Cloud Seeding

Cloud seeding is the processes of spraying clouds with a chemical solution to trigger the formation of raindrops. The following data is based on an experiment in Florida from the 1970s. On 52 separate days, target clouds were identified. On half of the days (randomly selected), a plane flew through the clouds spraying silver iodide solution. Radar was then used to measure the volume of rainfall produced (in acre-feet).

cloud = read.csv("http://people.hsc.edu/faculty-staff/blins/classes/spring17/math222/data/CloudSeeding.csv")
#head(cloud)
tre = subset(cloud,treatment=='seeded')
con = subset(cloud,treatment=='unseeded')
summary(con$rainfall)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.00   24.82   44.20  164.60  159.20 1203.00

summary(tre$rainfall)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    4.10   98.13  221.60  442.00  406.00 2746.00

From the summaries, you can see that the differences in mean rainfall are impressive. The mean increased from 164.6 in the control clouds to 446.0 in the treatment clouds. Unfortunately, this data is extremely right skewed with several large outliers in each group.

boxplot(tre$rainfall,con$rainfall,horizontal=T,col='gray',names=c('Treatment','Control'))

hist(con$rainfall,col='gray',main='Control Group Rainfall')

hist(tre$rainfall,col='gray',main='Treatment Group Rainfall')

You might argue that a 2-sample t-test is appropriate since the total sample size is 52. There are better options, however.

t.test(con$rainfall,tre$rainfall)

## 
##  Welch Two Sample t-test
## 
## data:  con$rainfall and tre$rainfall
## t = -1.9982, df = 33.855, p-value = 0.05377
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -559.556603    4.764295
## sample estimates:
## mean of x mean of y 
##  164.5885  441.9846