Problem 1: Download Data 1.csv from D2L/ course content/ Homework 6. The data re
ID: 3178645 • Letter: P
Question
Problem 1: Download Data 1.csv from D2L/ course content/ Homework 6. The data represents the following for an experiment on Lutetium oxide (a ceramic material): T Temperature in Degrees C B a Bulk Modului in GPa nu Poisson's Ratio v a) obtain the matrix of scatterplots of the three variables from R and paste it here. b) Calculate a Pearson's correlation coefficient matrix using R and fillin the blanks of the following table. (keep 3 decimal places) c) Based on the above table and scatterplots, describe the relationship between each pair of variables (T, B, and nu) for this material. d) calculate a spearman's correlation coefficient matrix using R and fill in the blanks of the following table. (keep 3 decimal places)Explanation / Answer
I cannot give you the exact answers without the data you mentioned in the question. Let me try to help with the inbuilt data in R. After installing R in your computer,
#setting the directory where your dataset is placed
setwd("c:Userdocument")
mydata = read.CSV("datasetname.CSV")
As of now, we just pulled the data inside R environment. Let's get started with your questions,
a) pairs(~T+B+nu, data = mydata)
b)cor.data = mydata[,c(1,2,3)]
#If you have T, B, nu in the column 2nd, 4th and 5th respectively then rename it as mydata[,c(2,4,5)]
cor(cor.data, method = "pearson")
You will get the correlation table as output.
c) Correlation is a measure that ranges from -1 to 1. It says how one variable is explained by another variable. For instance, T and B correlation value is 0.89, then it is rephrased 89% change in T explained if there is a change in B. And if you see the scatter plot from answer a, there will an approximately 45degree linear formation. There are two types of correlation, positive correlation and negative correlation. If the correlation value is in negative sign then it is negative correlation and if it has positive sign then it is called as a positive correlation.
If correlation value lies between the range,
0 to 0.2 (or) 0 to -0.2 = poor correlation
0.2 to 0.4 (or) -0.2 to -0.4= weak correlation
0.4 to 0.6 (or) -0.4 to -0.6= considerably good correlation
0.6 to 0.8 (or) -0.6 to -0.8= good correlation
0.8 to 1 (or) -0.8 to -1 = very high correlation
d) cor(cor.data, method = "spearman")
e) cor(cor.data, method = "kendall")