#1Create three variables x1, x2 and x3 by using for each the rnorm() function. Combine the three variables in a data frame df1 by using the function data.frame().
#2Study lab 6 handout for correlation. Are the three variables x1, x2, x3 in the df1, correlated to each other? If yes, why, if no, why? Use the function cor(df1) in estimating correlations to show the correlations for combinations, x1 with x2, x1 with x3 and x2 with x3.
#3 Perform cor.test() in pairs between x1 with x2, x1 with x3 and x2 with x3 and comment on the significance results.
#4 Use the data “energy” from the library ISwR of Dalgaard book and compare energy expenditure (expand) for the two groups of stature, a factor variable with two groups lean and obese. Compare the t.test p-value with the threshold of 0.05. Is the result significant? Comment on the results.
#5 Compare p-values of the results from point 4 (t.test()) with the results from wilxoc.test().
#6 Using the function hist() check the distribution of the variable “expand” from the “energy” data.
#7 Create a table from data energy on the variable stature. Report if there are statistical differences among the frequencies of lean individuals versus those obese. Use the following commands to create the table and them to test statistically if there are differences among the proportions of the two statures:
table(energy$stature) prop.test(table(energy$stature))
#8 Copy and paste the data provided into a text file and save it as loans.txt. Study handout 7 to read the file into an R object, by starting with library(readr) or you can call it from the library(tidyverse). Use the function read_delim() to read the file into an R object named loans. B. Having created the object loans, report the loan (dollars_outstanding) per individual per year. I would suggest first you create a variable loans$out_doll <- loans$dollars_outstanding * 1000000000. Also create another variable loans$recip <- loans$recipients * 1000000. Then divide loans$out_doll / loans$recip and assign the values to loans$loansPerPerson. The resulting new column will be in dollars per person. Comment on what you see as a trend for the loans in dollars per person (loansPerPerson) in the years.
library(tidyverse)
year dollars_outstanding recipients
|
2007 |
516.00 |
28.3 |
|
2008 |
577.00 |
29.9 |
|
2009 |
657.00 |
32.1 |
|
2010 |
749.80 |
34.3 |
|
2011 |
848.20 |
36.5 |
|
2012 |
948.20 |
38.3 |
|
2013 |
1040.20 |
39.6 |
|
2014 |
1129.80 |
40.7 |
|
2015 |
1212.40 |
41.6 |
|
2016 |
1292.20 |
42.3 |
|
2017 |
1366.90 |
42.6 |
|
2018 |
1439.20 |
42.9 |
|
2019 |
1510.30 |
42.9 |
|
2020 |
1515.00 |
42.8 |
#9 Perform linear regression analysis of dollars_outstanding (loans$out_doll) by recipients (loans$recip).
#10 If you perform/ run plot(obj1), R will provide 4 graphs which will tell the story if the data analyzed were linear or not. Run also the following plot: plot(loans$recip
, loans$out_doll).
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