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Chapter 6: How Can I Miss You If You Won’t Leave? Regression Model Selection
                                  The secret to a punter’s success: An example                          113

                                  Returning to the punt distance example from earlier in this chapter, suppose
                                  that you analyzed the punt distance data by using the best subsets model
                                  selection procedure. Your results are shown in Figure 6-2. This section fol-
                                  lows Minitab’s footsteps in getting these results and provides you with a
                                  guide for interpreting the results.

                                  Assuming that you already used Minitab to carry out the best subsets selec-
                                  tion procedure on the punt distance data, you can now analyze the output
                                  from Figure 6-2. Each variable shows up as a column on the right side of the
                                  output. Each row represents the results from a model containing the number
                                  of variables shown in column one. The X’s at the end of each row tell you
                                  which variables were included in that model. The number of variables in the
                                  model starts at 1 and increases to 6 because six x variables are available in
                                  the data set.

                                  The models with the same number of variables are ordered by their values
                                     2
                                  of R  adjusted and Mallows C-p, from best to worst. The top-two models (for
                                  each number of variables) are included in the computer output.

                                  For example, rows one and two of Figure 6-2 (both marked 1 in the Vars
                                  column) show the top-two models containing one x variable; rows three and
                                  four show the top two models containing two x variables; and so on. Finally,
                                  the last row shows the results of the full model containing all six variables.
                                  (Only one model contains all six variables, so you don’t have a second-best
                                  model in this case.)

                                  Looking at the first two rows of Figure 6-2, the top one-variable model is the
                                  one including hang time only. The second-best one-variable model includes
                                  only right foot flexibility. The right foot flexibility model has a lower value of
                                   2
                                  R  and a higher Mallow’s C-p than the hang time model, which is why it’s the
                                  second best.

                                  Row three shows that the best two-variable model for estimating punt dis-
                                  tance is the model containing right leg strength and overall leg strength.
                                  The best three-variable model is in row five; it shows that the best three-
                                  variable model includes right foot strength, right foot flexibility, and overall
                                  leg strength. The best four-variable model is found in row seven and includes
                                  right foot strength, right and left foot flexibility, and overall foot strength.
                                  The best five-variable model is found in row nine and includes every vari-
                                  able except left foot strength. The only six-variable model with all variables
                                  included is listed in the last row.
















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           11_466469-ch06.indd   113                                                                   7/23/09   9:27:04 PM
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