Animated Solution for Mathematics - Statistics: If the variance of 10 natural numbers 1,1,1,…,1,k is less than 10, then the maximum possible value of k is
Enter Numerical Value:
Visualized Solution
Understanding the Data Set
Given data set: 1,1,1,1,1,1,1,1,1,k
Total number of observations (n): 10
Constraint: Variance (σ2) <10
Goal: Find the maximum integral value of k.
The Variance Formula
The formula for Variance (σ2) is:
σ2=n∑xi2−(n∑xi)2
Where xi are the individual observations and n is the total count.
Calculating the Sums
Sum of observations (∑xi):
∑xi=9(1)+k=9+k
Sum of squares of observations (∑xi2):
∑xi2=9(12)+k2=9+k2
Setting up the Inequality
Substitute ∑xi=9+k, ∑xi2=9+k2, and n=10 into σ2<10:
109+k2−(109+k)2<10
Clearing the Denominators
Multiply the entire inequality by 100 to clear denominators:
10(9+k2)−(9+k)2<1000
Expanding the Terms
Expand the brackets:
90+10k2−(81+18k+k2)<1000
90+10k2−81−18k−k2<1000
Simplifying the Quadratic
Combine like terms:
(10k2−k2)−18k+(90−81)<1000
9k2−18k+9<1000
Factoring the Expression
Factor out 9 and recognize the perfect square:
9(k2−2k+1)<1000
9(k−1)2<1000
Solving for k
Divide by 9 and take the square root:
(k−1)2<91000
k−1<31010≈10.54
Final Conclusion
k<11.54
Since k is a natural number (k∈N):
Maximum possible value of k=11
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The Sigma Insight: Variance and Standard Deviation
Solution Diagram
Analyzing the Setup
Imagine you are standing on a number line. You have a crowd of nine people standing exactly at the position 1. They are perfectly clustered, a static, unmoving group.
Then, you have one more person, a mysterious outlier named k, who is wandering somewhere further down the line. Your goal is to keep the 'spread' of this group—the variance—under a strict limit of 10.
This is the essence of our problem. We are not just crunching numbers; we are managing the influence of an outlier on the collective behavior of a dataset.
The Mathematical Arsenal
To measure this spread, we reach for the most powerful tool in our statistical toolkit: the computational formula for variance:
σ2=n∑xi2−(xˉ)2
Why this formula? Because it is elegant. It separates the sum of squares from the square of the sum, allowing us to handle our cluster of ones without calculating the mean for every single point.
We know our total count n is 10. Our data set is 1,1,1,1,1,1,1,1,1,k.
The sum of these observations is simple: ∑xi=9(1)+k=9+k. The sum of their squares is equally straightforward: ∑xi2=9(12)+k2=9+k2.
The Algebraic Dance
Substituting our values into the variance formula, we get:
σ2=109+k2−(109+k)2
The problem demands that this variance be strictly less than 10. So, we write:
109+k2−(109+k)2<10
This looks intimidating with the fractions, but do not panic. We can clear the denominators by multiplying the entire inequality by 100. This transforms our expression into:
10(9+k2)−(9+k)2<1000
Now, we expand. The first term becomes 90+10k2. The second term, using the identity (a+b)2=a2+2ab+b2, becomes (81+18k+k2).
Be careful with that negative sign! It distributes to every term inside, giving us:
90+10k2−81−18k−k2<1000
The Final Revelation
Combining like terms is where the beauty of the problem reveals itself. 10k2−k2 gives us 9k2. The linear term is −18k, and the constants 90−81 leave us with 9.
Our inequality is now:
9k2−18k+9<1000
Notice the common factor of 9? We factor it out: 9(k2−2k+1)<1000. That expression inside the parenthesis is a perfect square: (k−1)2.
So, we have:
9(k−1)2<1000
Dividing by 9, we get (k−1)2<91000, which is approximately 111.11. Taking the square root of both sides, we find k−1<111.11≈10.54.
This means k<11.54. Since k must be a natural number, the largest integer satisfying this condition is 11. We have successfully constrained our outlier.