wanted to get a taste, if it is really as simple as I described.

- Thread starter hlsmith
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- Tags sensitivity analysis

I never have enough time to review everything I want to, but just wanted to get a taste, if it is really as simple as I described.

I don't have my review, many pages long open right now - I will have to wait to Friday to pull it up. But two starting sources are "Excel 2010: Data Analysis and Business Modeling" by Wayne Winston 2011 pages 127-136 (this includes the use of tables for sensitivity analysis ) and 143-148 (this uses the scenario manager). A limit of this, of all excel approaches I know, is that you have to create the formulas that drive your model first. Solver and the various tables use these formulas to generate the result (they are essentially table/graphical features that utilize pre-existing formulas you create).

Scenarios are covered in 593-598 of the "Missing Manual" by Mathew McDonald 2010. Note that these are starting points. I had to flesh them out with lots of links. I will try to pull out that document which is much more complete on Friday.

One thing the author commented on was there was at least three ways to calculate R squared - which I had not known. I always thought it was SSR/SST.

Three take aways fron that link I think:

1) R squared is only explained variance with an intercept.

2) You calculate r squared differently with and without an intercept.

3) You really can not compare, at least in terms of R squared, a model with and without an intercept.

Interestingly the author notes, which I had not heard before that the assumptions that the conditional mean of the error term was zero was only a major issue without the intercept. I always assumed this was a critical issue with regression generally (well linear regression).

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Three take aways fron that link I think:

1) R squared is only explained variance with an intercept.

2) You calculate r squared differently with and without an intercept.

3) You really can not compare, at least in terms of R squared, a model with and without an intercept.

Interestingly the author notes, which I had not heard before that the assumptions that the conditional mean of the error term was zero was only a major issue without the intercept. I always assumed this was a critical issue with regression generally (well linear regression).

Pass your a+ course and comptia network+ exams in first try by using our northwestern passguide and www.usna.edu and best quality Jacksonville University

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