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Demand Forecasting: Methods, Techniques, and Applications, Exams of Business Economics

A comprehensive overview of demand forecasting, covering various methods, techniques, and applications. It explores different forecasting approaches, including naรฏve, moving average, and exponential smoothing, and delves into the concepts of trend, seasonality, and statistical noise. The document also discusses the importance of forecast accuracy and the use of metrics like mae, mape, and mse to evaluate forecast performance. It further examines the role of human intervention in forecasting and the use of automated forecasting systems. This resource is valuable for students and professionals seeking to understand the fundamentals of demand forecasting and its practical applications.

Typology: Exams

2024/2025

Available from 01/27/2025

Shantelle
Shantelle ๐Ÿ‡บ๐Ÿ‡ธ

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_______ analysis estimates the relationship of one variable with multiple variables that influence this
one variable. - โœ” โœ” Regression
___________ analysis estimates the relationship of one variable with multiple variables that influence
this one variable. - โœ” โœ” Regression
______________ is the process of creating statements about outcomes of variables that will only be
realized in the future and are currently uncertain. - โœ” โœ” Forecasting
A forecast should be ______, or, correct on average. - โœ” โœ” unbiased
A forecast that is, on average, correct is _blank_. - โœ” โœ” unbiased
A good forecast should come close to the real outcomes as measured by the mean _______ error. - โœ”
โœ” squared or absolute
A good forecast should come close to the real outcomes as measured by the mean _________ error. - โœ”
โœ” squared or absolute
A time series-based forecast is a form of __________, that is, of assuming that some patterns in the data
observed will prevail in the future. - โœ” โœ” extrapolation
Automated forecasts are typically _blank_. - โœ” โœ” created by computers
Demand __________ is the process of creating statements about future realizations of demand. - โœ” โœ”
forecasting or forecast
Double exponential smoothing _blank_. - โœ” โœ” does not work well when there is a seasonality
pattern to the data
Double exponential smoothing is a(n) _blank_ method. - โœ” โœ” additive
Drag each business decision to the type of forecasting it would require.
Short-term----->
Mid-term------>
Long-term----> - โœ” โœ” Short-term----->Staffing levels and scheduling
Mid-term------->Recruiting and machine acquisition
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_______ analysis estimates the relationship of one variable with multiple variables that influence this one variable. - โœ” โœ” Regression ___________ analysis estimates the relationship of one variable with multiple variables that influence this one variable. - โœ” โœ” Regression ______________ is the process of creating statements about outcomes of variables that will only be realized in the future and are currently uncertain. - โœ” โœ” Forecasting A forecast should be ______, or, correct on average. - โœ” โœ” unbiased A forecast that is, on average, correct is blank. - โœ” โœ” unbiased A good forecast should come close to the real outcomes as measured by the mean _______ error. - โœ” โœ” squared or absolute A good forecast should come close to the real outcomes as measured by the mean _________ error. - โœ” โœ” squared or absolute A time series-based forecast is a form of __________, that is, of assuming that some patterns in the data observed will prevail in the future. - โœ” โœ” extrapolation Automated forecasts are typically blank. - โœ” โœ” created by computers Demand __________ is the process of creating statements about future realizations of demand. - โœ” โœ” forecasting or forecast Double exponential smoothing blank. - โœ” โœ” does not work well when there is a seasonality pattern to the data Double exponential smoothing is a(n) blank method. - โœ” โœ” additive Drag each business decision to the type of forecasting it would require. Short-term-----> Mid-term------> Long-term----> - โœ” โœ” Short-term----->Staffing levels and scheduling Mid-term------->Recruiting and machine acquisition

Long-term------>Entering new markets or launching new products Drag each category of data to the smoothing parameter number that would be assigned to it in the exponential smoothing forecast. Instructions .9---> .1---> - โœ” โœ” .9----->Current demand .1------>Old data Drag each data category to the amount of weight it would be assigned in an exponential smoothing forecast. More weight---> Less weight---> - โœ” โœ” More weight---> Recent data Less weight---> Older data Drag each data category to the amount of weight it would be assigned in an exponential smoothing forecast. Recent data----> Older data---> - โœ” โœ” Recent data--->More weight Older data--->Less weight Drag each feature to the forecasting method it matches. Naรฏve method-----> Moving average----> Exponential smoothing----> - โœ” โœ” Naรฏve method----->Very vulnerable to noise Moving average---->Reduces statistical noise by averaging it out Exponential smoothing---->More responsive to changes in demand Drag each level of human intervention to its corresponding forecasting framework. Automated forecasting-----> Expert panel forecasting-------> - โœ” โœ” Automated forecasting------>Done by computers with little or no human intervention.

Regression analysis is based on blank. - โœ” โœ” statistics Select all that apply Select all the average forecast errors for a biased forecast. - โœ” โœ” > 0 < 0 = 13 Statistical noise blank. - โœ” โœ” cannot be forecasted Suppose a forecast over-estimates demand by 5,000 units on day 1 and under-estimates demand by 5,000 units on day 2. Which metric better reflects the accuracy of the forecast? - โœ” โœ” Mean squared error Suppose you want to understand the influence of interest rates on new home sales. Drag each type of variable to the financial element it represents. Interest rates----->__________ New home sales------>___________ - โœ” โœ” Interest rates------>Independent variable New home sales------>Dependent variable Taking the moving average reduces the effect of statistical _______ - โœ” โœ” noise The closer the forecast value is to the actual value, the _____ the forecast error. - โœ” โœ” lower The idea of _________ smoothing is to put more weight on recent data and less weight on older data. - โœ” โœ” exponential The MAE takes the _______ values of the forecast errors to ensure they don't cancel each other out. - โœ” โœ” absolute The MAE takes the _________ values of the forecast errors to ensure they don't cancel each other out. - โœ” โœ” absolute The mean absolute percentage error is achieved by dividing the forecast errors for a time period by the actual ________ of that time period. - โœ” โœ” demand The more periods considered for the moving average, the blank. - โœ” โœ” less statistical noise The naรฏve forecasting method uses how many old data points? - โœ” โœ” One The naรฏve, moving average, and exponential smoothing forecasting methods blank. - โœ” โœ” do not detect trends

The smaller the value of alpha, the blank. - โœ” โœ” more smooth the forecast The statistical ________ in the demand for a process is the amount of demand that is purely a result of randomness and could not have been forecasted. - โœ” โœ” noise The trend is measured by calculating the blank the old and the new demand forecasts. - โœ” โœ” difference between To find a smoothing parameter that will deliver a more conservative forecast, choose a blank. - โœ” โœ” lower value of alpha To forecast demand in period t+1 while considering a trend involves how many terms on the right-hand side of the equation? - โœ” โœ” Two Trends are detected based upon historical data and blank. - โœ” โœ” are not guaranteed to continue in the future True or false: The MAE and MSE will always agree on which forecast has the highest error. - โœ” โœ” False Reason: By squaring the errors, the MSE penalizes large errors much more than the MAE. True or false: The more unbiased a forecast is, the smaller the mean squared error (MSE). - โœ” โœ” False Reason: A forecast can be right on average, but produce values that are far from the real outcomes. Unlike moving average or exponential smoothing, detecting a ________ requires looking for increases or decreases in historical data. - โœ” โœ” trend When the trend is multiplicative (e.g. exponential growth), double exponential smoothing will blank demand. - โœ” โœ” underestimate