MCQs

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Overall MCQs for Reference

What is the primary purpose of the Exponentially Weighted Moving Average (EWMA) in time series analysis?
a. To model volatility directly
b. To detect changes in the mean
c. To measure variance of observations
d. To filter high-frequency signals
Answer: b. To detect changes in the meansciencedirect+1
Which parameter in EWMA determines the relevance of the most recent observation?
a. Beta
b. Lambda
c. Alpha
d. Gamma
Answer: c. Alphacorporatefinanceinstitute+1
How does EWMA treat older observations in comparison to recent ones?
a. Older observations are weighted higher
b. Recent and older observations are equally weighted
c. Older observations receive exponentially less weight
d. Recent observations are ignored
Answer: c. Older observations receive exponentially less weightwikipedia+1
In the calculation of EWMA, what happens when the alpha value increases?
a. Smoothing increases
b. Recent observations are weighted less
c. EWMA follows the time series more closely
d. Variance is maximized
Answer: c. EWMA follows the time series more closelytowardsdatascience+1
What does EWMA specifically monitor in statistical process control?
a. Median
b. Mode
c. Mean
d. Range
Answer: c. Meansixsigmastudyguide+1
How is EWMA helpful in process control charts?
a. It ignores historical data
b. It monitors variables using their entire history
c. It only detects large shifts
d. It is applied for non-continuous data
Answer: b. It monitors variables using their entire historyasq+1
For what type of data is EWMA especially suitable?
a. Discrete data
b. Continuous data
c. Categorical data
d. Binary data
Answer: b. Continuous datasixsigmastudyguide+1
Compared to Shewhart charts, what shifts does EWMA detect better?
a. Large shifts
b. Small shifts
c. No shifts
d. Random noise
Answer: b. Small shiftspubs.acs+1
What property of EWMA allows it to be easily updated with new data points?
a. Additive property
b. Recursive property
c. Symmetric property
d. Randomized property
Answer: b. Recursive propertystanford+1
Which equation does the simplest form of exponential smoothing use for output?
a.
b.
c.
d.
Answer: b. corporatefinanceinstitute+2
What is the advantage of EWMA over simple moving average?
a. Assigns equal weight to all observations
b. Allows monitoring mean with exponentially decaying weights
c. Ignores historical trends
d. Only for large datasets
Answer: b. Allows monitoring mean with exponentially decaying weightstowardsdatascience+2
In EWMA, what does a low alpha value indicate?
a. More importance on recent data
b. More smoothing and importance on historical data
c. High variance in output
d. More randomness
Answer: b. More smoothing and importance on historical datacorporatefinanceinstitute+1
What is another name often used for EWMA in literature?
a. Linear Moving Average
b. Weighted Rolling Mean
c. Exponential Moving Average
d. Median Filter
Answer: c. Exponential Moving Averagetowardsdatascience+1
Which machine learning models are sometimes combined with EWMA for control charting?
a. Random Forest and K-NN
b. Decision Tree and PCA
c. Logistic Regression and SVM
d. Naive Bayes and LDA
Answer: a. Random Forest and K-NNpmc.ncbi.nlm.nih+2
What is the main limitation of EWMA when used for detecting process shifts?
a. Slow to detect small shifts
b. Poor for mean estimation
c. Slower detection of large shifts than Shewhart charts
d. Cannot work with continuous data
Answer: c. Slower detection of large shifts than Shewhart chartspubs.acs+1
In financial modeling, what is EWMA commonly used for?
a. Direct price prediction
b. Modeling volatility
c. Data encryption
d. Outlier removal
Answer: b. Modeling volatilityeodhd+1
Which of the following is a common alpha value range used in EWMA applications?
a. 0 to 0.1
b. 0.1 to 0.3
c. 0.3 to 1.0
d. 0.01 to 1.0
Answer: d. 0.01 to 1.0towardsdatascience+1
In EWMA, how is the control limit related to sample data?
a. Random assignment
b. Fixed at zero
c. Calculated using standard deviation and mean
d. Ignored entirely
Answer: c. Calculated using standard deviation and meansixsigmastudyguide+1
What is the role of the forgetting factor (beta) in EWMA?
a. Determines the length of the time window
b. Sets the variance
c. Controls the rate at which historical data is discounted
d. Establishes the mean
Answer: c. Controls the rate at which historical data is discountedwikipedia+1
What type of smoothing does EWMA use for time series data?
a. Polynomial
b. Linear
c. Exponential
d. Quadratic
Answer: c. Exponentialwikipedia+1
Which domain frequently uses EWMA for anomaly detection?
a. Healthcare
b. Manufacturing
c. Finance
d. All of the above
Answer: d. All of the abovesciencedirect+2
What makes EWMA particularly advantageous for noisy data?
a. Ignores all noise
b. Smooths out uncontrollable noise
c. Maximizes noise impact
d. Randomizes output
Answer: b. Smooths out uncontrollable noisesixsigmastudyguide+1
Which step does EWMA calculation not require?
a. Compute weighted sum
b. Apply exponential weights
c. Dividing observations by variance
d. Initialize first observation
Answer: c. Dividing observations by variancetowardsdatascience+1
What statistical rule allows the use of normal distribution for EWMA plotted points?
a. Law of Large Numbers
b. Central Limit Theorem
c. Bayes Rule
d. Chebyshev’s Inequality
Answer: b. Central Limit Theoremsixsigmastudyguide
Which is a valid synonym for the smoothing parameter in EWMA?
a. Alpha
b. Gamma
c. Kappa
d. Delta
Answer: a. Alphacorporatefinanceinstitute+2
What is a recommended EWMA usage for sample sizes of 1?
a. Use Shewhart chart
b. Use Individual X chart
c. Use median filter
d. Discard small samples
Answer: b. Use Individual X chartsixsigmastudyguide
How does EWMA mathematical formulation differ for multivariate time series?
a. Uses median
b. Applies vector weights recursively
c. Ignores dependencies
d. Only uses univariate averages
Answer: b. Applies vector weights recursivelystanford
Which practical application demonstrated superior EWMA performance when paired with XGBoost compared to SVR?
a. Image classification
b. Air quality data monitoring
c. Fraud detection
d. Handwriting recognition
Answer: b. Air quality data monitoringpmc.ncbi.nlm.nih
What concept is often optimized in machine learning EWMA enhancements?
a. Loss minimization
b. Random selection
c. Data encryption
d. Standard deviation maximization
Answer: a. Loss minimizationstanford
Which time series technique is a predecessor to EWMA?
a. Linear Smoothing
b. Poisson’s recursive window
c. Kolmogorov’s random walk
d. Mann-Kendall test
Answer: b. Poisson’s recursive windowwikipedia
What does EWMA enable in monitoring patient care quality in healthcare?
a. Maximizing patient visits
b. Enhancing adaptive process control
c. Excluding rare events
d. Reducing mean accuracy
Answer: b. Enhancing adaptive process controlonlinelibrary.wiley
EWMA assigns exponentially-decaying weights. What effect does this have on outlier influence?
a. Amplifies outliers
b. Dampens outlier influence
c. Has no effect
d. Eliminates all outliers
Answer: b. Dampens outlier influencewikipedia+1
Which technique is best when you want to measure process variability instead of the mean?
a. EWMA
b. S-chart or R-chart
c. Exponential filter
d. Individual X chart
Answer: b. S-chart or R-chartsixsigmastudyguide
In what scenario should EWMA not be used?
a. When detection of large shifts is prioritized
b. For continuous data
c. To measure the mean
d. For time series forecasting
Answer: a. When detection of large shifts is prioritizedpubs.acs+1
What is a common use-case of EWMA in machine learning pipelines?
a. Feature scaling
b. Online parameter estimation
c. Outlier removal
d. Data encryption
Answer: b. Online parameter estimationnature+1
Which adjustment can automate selection of alpha in EWMA with pandas?
a. Halflife
b. Gamma
c. Activation function
d. Control limit
Answer: a. Halflifetowardsdatascience
EWMA can be used with which algorithm for adaptive control charts?
a. Logistic Regression
b. K-Means Clustering
c. Deep Learning Neural Nets
d. Support Vector Machines
Answer: a. Logistic Regressionpmc.ncbi.nlm.nih+1
EWMA is an example of which general family of algorithms?
a. ARMA
b. Smoothing
c. Filtering
d. All of the above
Answer: d. All of the abovesciencedirect+1
What restriction applies to EWMA when estimating mean using subgroup sizes?
a. Subgroup must be less than 2
b. Subgroup should be greater than 1
c. No subgroup allowed
d. Only applies for centroids
Answer: b. Subgroup should be greater than 1sixsigmastudyguide
In EWMA, what is the effect of choosing a large alpha close to 1?
a. Ignores historical trends
b. Makes output almost identical to most recent observation
c. Maximizes variance
d. Smooths data even more
Answer: b. Makes output almost identical to most recent observationcorporatefinanceinstitute+1
Which equation relates the forgetting factor beta to half-life in EWMA?
a.
b.
c.
d.
Answer: b. stanford
Which adaptive EWMA control chart approach was described for healthcare quality frameworks?
a. Second order ARMA
b. SVR-EWMA
c. Logistic regression
d. S-chart
Answer: b. SVR-EWMAonlinelibrary.wiley+1
In which scenario will EWMA outperform Shewhart charts?
a. When monitoring large process shifts
b. For binary classification
c. When small process shifts are more important
d. With non-time series data
Answer: c. When small process shifts are more importantpubs.acs+1
Which regularizer is commonly used in EWMA logistic regression?
a. L1 norm
b. L2 norm
c. Dropout
d. Max pooling
Answer: b. L2 normstanford
What does the EWMA chart plot for a time series?
a. Median of values
b. Exponentially weighted average
c. Variance
d. Random samples
Answer: b. Exponentially weighted averagepubs.acs+1
EWMA is commonly implemented for time series analysis in which data science library?
a. NumPy
b. Pandas
c. Matplotlib
d. Scikit-learn
Answer: b. Pandaseodhd+1
EWMA can help highlight which type of patterns in noisy time series data?
a. Random patterns
b. Periodic patterns
c. Persistent shifts
d. Any pattern without restriction
Answer: c. Persistent shiftswikipedia+1
Which concept is key for EWMA when using it for volatility estimation?
a. Variance estimation
b. Autocorrelation
c. Smoothing factor
d. Outlier detection
Answer: c. Smoothing factoreodhd+1
The recursive formula for EWMA is best described as:
a. Weighted sum minus old mean
b. New mean plus difference
c. Current EWMA based on previous EWMA and new observation
d. EWMA ignores the earliest observation
Answer: c. Current EWMA based on previous EWMA and new observationcorporatefinanceinstitute+2
Which algorithmic enhancement improves EWMA control chart performance in machine learning?
a. Feature normalization
b. Adaptive smoothing parameter
c. Min-max scaling
d. Standard deviation modification
Answer: b. Adaptive smoothing parameternature+2

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