hw8- ORIE 4740

# hw8- ORIE 4740 - 2 Done on Loose Leaf 3 The computational...

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HW # 8 – ORIE 4740 Shradha Jain (sj259) 1. > sum <- sum(orthoKmeans\$withinss) > sum [1] 22681.11 So, the smallest value of W (C) is 22681.11. The largest cluster has the significant predictor variables: { RBEDS, ADM, TH, REHAB }. The next-largest cluster has the significant predictor variables: { OUTV, KNEE95 }. The smallest cluster has the signi cant predictor variables: { BEDS, RBEDS, ADM, REHAB}. Yes, f different predictors significant in the different cluster.

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Unformatted text preview: 2. Done on Loose Leaf. 3. The computational complexity of a prediction using this cluster based regression model will be O(MKP). 4. a) Supervised b) Unsupervised c) False d) True e) True f) True 5. Done on Loose Leaf 6. mdat <- matrix(c(3,1, 1,3), nrow = 2, ncol=2, byrow=TRUE,dimnames = list(c("row1", "row2"), c("C.1", "C.2"))) eigen(mdat) \$values [1] 4 2 \$vectors [,1] [,2] [1,] 0.7071068 0.7071068 [2,] 0.7071068 -0.7071068 Rest done on loose leaf...
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hw8- ORIE 4740 - 2 Done on Loose Leaf 3 The computational...

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