MCQs

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Overall MCQs for Reference

In which forecasting technique are subjective inputs from various sources analyzed?
a. Judgemental forecast
b. Time series forecast
c. Associative model
d. All of the above
Answer: a. Judgemental forecastscribd+1
Which technique analyzes data from past experiences for forecasting?
a. Judgemental forecast
b. Time series forecast
c. Associative model
d. All of the above
Answer: b. Time series forecastscholarexpress+1
The naive method in time series forecasting predicts next period’s demand as:
a. The average of last two periods
b. The demand two periods ago
c. The previous period’s demand
d. The sum of previous two periods
Answer: c. The previous period’s demandstudocu+1
Which method discounts older data but assigns higher weights to more recent data?
a. Simple moving average
b. Weighted moving average
c. Naive method
d. Exponential smoothing
Answer: b. Weighted moving averagetestbook+1
In exponential smoothing, the smoothing constant is denoted by:
a. β
b. α
c. γ
d. δ
Answer: b. αtestbook+1
The formula for exponential smoothing is:
a. Ft+1 = Ft + α(Dt – Ft)
b. Ft = Dt-1 + α
c. Ft+1 = αDt + (1-α)Ft
d. Ft = Ft-1 + β(Dt-1 – Ft-1)
Answer: a. Ft+1 = Ft + α(Dt – Ft)studocu+1
Which forecasting method treats last k observations equally and ignores previous ones?
a. Weighted moving average
b. Simple moving average
c. Exponential smoothing
d. Naive forecast
Answer: b. Simple moving averagetestbook+1
Which of the following is not a forecasting technique?
a. Judgemental
b. Time series
c. Time horizon
d. Associative
Answer: c. Time horizonscholarexpress
What is the primary goal of time series forecasting?
a. To collect historical data
b. To predict future values based on past observations
c. To clean data before analysis
d. To understand the underlying distribution
Answer: b. To predict future values based on past observationsaionlinecourse
Which method combines both qualitative and quantitative forecasting?
a. Moving average
b. Composite approach
c. Delphi method
d. Associative model
Answer: b. Composite approachstudocu
The Delphi method relies mainly on:
a. Expert opinion
b. Time series data
c. Regression models
d. Trend analysis
Answer: a. Expert opinionscholarexpress+1
What is cycle in time series forecasting?
a. Seasonal fluctuation
b. Long-term pattern repeating at irregular intervals
c. Random error
d. Short-term anomaly
Answer: b. Long-term pattern repeating at irregular intervalsitfeature+1
Which of the following is NOT true about forecasts?
a. Forecasts are rarely perfect
b. Forecast group accuracy > individual item accuracy
c. Underlying systems are always stable
d. Short range forecasts are more accurate than long range ones
Answer: c. Underlying systems are always stablesanfoundry+1
Which is a qualitative data forecasting model?
a. Time series
b. Naive model
c. Sales force composite
d. Exponential smoothing
Answer: c. Sales force compositestudocu
In time series analysis, what is “random variation”?
a. A predictable seasonal pattern
b. Sudden unexpected changes with no discernible pattern
c. Upward trend over years
d. Impact of external economic factors
Answer: b. Sudden unexpected changes with no discernible patternitfeature+1
Mean Squared Error (MSE) in forecasting measures:
a. Variability of past data
b. Overall error of a forecasting model
c. Node strength in regression trees
d. Frequency of error in time series
Answer: b. Overall error of a forecasting modelitfeature+1
Which model forecasts by repeating previous values until a change is detected?
a. Naive model
b. ARIMA
c. Decomposition model
d. Regression model
Answer: a. Naive modelscribd+1
ARIMA stands for:
a. Automatic Regression Inputs Model Analysis
b. Autoregressive Integrated Moving Average
c. Average Recursive Input Model Algorithm
d. Automated Recurrence Index and Moving Algorithm
Answer: b. Autoregressive Integrated Moving Averagescribd
Which time series function is used in R for monthly data?
a. ts()
b. plot()
c. freq()
d. data()
Answer: a. ts()scribd
For monthly frequency in R’s ts() function, the value is:
a. 4
b. 12
c. 52
d. 365
Answer: b. 12scribd
What does the ‘autoplot()’ function do in R?
a. Plots regression
b. Plots time series data
c. Creates histograms
d. Analyzes outliers
Answer: b. Plots time series datascribd
Which is NOT a requirement for regression model forecasting?
a. Coefficient of determination
b. F-statistics
c. Seasonal index
d. Linear relationship between variables
Answer: c. Seasonal indexscribd
Which is associated with the Big M Method?
a. Simplex optimization with artificial variables
b. Regression analysis
c. Qualitative input synthesis
d. Time series decomposition
Answer: a. Simplex optimization with artificial variablestestbook
The moving average method is better suited for:
a. Erratic, non-repeating data
b. Stable demand
c. Sudden jumps in trend
d. Highly seasonal patterns
Answer: b. Stable demandtestbook+1
If actual demand is 100 units and forecast was 90 units, the forecast error is:
a. -10
b. +10
c. -5
d. +5
Answer: b. +10scribd
What is one advantage of exponential smoothing?
a. Requires complex record keeping
b. Ignores large forecast errors
c. Simple calculation and adapts quickly
d. Only works with qualitative data
Answer: c. Simple calculation and adapts quicklystudocu+1
Forecast accuracy is highest in:
a. Long range forecasts
b. Short range forecasts
c. Medium range forecasts
d. All are equal
Answer: b. Short range forecastssanfoundry+1
Weighted moving average assigns:
a. Equal weight to all past periods
b. More weight to older data
c. More weight to recent data
d. No weight to outliers
Answer: c. More weight to recent datatestbook+1
In decomposition modeling, what is the main focus?
a. Breaking series into trend, seasonality, and residuals
b. Assigning equal weights
c. Eliminating qualitative variation
d. Maximizing forecast error
Answer: a. Breaking series into trend, seasonality, and residualsscribd
Which technique would best suit predicting sales affected by advertising?
a. Naive model
b. ARIMA
c. Associative model
d. Exponential smoothing
Answer: c. Associative modelscholarexpress
A qualitative forecast predicts:
a. Magnitude of change
b. Direction but not magnitude
c. Precise numeric value
d. Only trend pattern
Answer: b. Direction but not magnitudeglobal.oup
A combination of expert input and past data in forecasting is best referred to as:
a. Time series model
b. Associates model
c. Mixed model
d. Hybrid approach
Answer: d. Hybrid approachstudocu
What best describes “seasonality” in time series data?
a. Regular upward trend
b. Random error pattern
c. Regular patterns repeating within periods
d. Instant change in trend
Answer: c. Regular patterns repeating within periodsitfeature+1
Regression models in forecasting primarily require:
a. Dummy variables for all periods
b. Quantitative variables and causal relationships
c. Only past averages
d. Random qualitative input
Answer: b. Quantitative variables and causal relationshipssanfoundry+1
Which time series method is most responsive to sudden demand changes?
a. Naive method
b. Simple moving average
c. Weighted moving average
d. Exponential smoothing with large α
Answer: d. Exponential smoothing with large αtestbook+1
If α in exponential smoothing is set close to 1, the forecast:
a. Hardly changes
b. Responds very quickly to changes
c. Responds very slowly
d. Ignores new data
Answer: b. Responds very quickly to changesstudocu
Short range forecasts are generally:
a. More accurate than long range
b. Less accurate than long range
c. Equal in accuracy
d. Unreliable
Answer: a. More accurate than long rangesanfoundry+1
Which is not a time series component?
a. Long-term trend
b. Cyclical fluctuation
c. Random error
d. Decision-maker bias
Answer: d. Decision-maker biasitfeature
Delphi method is used when:
a. Past data is absent
b. Only quantitative methods are preferred
c. Trend is stable
d. Qualitative consensus is needed
Answer: d. Qualitative consensus is neededscholarexpress+1
What is a forecast based on previous forecast plus percent of error?
a. Qualitative forecast
b. Naive forecast
c. Moving average forecast
d. Exponentially smoothed forecast
Answer: d. Exponentially smoothed forecaststudocu
What is the coefficient of determination in regression?
a. Measure of prediction strength
b. Number of independent variables
c. Size of population sample
d. Frequency of errors
Answer: a. Measure of prediction strengthscribd
In time series forecasting, a six-month moving average is best used when demand:
a. Is highly seasonal
b. Is stable
c. Has frequent promotions
d. Has frequent random spikes
Answer: b. Is stablestudocu
Associative models use:
a. Only past data
b. Causal relationships or predictor variables
c. Expert opinions
d. Random selection methods
Answer: b. Causal relationships or predictor variablesscribd+1
Exponential smoothing is preferable over simple moving average because:
a. It requires more data storage
b. It is complex to calculate
c. It weights recent data higher
d. It only works with categorical data
Answer: c. It weights recent data highertestbook+1
The formula for weighted moving average includes:
a. Total sum of prior data
b. Assignment of different weights for each period
c. Ignoring older values
d. Using only qualitative data
Answer: b. Assignment of different weights for each periodtestbook+1
When forecasting, the underlying causal system is assumed:
a. To be unstable
b. To remain the same in future
c. To change every season
d. To be unpredictable
Answer: b. To remain the same in futurescholarexpress
Which component is not present in every time series model?
a. Trend
b. Seasonality
c. Cycles
d. Regression coefficient
Answer: d. Regression coefficientitfeature+1
Best use of ARIMA model is:
a. When seasonal and trend patterns both exist
b. For purely qualitative prediction
c. For random event forecasting only
d. For single point prediction
Answer: a. When seasonal and trend patterns both existscribd
Which type of forecast uses salesperson’s estimates?
a. Time series model
b. Salesforce composite
c. Exponential smoothing
d. Regression model
Answer: b. Salesforce compositestudocu
“Random variation” in forecasting occurs due to:
a. Predictable anomalies
b. Unexpected, non-patterned influences
c. Cyclical changes in trend
d. Regular seasonal adjustment
Answer: b. Unexpected, non-patterned influencesitfeature+1

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