A) It requires more computing power than the median.
B) It is susceptible to rare events or rapid shocks.
C) It cannot handle time series data.
D) It is unable to process large datasets.

A) When fluctuations are normally distributed.
B) When the data contains many anomalies.
C) When fluctuations are Laplace distributed.
D) When the time series is purely random.

A) It does not place high probability on very large deviations.
B) It assumes the mean is always zero.
C) It places too much weight on the middle value.
D) It ignores the variance of the data.

A) Uniform distribution.
B) Poisson distribution.
C) Laplace distribution.
D) Normal distribution.

A) By calculating the arithmetic mean.
B) By sorting the entire time series repeatedly.
C) By updating an indexable skiplist.
D) By using a regression model.

A) Weighted average model.
B) Median filter.
C) Normal distribution filter.
D) Moving average regression.

A) Image signal processing.
B) Quantum physics.
C) Financial forecasting.
D) Weather prediction.

A) Places lower probability on rare events.
B) Makes the data perfectly linear.
C) Eliminates all variance.
D) Places higher probability on rare events.

A) The median of the distribution.
B) Random noise.
C) Parameters to be estimated.
D) Fixed constants.

A) Due to their similar names.
B) Because they use the exact same calculation.
C) Because they are identical methods.
D) Because they have the same statistical optimality.

A) Constant variance
B) Fixed summation
C) Rolling average
D) Static median

A) Differentiation
B) Integration
C) Convolution
D) Logarithmic scaling

A) Because it moves like a boxcar
B) Because the boxcar function outlines its coefficients
C) Because it uses square root calculations
D) Because it was used in the train industry

A) 1905
B) 1799
C) 1865
D) 1833

A) The Bank of England
B) The London Stock Exchange
C) The University of Cambridge
D) The Royal Statistical Society

A) FIFO / circular buffer
B) Binary tree
C) Hash table
D) Stack

A) Gaussian kernel
B) Polynomial decay
C) Sinc-in-frequency
D) Cosine oscillation

A) ε
B) x - ε
C) ε/2
D) 2ε

A) (x1 + ... + xn) / (n+1)
B) (x1 + ... + xn) * n
C) (x1 + ... + xn) / n
D) (x1 * ... * xn) / n

A) (xn+1 + n * CAn) / (n + 1)
B) (xn+1 + n) * CAn / n
C) (xn+1 + CAn) / n
D) (xn+1 * n) + CAn / n+1

A) Increasing image resolution
B) Removing pixelization
C) Color correction
D) Encrypting image files

A) Arithmetical progression
B) Uniform distribution
C) Exponential decay
D) Logarithmic progression

A) Leaky integrator
B) Fast filter
C) Triangle integrator
D) Volume filter

A) John Stuart Hunter
B) Spencer
C) Isaac Newton
D) Alan Turing

A) MEMA
B) MEWMA
C) Linear EWMA
D) Multivariate WMA

A) Fast Fourier Transform
B) Matrix inversion
C) Linear interpolation
D) Recursive summation