Monte Carlo Simulation

Use these to delineate what outcomes are possible and which are not possible.

=Or() Statements

Use these to yield "True" only if every argument is satisfied.

Standardize the Form of Your Models for Solver

Solver doesn't require you to set up your model in any particular way ... but a user is going to have to input target/objective, changing cells/decisions, and constraints, and should be able to find them easily.

How to Evaluate Your Regression Overall

Look at "significance F". This is a p-value for the whole regression, and should be below 0.05.

Regression Modelling Strategy

Old School: start with one X, and add them one by one until happy with the regression. Con: the smaller models that you're building on are probably biased, and may lead you in the wrong direction.

New School: start with all (your available) X's, and delete them one by one until happy with the regression. Con: when you start out your model looks like junk, and may continue to look like junk for a long time as you prune it.

Dr. Tufte prefers the latter method. The risks of omitting variables tend to be worse than those of including irrelevant ones ... so I lean towards using the extra computing power required by the New School method.

Neither method solves the problem of what variable to add/cut first, or whether to do them in groups or one by one.

Better Understanding of Regression Output

Luke learned more about interpreting t-ratios, p-values, r-squared, and adjusted R-squared.

Regression Tool

1) It has most of what you need.
2) It includes stuff you may not need, or may use inappropriately.