Module 3 LIS 4273

 > set1 <- c(10,2,3,2,4,2,5)

> set2 <- c(20,12,13,12,14,12,15)
> # Set 1
> mean(set1,trim = 0,na.rm = FALSE)
[1] 4
> median(set1,trim = 0,na.rm = FALSE)
[1] 3
> mode = function(){
+   return(names(sort(-table(set1)))[1])
+ }
> mode()
[1] "2"
> sd(set1,na.rm = FALSE)
[1] 2.886751
> range(set1,trim = 0,na.rm = FALSE)
[1]  0 10
> quantile(set1,trim = 0,na.rm = FALSE)
  0%  25%  50%  75% 100% 
 2.0  2.0  3.0  4.5 10.0 
> var(set1)
[1] 8.333333
> sd(set1,na.rm = TRUE)/mean(set1,na.rm = TRUE)*100
[1] 72.16878
> summary(set1)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    2.0     2.0     3.0     4.0     4.5    10.0 
> # Set 2
> mean(set2,trim = 0,na.rm = FALSE)
[1] 14
> median(set2,trim = 0,na.rm = FALSE)
[1] 13
> mode = function(){
+   return(names(sort(-table(set2)))[1])
+ }
> mode()
[1] "12"
> sd(set2,na.rm = FALSE)
[1] 2.886751
> range(set2,trim = 0,na.rm = FALSE)
[1]  0 20
> quantile(set2,trim = 0,na.rm = FALSE)
  0%  25%  50%  75% 100% 
12.0 12.0 13.0 14.5 20.0 
> var(set2)
[1] 8.333333
> sd(set2,na.rm = TRUE)/mean(set2,na.rm = TRUE)*100
[1] 20.61965
> summary(set2)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   12.0    12.0    13.0    14.0    14.5    20.0 

The primary differences between these two sets of data lies in their measures of central tendency. Set #2 has higher values for mean (14 vs. 4), median (13 vs. 3), and mode (12 vs. 2) than set #1. That indicates that set #2 data is larger. The measure of variation is very similar for both sets. Both have a range of 8, a variance of around 8.33, and a standard deviation of around 2.89. This suggests that the spread of data points around their means are very similar for both sets.

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