Interactive Visualizations for Probability & Statistics
The simplest distribution.
Visualize the sequence of independent Bernoulli trials and analyze the outcomes over time.
Explore the probability mass function and observe how parameters n and p affect the distribution shape.
Understand the efficiency of group testing procedures and their applications in statistical screening.
Model the probability of a given number of events occurring in a fixed interval of time or space.
Simulate and observe the arrival of events over time according to the Poisson process model.
Examine the Gaussian curve and the effects of changing the mean and standard deviation.
Analyze this continuous distribution of a random variable whose logarithm is normally distributed.
Investigate the continuous probability distribution defined by shape and scale parameters.
Visualize the Bivariate Normal Distribution and understand correlation coefficients in 2D space.
Markov's Inequality and Chebyshev's Inequality are important inequalities for proving the Law of Large Numbers.
The average of the results obtained from larges numbers of trials will converge to the expected value.
The sampling distribution of the sample mean will approximate a normal distribution as the sample size becomes large.
The fundamental reason we can use the 'Sample Mean' to infer the 'Population Mean'!"
Try to understand the essence of confidence intervals by simulation
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