docs: fix typos (#1283)

* docs: fix typos

* fix

Maths/BinomialCoefficient.js

@@ -7,7 +7,7 @@
 
 /**
  * @function findBinomialCoefficient
- * @description -> this function returns bonimial coefficient
+ * @description -> this function returns binomial coefficient
  * of two numbers n & k given by n!/((n-k)!k!)
  * @param {number} n
  * @param {number} k

Maths/CheckKishnamurthyNumber.js

@@ -37,7 +37,7 @@ const CheckKishnamurthyNumber = (number) => {
     sumOfAllDigitFactorial += factorial(lastDigit)
     newNumber = Math.floor(newNumber / 10)
   }
-  // if the sumOftheFactorial is equal to the given number it means the number is a Krishnamurthy number.
+  // if the sumOfAllDigitFactorial is equal to the given number it means the number is a Krishnamurthy number.
   return sumOfAllDigitFactorial === number
 }
 

Maths/ExtendedEuclideanGCD.js

@@ -1,7 +1,7 @@
 /**
  * Problem statement and explanation: https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm
  *
- * This algorithm plays an important role for modular arithmetic, and by extension for cyptography algorithms
+ * This algorithm plays an important role for modular arithmetic, and by extension for cryptography algorithms
  *
  * Basic explanation:
  * The Extended Euclidean algorithm is a modification of the standard Euclidean GCD algorithm.

Maths/FermatPrimalityTest.js

@@ -21,7 +21,7 @@
  *     1 / 2^50 = 8.8 * 10^-16 (a pretty small number)
  *
  * For comparison, the probability of a cosmic ray causing an error to your
- * infalible program is around 1.4 * 10^-15. An order of magnitude below!
+ * infallible program is around 1.4 * 10^-15. An order of magnitude below!
  *
  * But because nothing is perfect, there's a major flaw to this algorithm, and
  * the cause are the so called Carmichael Numbers. These are composite numbers n

Maths/test/MeanAbsoluteDeviation.test.js

@@ -9,7 +9,7 @@ describe('tests for mean absolute deviation', () => {
     expect(() => meanAbsoluteDeviation('fgh')).toThrow()
   })
 
-  it('should return the mean absolute devition of an array of numbers', () => {
+  it('should return the mean absolute deviation of an array of numbers', () => {
     const meanAbDev = meanAbsoluteDeviation([2, 34, 5, 0, -2])
     expect(meanAbDev).toBe(10.479999999999999)
   })