Consider the use of k-NN for estimation, using k=3. The dependent variable is y, and the independent variables are x₁ and x₂. Table 1 shows the Training Set (observations #1 through #6) followed by four new observations (observations #7 through #10) which the algorithm will score (estimate the y value). x₁ and x₂ values are standardized.

Table 1: Data

Table 1: Data Obs # y X 1 X 2 4 -0.3 0.7 2 7 1. 1 0.5 3 3 0.6 2.2 4 4 1.2 1.2 5 5 0.2 -0.6 6 2 -0.5 0.8 0.2 0.4 0.1 0.2 -0.3 0.7 10 -1Table 2: Euclidean distances Obs # 1 2 3 4 5 6 7 0.58 0.91 1.84 1.28 1.00 0.81 8 0.64 1.04 2.06 1.49 0.81 0.85 9 0.00 1.41 1.75 1.58 1.39 0.22 10 0.76 2.16 2.00 2.21 2.00 0.54