Business Analytics: Bootstrap скачать в хорошем качестве

Business Analytics: Bootstrap

[00:00] The "Lonely Sample" Problem. In real-world analytics (e.g., churn, revenue), we often have only one finite, skewed dataset. We need to assess risk/uncertainty, not just the mean. [00:46] Limitations of Classical Statistics: Traditional methods often assume a Normal Distribution (Bell Curve). This fails when data is heavily skewed or chaotic, leading to inaccurate risk models. [01:36] The Solution (Resampling): Instead of assuming a theoretical shape, Bootstrapping treats the original sample as the population. We draw new samples with replacement to mimic natural variation. [04:11] The Sampling Distribution: By calculating the statistic (e.g., mean) for thousands of resamples, we generate a histogram that visualizes the actual uncertainty and standard error. [05:08] Asymmetric Confidence Intervals: Unlike the symmetric intervals of the Central Limit Theorem, Bootstrapping captures the true skew of the data. This is crucial for business cases like pricing or insurance, where "downside risk" is rarely symmetrical to "upside opportunity.