In statistics, sample variance (s²) plays a crucial role in understanding how spread out data points are within a sample. It reflects the average squared distance of each data point in the sample from the sample mean (x̄). A higher variance indicates a wider spread of data points, while a lower variance suggests the data points are clustered closer to the mean.
Sample variance provides valuable insights into data variability within a sample. This information is beneficial for:
It’s important to distinguish between sample variance (s²) and population variance (σ²). Population variance reflects the variability within the entire population, while sample variance estimates the population variance using data from a subset (sample) of the population. Obtaining data for the entire population can be impractical or impossible in many real-world scenarios. Sample variance plays a significant role when complete population data isn’t available.
Here’s the mathematical formula for calculating sample variance:
s² = Σ(xi - x̄)² / (n - 1)
Explanation of the Formula:
Steps for Calculating :
Consider a sample data set representing the test scores of 5 students: {85, 72, 90, 88, 65}.
x̄ = (85 + 72 + 90 + 88 + 65) / 5
x̄ = 400 / 5
x̄ = 80
Individual | Value (xi) | Deviation (xi – x̄) | Squared Deviation (xi – x̄)² |
---|---|---|---|
1 | 85 | 5 | 25 |
2 | 72 | -8 | 64 |
3 | 90 | 10 | 100 |
4 | 88 | 8 | 64 |
5 | 65 | -15 | 225 |
25 + 64 + 100 + 64 + 225 = 478
s² = 478 / (5 - 1) s² = 478 / 4 s² = 119.5
Therefore, the sample variance (s²) for this example is 119.5, indicating a moderate spread of test scores within this sample.
Here’s a Python code example demonstrating how to calculate sample variance:
sample_data = [85, 72, 90, 88, 65]
sample_mean = sum(sample_data) / len(sample_data)
squared_deviations = [(x - sample_mean) ** 2 for x in sample_data]
sample_variance = sum(squared_deviations) / (len(sample_data) - 1)
print("Sample Mean:", sample_mean)
print("Sample Variance:", sample_variance)
Explanation:
sample_data
containing the test scores (replace with your actual data).sample_mean
is calculated by summing the elements in sample_data
and dividing by the total number of elements (using len(sample_data)
).squared_deviations = [(x - sample_mean) ** 2 for x in sample_data]
iterates through each element x
in sample_data
. It subtracts the sample_mean
from each element, squares the result using the exponent **2
, and creates a new list containing the squared deviations from the mean for all elements.sample_variance
is calculated by summing the squared deviations in squared_deviations
and dividing by the total number of elements in the sample minus one (len(sample_data) - 1
). This adjustment (n-1) is used to obtain an unbiased estimate of the population variance.sample_mean
and sample_variance
are printed.Sample Mean: 80.0
Sample Variance: 119.5
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Reference: Variance | Brilliant Math & Science Wiki
Hi, I am Vishal Jaiswal, I have about a decade of experience of working in MNCs like Genpact, Savista, Ingenious. Currently i am working in EXL as a senior quality analyst. Using my writing skills i want to share the experience i have gained and help as many as i can.
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