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Which of the two models would you choose for predicting the point of interest and why?\n \n **Answers**:\n \n-1.\n-1.\n-1.\n+1. Our main sanity check for these results would be verifying that the simplier model (LM) indeed has a lower variance than the more complex model (Spline).  Conversely, we could also observe that the simplier model has a higher bias than the more complex model.\n+\n+2. We would need to create a test set from out simulation, eg. we would assign one of our 10,000 samples of $n=500$ to serve as the test data.  Then, we would build a model for each other 9,999 sets then make predictions for $y$ ( perhaps report the RMSE value since we will have to compute a large number of values and store them in a list ) on each using our test set.  \n+\n+3. The Spline model, as although it has a very high variance model to model, it captures the desired value far more often. ","path":"PS06.Rmd","position":144,"original_position":144,"commit_id":"51e4f818a046ce46847b72e946d25fcb2b5c0aff","original_commit_id":"51e4f818a046ce46847b72e946d25fcb2b5c0aff","user":{"login":"akim17","id":17258637,"avatar_url":"https://avatars3.githubusercontent.com/u/17258637?v=4","gravatar_id":"","url":"https://api.github.com/users/akim17","html_url":"https://github.com/akim17","followers_url":"https://api.github.com/users/akim17/followers","following_url":"https://api.github.com/users/akim17/following{/other_user}","gists_url":"https://api.github.com/users/akim17/gists{/gist_id}","starred_url":"https://api.github.com/users/akim17/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/akim17/subscriptions","organizations_url":"https://api.github.com/users/akim17/orgs","repos_url":"https://api.github.com/users/akim17/repos","events_url":"https://api.github.com/users/akim17/events{/privacy}","received_events_url":"https://api.github.com/users/akim17/received_events","type":"User","site_admin":false},"body":"You should be a little more specific with this last answer. When you mean \"captures the desired value far more often\", do you mean that the results will be relatively unbiased? If so, when would high variability and low bias be useful? When might we want a more biased 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