本次澳洲代写是一个R统计的Lab

Goals:

(i) Fit and interpret the simple regression model in R;

(ii) Check model assumptions;

(iii) Explore more general estimation methods beyond the least squares method.
Data: In this lab, we will use the crime data from Agresti and Finlay (1997), describing
crime rates in the United States. The variables are state ID (sid), state name (state), violent
crimes per 100,000 people (crime), murders per 1,000,000 people (murder), the percentage of
the population living in metropolitan areas (pctmetro), the percentage of the population that
are white (pctwhite), the percentage of the population with a high school education or above
(pcths), the percentage of the population living under the poverty line (poverty), and the
percentage of the population that are single parents (single). Data can be obtained from the
shared folder in the computer labs, from the LMS, or downloaded from https://stats.idre.
ucla.edu/stat/data/crime.dta. The data file is in the Stata binary format (.dta) so you
will need to install the foreign package to import it into R.

1 Explore pairwise associations

R can import and export data in different formats. For example, the crime.dta data file is
produced by Stata, a commercial statistical software package. The R package foreign defines
useful functions to manipulate data formats different from .csv or .txt. If you working on
your own computer (rather than the lab machines), you might need to install it first:

install.packages(“foreign”)

Now we can load the data and compute some basic summary statistics:

library(foreign)
cdata <- read.dta(“crime.dta”)
summary(cdata)

1. Explore pairwise association between variables by constructing a scatter plot matrix. We
are interested in finding variables related to crime.

plot(cdata[, -c(1, 2)])

We excluded the first two columns since they do not contain numerical variables. Often
transforming the variables by the log() function helps us see linear association between
variables.

plot(log(cdata[, -c(1, 2)]))


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