Assignment08.pdf - EAPS 507 Assignment 08 Select the Best Linear Model for Forest Fires Claudia Aviles Instructions Important 1 Please change

Assignment08.pdf - EAPS 507 Assignment 08 Select the Best...

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EAPS 507 Assignment 08 Select the Best Linear Model for Forest Fires Claudia Aviles 10/25/2019 Instructions: Important 1. Please change yourUsername to your Purdue Career Account name; that is, your email without @pur- due.edu 2. Save the file with this new filename with the .Rmd (R Markdown) extension 3. Answer the questions below. 4. The minimum check for correctness is to run all chunks of code without Error messages. Due Dates Knit your file into a pdf or a Word document to upload in Circuit by 11:59AM on 10/30/2019 Peer-Review by 11:59AM on 11/01 on Circuit Final R Markdown (Rmd) file due to Blackboard by 11:59AM on 11/02 (Saturday) Packages: You will need the following packages, including the leaps package that contains the regsubsets() functions for model selection. library (tidyr) library (dplyr) ## ## Attaching package: dplyr ## The following objects are masked from package:stats : ## ## filter, lag ## The following objects are masked from package:base : ## ## intersect, setdiff, setequal, union library (ggplot2) library (GGally) ## Registered S3 method overwritten by GGally : ## method from ## +.gg ggplot2 1
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## ## Attaching package: GGally ## The following object is masked from package:dplyr : ## ## nasa library (leaps) Data Download the data from: . csv and save it in your working directory. This dataset poses a challenging regression task to predict the burned area of forest fires in the northeast region of Portugal. More information can be seen here: . You are encouraged to use this dataset for your final project. Reference: [Cortez and Morais, 2007] P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data. In J. Neves, M. F. Santos and J. Machado Eds., New Trends in Artificial Intelligence, Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, Guimarães, Portugal, pp. 512-523, 2007. APPIA, ISBN-13 978-989-95618-0-9. Available at: destfile <- "C:/Users/claud/Documents/forestfires.csv" forestdata <- read.csv (destfile, header = TRUE , sep = "," , stringsAsFactors= FALSE ) attach (forestdata) #Put the data into R search path #for direct access of variables by names Introduction We try to use linear regression to model area burned by forest fires in the Montesinho Park in Portugal. The variables include: 1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9 2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9 3. month - month of the year: ‘jan’ to ‘dec’ 4. day - day of the week: ‘mon’ to ‘sun’ 5. FFMC - FFMC index from the FWI* system: 18.7 to 96.20 6. DMC - DMC index from the FWI system: 1.1 to 291.3 7. DC - DC index from the FWI system: 7.9 to 860.6 8. ISI - ISI index from the FWI system: 0.0 to 56.10 9. temp - temperature in Celsius degrees: 2.2 to 33.30 10. RH - relative humidity in %: 15.0 to 100 11. wind - wind speed in km/h: 0.40 to 9.40 12. rain - outside rain in mm/m2 : 0.0 to 6.4 13. area - the burned area of the forest (in ha): 0.00 to 1090.84 (this output variable is very skewed towards 0.0, thus it may make sense to model with the logarithm transform).
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