Statistics and Probability

Introduction to Statistics

Statistics is the science of collecting, organizing, analyzing, and interpreting numerical data.

Formula:

Mean (μ) = Σx / N

Example:

Given the numbers: 5, 10, 15, 20, 25

Mean = (5 + 10 + 15 + 20 + 25) / 5 = 15

Visualization:

Measures of Dispersion

Measures of dispersion describe the spread of data points in a dataset.

Formula:

Variance (σ²) = Σ(x - μ)² / N

Example:

Given the numbers: 5, 10, 15, 20, 25

Variance = ((5-15)² + (10-15)² + (15-15)² + (20-15)² + (25-15)²) / 5 = 50

Visualization:

Coefficient of Variation

The coefficient of variation (CV) is a measure of relative variability.

Formula:

CV = (σ / μ) * 100%

Example:

Given the numbers: 5, 10, 15, 20, 25

CV = (7.071 / 15) * 100% ≈ 47.14%

Visualization:

Probability

Probability is the measure of the likelihood that an event will occur.

Formula:

P(A) = Number of favorable outcomes / Total number of outcomes

Example:

Probability of rolling a 6 on a fair die:

P(6) = 1 / 6 ≈ 0.1667

Visualization:

Algebra of Events

The algebra of events deals with the union, intersection, and complement of events in probability.

Formulas:

Union: P(A ∪ B) = P(A) + P(B) - P(A ∩ B)

Intersection: P(A ∩ B) = P(A) * P(B|A)

Complement: P(A') = 1 - P(A)

Example:

Given P(A) = 0.5, P(B) = 0.4, and P(A ∩ B) = 0.2:

P(A ∪ B) = 0.5 + 0.4 - 0.2 = 0.7

Visualization:

Addition Theorem of Probability

The addition theorem states that the probability of the union of two events is the sum of their individual probabilities minus the probability of their intersection.

Formula:

P(A ∪ B) = P(A) + P(B) - P(A ∩ B)

Example:

Given P(A) = 0.6, P(B) = 0.3, and P(A ∩ B) = 0.1:

P(A ∪ B) = 0.6 + 0.3 - 0.1 = 0.8

Visualization: