The number that need to be working on will be available for me tomorrow. (I can send the extra information)
The homework will be run for plagiarism test, therefore original work must be created.
The answer have to fitted in the box provided.
The interpretation must be well written and clear explanation.
This homework should be done in R. (I need the R script and all relevant work that are done, including steps and any graph and equation, and I should be able to open the r.file)
The interpretation should address Residual diagnosis (if capable), outlier need to be address (if any).. and any other important related..
BSTAT 5325 FINAL EXAMNAME: __________________________________ Challenge 1 (25 points). The tab dealer has hypothetical data regarding the sales prices of franchised auto dealerships nationwide. The variables are: 1) PRICE (the sales price, in $105, of the dealership); 2) SALES (the dealership’s most recent annual sales, in $106); 3) AGE (the age, in months, of the dealership); 4) UNITS (the dealership’s most recent unit sales); 5) ACREAGE (the footprint, in acres, of the dealership); 5) BLDG (the footprint, in 103 ft2 of the dealership’s building(s)); 6) COMPS (the number of franchised dealership competitors in the dealership’s market; 7) COBRNDS (the number of other franchised dealerships in that market owned by the dealership’s owners); and 8) WITHIN (the number of franchised dealerships located within 3 miles of the dealership). You have been assigned a starting observation number in Blackboard. Select that observation and the next 80 for your training sample. Then select the next 20 as your validation sample. With the former use your regression and regression-model-building skills to estimate a “good” (by your standards) model to predict the sales price of a dealership. Evaluate that model using your validation sample. In the space provided, report the steps you took and the conclusions you arrived at, as well as your assessment of your model’s performance. Challenge 2 (25 points). A state is considering an overhaul of its restaurant health-inspection protocol. The tab health presents the results (Pass or Fail) of inspections done (using the proposed protocol) on a large number of of small (seating cap. < 50)="" restaurants.="" this="" tab="" also="" includes:="" 1)="" exper="" (the="" number="" of="" years="" of="" experience="" of="" that="" restaurant’s="" general="" manager;="" 2)="" age="" (the="" number="" of="" years="" that="" particular="" restaurant="" has="" been="" in="" that="" particular="" location;="" 3)="" chain="" (whether="" that="" restaurant="" is="" part="" of="" a="" chain;="" 1="Yes);" and="" 4)="" region="" (the="" region="" (a,="" b="" or="" c)="" in="" the="" state="" of="" that="" location).="" a)="" you="" have="" been="" assigned="" a="" starting="" observation="" number="" in="" blackboard.="" select="" that="" observation="" and="" the="" next="" 99="" for="" a="" sample="" of="" n="100." using="" the="" first="" 75="" of="" these="" as="" a="" training="" sample,="" formulate="" (and="" summarize)="" a="" model="" that="" would="" allow="" you="" to="" predict="" whether="" a="" particular="" location="" will="" pass="" the="" inspection.="" how="" do="" those="" predictors="" that="" you="" use="" in="" your="" model="" influence="" the="" likelihood="" of="" a="" location’s="" passing?="" b)="" using="" your="" fitted="" prediction="" model,="" estimate="" the="" likelihood="" of="" your="" 25="" held-out="" restaurants="" passing="" the="" inspection="" (use="" this="" rule="" …="" if="" the="" estimated="" probability="" of="" passing="" is="" less="" than="" or="" equal="" to="" 0.35,="" forecast="" that="" restaurant="" as="" a="" “fail”;="" if="" the="" estimated="" probability="" is="" greater="" than="" or="" equal="" to="" 0.65,="" forecast="" that="" restaurant="" as="" a="" “pass”).="" summarize="" how="" well="" this="" fitted="" model="" works="" with="" a="" 2="" x="" 2="" table.="" challenge="" 3="" (25="" points).="" use="" the="" data="" in="" your="" training="" sample="" from="" the="" previous="" challenge="" to="" formulate="" a="" classification="" tree="" (pruned="" according="" to="" the="" “minimum="" xerror”="" rule).="" describe="" the="" “paths”="" that="" your="" tree="" retains="" (e.g.,="" “if="" age="" ≤="" 12="" and="" exper=""> 4 then “PASS”). In a simple 2 x 2 table, report how well your classification tree does in predicting the pass/fail question in your validation sample. Challenge 4 (25 points). The data in the tab visits are a quarterly time series depicting unique visitors to a health care website over a 20-year period. For this challenge, use the first 72 periods as your training sample and the last 8 periods as your validation sample. If you had used multiplicative Loess decomposition to forecast those last 8 quarters, what would the correlation between your forecasts and the actual values have been? What would it have been had you used an ARIMA(p,d,q)(P,D,Q)4 model?