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
Exercise in R
2 messages · Alex Ferrara, Bert Gunter
We don't do homework here. Ask your professor for help. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Fri, Jun 9, 2017 at 9:10 AM, Alex Ferrara <Rikushadow at hotmail.it> wrote:
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 at 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.