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R Coding Homework, Numbers One Through Four
Code to answer these questions and comments that explain what is being done. Do not need the last 2 questions answered. Does not require any textbooks, the data is provided on the attached sheet. Numbers (1-4). I think this assignment is fairly easy for someone that knows R since this is an introductory course.
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Homework #3 [15 pts] Due date: 03/07 at 10:00am ** You are required to submit an error-free, fully commented R code Q1. While running an experiment about wafer manufacturing, you collected the following data: Inputs Output Observation # Furnace setting Temperature setting Layer Thickness 1 -1.056 -1 1004 2 1.120 -1 1636 3 -1.045 0.667 852 4 0.952 0.667 1506 5 0.993 -0.167 1555 Write an R code to do the following: (1) [5 pts] Perform a Leave one out cross validation (LOOCV) using the linear regression model with two input variables and kNN (k = 2) with two input variables. Report your final LOOCV mean squared error. (2) [2 pts] Now, using the “best” model, re-train using all the data. Report the estimated values of the model parameters. (3) [2 pts] Use the best model to predict at (furnace setting, temperature setting) = (1,1). Report the final value of the prediction. (4) [4 pts] Train a new model using all the data. The model is a quadratic polynomial regression of the following form: Layer thickness = beta0 + beta1*furnace setting + beta2*temperature + beta3*temperature^2 Report the estimated values of the model parameters. Also, what is your prediction for this model at (furnace setting, temperature setting) = (1,1)? (5) [1 pts] In 1-2 sentence, why do you think Leave one out cross validation is a suitable validation approach for this experiment? (6) [1 pts] In 1-2 sentences, what is your comment on the performance of the kNN regression model in the first question.
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