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**Best subsets regression**

Model selection Best subsets regression

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Statement of problem A common problem is that there is a large set of candidate predictor variables. Goal is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability.

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Example: Cement data Response y: heat evolved in calories during hardening of cement on a per gram basis Predictor x1: % of tricalcium aluminate Predictor x2: % of tricalcium silicate Predictor x3: % of tetracalcium alumino ferrite Predictor x4: % of dicalcium silicate

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Example: Cement data

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**Two basic methods of selecting predictors**

Stepwise regression: Enter and remove predictors, in a stepwise manner, until no justifiable reason to enter or remove more. Best subsets regression: Select the subset of predictors that do the best at meeting some well-defined objective criterion.

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**Why best subsets regression?**

# of predictors (p-1) # of regression models 1 2 : ( ) (x1) 2 4 : ( ) (x1) (x2) (x1, x2) 3 8: ( ) (x1) (x2) (x3) (x1, x2) (x1, x3) (x2, x3) (x1, x2, x3) 4 16: 1 none, 4 one, 6 two, 4 three, 1 four

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**Why best subsets regression?**

If there are p-1 possible predictors, then there are 2p-1 possible regression models containing the predictors. For example, 10 predictors yields 210 = 1024 possible regression models. A best subsets algorithm determines the best subsets of each size, so that choice of the final model can be made by researcher.

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**What is used to judge “best”?**

R-squared Adjusted R-squared MSE (or S = square root of MSE) Mallow’s Cp

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R-squared Use the R-squared values to find the point where adding more predictors is not worthwhile because it leads to a very small increase in R-squared.

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**Adjusted R-squared or MSE**

Adjusted R-squared increases only if MSE decreases, so adjusted R-squared and MSE provide equivalent information. Find a few subsets for which MSE is smallest (or adjusted R-squared is largest) or so close to the smallest (largest) that adding more predictors is not worthwhile.

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Mallow’s Cp criterion The goal is to minimize the total standardized mean square error of prediction: which equals: which in English is:

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**Mallow’s Cp criterion Mallow’s Cp statistic estimates where:**

SSEp is the error sum of squares for the fitted (subset) regression model with p parameters. MSE(X1,…, Xp-1) is the MSE of the model containing all p-1 predictors. It is an unbiased estimator of σ2. p is the number of parameters in the (subset) model

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**Facts about Mallow’s Cp**

Subset models with small Cp values have a small total standardized MSE of prediction. When the Cp value is … near p, the bias is small (next to none), much greater than p, the bias is substantial, below p, it is due to sampling error; interpret as no bias. For the largest model with all possible predictors, Cp= p (always).

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**Using the Cp criterion So, identify subsets of predictors for which:**

the Cp value is smallest, and the Cp value is near p (if possible) In general, though, don’t always choose the largest model just because it yields Cp= p.

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**Best Subsets Regression: y versus x1, x2, x3, x4**

Response is y x x x x Vars R-Sq R-Sq(adj) C-p S X X X X X X X X X X X X X X X X

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**Stepwise Regression: y versus x1, x2, x3, x4**

Alpha-to-Enter: Alpha-to-Remove: 0.15 Response is y on 4 predictors, with N = 13 Step Constant x T-Value P-Value x T-Value P-Value x T-Value P-Value S R-Sq R-Sq(adj) C-p

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Example: Modeling PIQ

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**Best Subsets Regression: PIQ versus MRI, Height, Weight**

Response is PIQ H W e e i i M g g R h h Vars R-Sq R-Sq(adj) C-p S I t t X X X X X X X X X

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**Stepwise Regression: PIQ versus MRI, Height, Weight**

Alpha-to-Enter: Alpha-to-Remove: 0.15 Response is PIQ on 3 predictors, with N = 38 Step Constant MRI T-Value P-Value Height T-Value P-Value S R-Sq R-Sq(adj) C-p

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Example: Modeling BP

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**Best Subsets Regression: BP versus Age, Weight, ...**

Response is BP D u W r S e a P t i t u r A g B i l e g h S o s s Vars R-Sq R-Sq(adj) C-p S e t A n e s X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X

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Stepwise Regression: BP versus Age, Weight, BSA, Duration, Pulse, Stress Alpha-to-Enter: Alpha-to-Remove: 0.15 Response is BP on 6 predictors, with N = 20 Step Constant Weight T-Value P-Value Age T-Value P-Value BSA T-Value P-Value S R-Sq R-Sq(adj) C-p

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**Best subsets regression**

Stat >> Regression >> Best subsets … Specify response and all possible predictors. If desired, specify predictors that must be included in every model. (Researcher’s knowledge!) Select OK. Results appear in session window.

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Solutions to Tutorial 5 Problems Source Sum of Squares df Mean Square F-test Regression2174.41 40.34 Residual862.51653.9 Total3036.917 ANOVA Table Variable.

Solutions to Tutorial 5 Problems Source Sum of Squares df Mean Square F-test Regression2174.41 40.34 Residual862.51653.9 Total3036.917 ANOVA Table Variable.

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