Consider a scenario where a factory manufactures microprocessors. These microprocessors are very complex; their performance is not identical despite being manufactured the same way, owing to minute differences in the silicon. Despite this, they can be classified into two groups - Normal- Performance and High-Performance. You know that the performance of the processor is tied to a resistance measurement R that you can perform in your lab and that R is expected to come from a normal distribution. You collect 10 samples of each kind of processor and obtain the following results: R for Normal-Performance Samples: 2.05, 0.59, 1.51, 1.44, -0.01, 2.04, 1.82, 3.89, 1.7, 1.36 R for High-Performance Samples: 4.23, 4.51, 2.11, 0.68, 0.11, 2.05, 0.23, -0.49, 2.35, 2.23 Use this data to create a GMM that can determine if a new sample is high-performance or normal- performance. Suppose you get a test sample with R = 1.75. What performance class would you select for this sample