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Autokorelacja, quiz ACF i PACF

Czytaj wzorce ACF i PACF, odróżniaj skojarzenia opóźnione od skojarzeń częściowych i unikaj traktowania heurystyk wykresów jako automatycznych porządków modeli.

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Pytanie 1 z 8

What does the ACF at lag k describe?

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  1. What does the ACF at lag k describe?
    • Correlation between observations k time steps apart
    • Correlation after removing all predictor effects
    • The partial regression slope at every lag
    • The forecast variance at horizon k
  2. How does PACF at lag k differ from ACF?
    • It measures only seasonal frequencies
    • It accounts for linear effects of shorter lags
    • It is a correlation between two different series
    • It is always zero for nonstationary data
  3. Repeated ACF spikes at lags 12, 24 and 36 in monthly data suggest what?
    • That all seasonal effects are removed
    • A guaranteed AR order of 36
    • Possible annual seasonal dependence
    • A perfect moving-average fit
  4. Why can a slowly decaying ACF reflect trend rather than short-memory dependence?
    • A trend is identical to an MA term
    • The ACF omits all time information
    • Trend makes every lag exactly zero
    • Nonstationary level changes can induce broad persistence across lags
  5. Which textbook pattern is associated with a pure AR(p) process under ideal conditions?
    • ACF cuts off after p while PACF is always one
    • PACF cuts off after p while ACF decays
    • Both functions are exactly zero at lag one
    • Both cut off at the sample size
  6. Why are isolated spikes across many lags not definitive evidence?
    • Confidence bands equal exact causal tests
    • ACF values are always deterministic
    • Multiple approximate comparisons and finite-sample noise can create them
    • A lag plot cannot include uncertainty
  7. Which ACF is useful after fitting an ARIMA candidate?
    • The seasonal ACF after labels are removed
    • Only the original series ACF because residuals are never diagnostic
    • The predictor ACF to estimate accuracy
    • The residual ACF to inspect remaining serial dependence
  8. How should ACF/PACF plots guide model selection?
    • Suggest candidate orders that are then validated with diagnostics and forecasts
    • Set exact p and q without further checks
    • Prove stationarity whenever confidence bands are crossed
    • Replace a time-ordered test set