Kazakhstan: 8/20
8/20 Autocorrelation Analysis
8/20: do draws have "memory"? Are results correlated between draws?
Autocorrelation shows whether the results of draw N are related to draw N-1, N-2, and beyond. If significant autocorrelation is detected, it's a valuable signal for forecasting. If not, it confirms the randomness of the "8/20" lottery.
Analysis based on 20 draws from to
Max lag:
Draw sums autocorrelation
Correlogram with 95% confidence intervals
20
Observations
1
Significant lags
±0.4383
95% confidence interval
Significant autocorrelation detected
Lags with significant correlation: 1 (ACF=-0.4469)
ACF(1) for All Numbers
Autocorrelation at lag 1 — quick overview of each number's "memory"
| Ball | ACF(1) | Status |
|---|---|---|
| 1 | -0.2833 | Normal |
| 2 | -0.4083 | Normal |
| 3 | -0.2530 | Normal |
| 4 | 0.0940 | Normal |
| 5 | 0.6104 | Significant |
| 6 | -0.3786 | Normal |
| 7 | -0.2000 | Normal |
| 8 | -0.1621 | Normal |
| 9 | -0.1500 | Normal |
| 10 | -0.4500 | Significant |
| 11 | 0.0399 | Normal |
| 12 | -0.0167 | Normal |
| 13 | -0.4885 | Significant |
| 14 | -0.1621 | Normal |
| 15 | 0.1709 | Normal |
| 16 | -0.3500 | Normal |
| 17 | -0.1621 | Normal |
| 18 | -0.0750 | Normal |
| 19 | -0.1918 | Normal |
| 20 | -0.1621 | Normal |
About Autocorrelation
Mathematical foundations
The autocorrelation function (ACF) measures the linear dependence between values of a time series separated by k steps (lag). In the context of a lottery: is the result of draw N related to the result of draw N-k?
ACF Formula
ACF(k) = Σ(xₜ - x̄)(xₜ₊ₖ - x̄) / [n · Var(x)]
ACF values range from -1 to +1. If |ACF| exceeds the confidence interval ±1.96/√n, the correlation is statistically significant.