Visualize and analyze different statistical distributions with interactive charts
A data distribution shows how data values are spread across different ranges. Understanding distributions is crucial for data analysis, statistical modeling, and machine learning.
Normal (Gaussian) Distribution: The most common distribution, bell-shaped and symmetric. Many natural phenomena follow this pattern. Characterized by mean (μ) and standard deviation (σ).
Uniform Distribution: All values have equal probability. Examples: rolling a fair die, random number generation.
Exponential Distribution: Models time between events in a Poisson process. Common in reliability analysis and queuing theory.
Binomial Distribution: Discrete distribution for number of successes in n independent trials. Used in quality control, A/B testing.
Poisson Distribution: Models count of events in fixed time/space interval. Used for rare events, customer arrivals, defect counts.
Mean: Average value, center of mass of the distribution.
Median: Middle value when data is sorted. Less affected by outliers than mean.
Standard Deviation: Measure of spread/variability. Larger values = more spread out.
Range: Difference between maximum and minimum values.
Histogram: Shows frequency of values in bins. Great for seeing overall shape.
Box Plot: Shows median, quartiles, and outliers. Good for comparing distributions.
Density Curve: Smooth estimate of the probability density function.
CDF (Cumulative Distribution Function): Shows probability that a value is less than or equal to x.
• Feature Engineering: Understanding feature distributions helps in normalization and transformation
• Anomaly Detection: Identifying values far from the normal distribution
• Model Selection: Some models assume specific distributions (e.g., Naive Bayes assumes normal)
• Sampling: Generating synthetic data for testing and augmentation
• Statistical Testing: Many tests assume normal distribution (t-test, ANOVA)