data analysis

############### Data Analysis 9 ################################################### # Upload the dataset CorvalisRentalsSp2018.csv call it "rentals" rentals = read.csv(file.choose(), header = TRUE) # attach the dataset attach(rentals) # look at the variable names in the dataset names(rentals) #Create a scatterplot matrix of all the varaibles in the rentals dataset pairs(rentals) #yuck! Too many variables. Let's remove the categorical variables. # Create a scatterplot matrix with only the columns 1 the 4. pairs(rentals[,1:4]) # Fit the full module with all 7 of the explanatory variables. full.model = lm(rent~rooms+baths+sqrfoot+house+campusclose+pets+new) summary(full.model) # Perform the basic model selection process. # This is described in data analysis 9 and in lesson 49. # Hints: You can use R to check your hand written answers for parts 3 and 4 # using the commands confint() and predict() with your final model.