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- Day 7: Spearman's Rank Correlation Coefficient

# Day 7: Spearman's Rank Correlation Coefficient

# Day 7: Spearman's Rank Correlation Coefficient

**Objective**

In this challenge, we practice calculating *Spearman's rank correlation coefficient*. Check out the Tutorial tab for learning materials!

**Task**

Given two -element data sets, and , calculate the value of Spearman's rank correlation coefficient.

**Input Format**

The first line contains an integer, , denoting the number of values in data sets and .

The second line contains space-separated real numbers (scaled to *at most* one decimal place) denoting data set .

The third line contains space-separated real numbers (scaled to *at most* one decimal place) denoting data set .

**Constraints**

- , where is the value of data set .
- , where is the value of data set .
- Data set contains unique values.
- Data set contains unique values.

**Output Format**

Print the value of the Spearman's rank correlation coefficient, rounded to a scale of decimal places.

**Sample Input**

```
10
10 9.8 8 7.8 7.7 1.7 6 5 1.4 2
200 44 32 24 22 17 15 12 8 4
```

**Sample Output**

```
0.903
```

**Explanation**

We know that data sets and both contain unique values, so the rank of each value in each data set is unique. Because of this property, we can use the following formula to calculate the value of Spearman's rank correlation coefficient:

Now, we find the value of the coefficient:

When rounded to a scale of three decimal places, we get as our final answer.