Task 1

data <- read.table("./LocationTest.csv", 
                     header=TRUE, 
                     sep=";", 
                     dec=",")

head(data)

#parametric test
t.test(data$relativehumidity_2m,
       mu = 0.9,
       alternative = "two.sided")

## 
##  One Sample t-test
## 
## data:  data$relativehumidity_2m
## t = 886.56, df = 43799, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.9
## 95 percent confidence interval:
##  72.13092 72.44657
## sample estimates:
## mean of x 
##  72.28874

# Wilcoxon-Mann-Whitney test
wilcox.test(data$
winddirection_10m, data$
winddirection_100m,
            paired=TRUE,
            correct= FALSE,
            exact=FALSE,
            alternative="two.sided")

## 
##  Wilcoxon signed rank test
## 
## data:  data$winddirection_10m and data$winddirection_100m
## V = 123099012, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0

#parametric tests are better in this particular case, because they give more accurate
#high t-statistics (886.56) with low p-values ​​(< 2.2e-16), which means that there is a significant difference between the sample and the population value (0.9). Also, the 95 percent confidence interval does not include the population value, which supports the rejection of the null hypothesis
#results
#data is from this location: https://www.kaggle.com/datasets/mubashirrahim/wind-power-generation-data-fore#casting?resource=download&select=Location1.csv

#A Wilcoxon signed-rank test was performed to determine if there was a statistically significant difference between the wind direction at the 10-meter level (data$winddirection_10m) and the wind direction at the 100-meter level (data$winddirection_100m). Here are the key takeaways from the test results:

#Statistical results:

#V (Wilcoxon rank-sum): 123099012
#p-value: < 2.2e-16 (very small p-value, indicating statistically significant results)
#Alternative hypothesis:

#An alternative hypothesis suggests that the actual change in wind direction is not zero.
#These results suggest that there is a statistically significant difference between the wind direction at different altitudes. A p-value of less than 0.05 (or another significance threshold of your choice) indicates that you can reject the null hypothesis of no change in wind direction.