For the Final Project, please analyze two datasets of your choice (please see 1. below) and pick one model (formula) for each dataset that you think best predicts the response variable in each set.Please assume that a client brings you these two datasets (which you have chosen) and requests that you find a “good” model for each. To convince that client that you were thorough in your analysis, please type-up one “report” for each dataset: a report for one dataset — written in full sentences and divided into paragraphs — and a report for the other dataset — written in bullet points of your key/main takeaways. Please include screenshots from your analysis in JMP (or another software, if you prefer to use any software alternative to JMP).Here’s an outline of main things you may want to consider, not necessarily in this order (use this as a general guide; this is not a complete list and you don’t have to use each point of this outline):1. Pick 2 datasets (one for a full-sentence write-up and one for bullet-points write-up) and please ensure that each has:a) minimum of 32 observations (data points), 50-500 would be idealb) minimum of 3 regressors, 5-10 would be great2. Think about the data:a) source (ex. do YOU think the source is trustworthy?)b) is the data still applicable (is there anything that makes the data outdated/no longer accurate?)c) are there any problems with the data (missing values, unreasonable entries, etc.?) Looking at distributions of the response and each regressor may be helpfuld) if you fix/change/exclude anything, MAKE SURE to discuss it (what/why/how/etc.)e) include anything else that YOU think is important3. Start fitting simple models and models with multiple variables:a) what regressors appear to be significant (individually and as a group)4. Residuals (using one type of scaled residuals would probably be best) – look for patterns, problematic data points:a) is some nonlinear relationship present between (a) regressor(s) and response and do we need a data transformation?b) are there any outliers and how to handle them (keep or exclude)c) test for Lack of Fit5. Transformations (and weighting) of variables:a) what and how to transform to, if possible, correct non-linear relationships between the variables in the data6. Diagnostics for Leverage and Influence7. Selecting variable and building a model8. Model validationI will talk more about the project during our classes and I’ll make sure to keep pointing out what you may want to consider including in your project work as we discuss new topics.If you wish to use data from your employer or another non-publicly available source, please ensure that you have a written and signed permission, from whoever is responsible for the data, to use and release the data as the data may be confidential. For that reason, I’d strongly discourage the use of company data..doc filePurchase answer to see full
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