If two independent samples are obtained, then we can ask whether
they correlate to some extent. Correlation coefficients measure the
strength of the linear relationship between these two variables
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and read the content from file "Golf.txt" which lists divorce rates and
number of golf courses for 20 US cities. The obtain Pearson's Product
Moment Correaltion Coefficient ... > Golf <- read.table("http://caspar.bgsu.edu/~courses/stats/Labs/Datasets/Golf.txt", header=TRUE) > cor(Golf$GolfCourses,Golf$DivorceRate); or simply obtain the correlation matrix for all variables contained in the dataframe, in this case using the method for rank correlations > cor(Golf, method = "spearman") For a more comprehensive view of the associations between variables create a correlation matrix with library Deducer, then print the results ... > library(Deducer) > Golf.corrMat<-cor.matrix(variables=d(Golf$GolfCourses,Golf$DivorceRate), test=cor.test, method='pearson', alternative="two.sided") > print(Golf.corrMat) Create scatterplot of the correaltion matrix with added linear model for fit ... > qscatter_array(d(GolfCourses,DivorceRate),d(GolfCourses,DivorceRate),data=Golf) + geom_smooth(method="lm") or a sweet one with ggpairs from library GGally ... > library(GGally) > ggpairs(Golf)
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