Hi i need some help with this exercise: FIles: https://mega.nz/#!JxMFGIwC!qA85SBIBRVagCzYfmLwSvGuNK_qXqCXrakPxXryCpGg
#PARZIAL 3: GEO #Data: # Shapefile "INCOME" contains dummy information about revenue # Common Abbreviations in the "INCOME" variable and the centroid altitude #dell common in the variable "ALT" #Richieste # 1 #map of the variable "INCOME", choosing an appropriate color scale (save the map in pdf) #2 #map of the variable "ALT", choosing a color palette from green to white, passing for brown (save the map in pdf) # 3 #calculate the confidence interval of the pearson correlation coefficient between the variables considered # 4 # Graph (and save in pdf) the relationship between variables via scatterplot, #Set the graphic parameters appropriately and enter the correlation value in the title #del previous point # 5 #Comment what appears from the analysis performed # 6 # Find a way to map variables on the same scale so that it is obvious #the correlation found. (Suggestion: to use transformation, and possibly reversal of signs) # 7 #fit a linear model that explains the income in function of altitude (original scales) # 8 #load the metered values and those observed on the same scale # 9 #wrap the gap between residual and observed and write the instructions that they print # Consoles municipalities with the worst estimate (below and above estimated) How should i do? Thanks for reply [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.