Exploratory Statistics: Understanding Populations and Samples

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Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

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Covers broadcasting, operations, comparisons, and numpy constants like pi, e, and infinity.

Explores quantiles, sampling, and histogram density for understanding distributions and constructing confidence intervals.

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Covers moments computation and probability distribution transformations.

Covers hypothesis testing, confidence intervals, and their applications in statistics.

Explores funding an idea, big data analysis, and statistics basics with insights on avoiding common pitfalls.

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