Statistical Concepts: Sampling and Probability
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Parameter vs. Statistic
Parameter: Population (μ, σ)
Statistic: Sample (x̄, s)
Rules of Probability
- 0 ≤ P(x) ≤ 1
- Σ P(x) = 1
- P(not x) = 1 - P(x)
Central Limit Theorem
As n (sample size) gets bigger, the sample distance will become approximately normal (shape).
Law of Large Numbers
As n gets bigger, the sample mean (x̄) will get closer to the population mean (μ) (number).
68-95-99.7 Rule
- 68% of data will lie within 1 standard deviation of the mean (σ + μ)
- 95% of data will lie within 2 standard deviations of the mean (2σ + μ)
- 99.7% of data will lie within 3 standard deviations of the mean (3σ + μ)
Sampling Types
Simple Random Sampling (SRS): Normal, random picking.
Systematic Sampling: Every kth sample.
Stratified Sampling: Groups are put together... Continue reading "Statistical Concepts: Sampling and Probability" »