R help - Jun 2017 - Exercise in R

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


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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]]
>
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