_______ analysis estimates the relationship of one variable with multiple variables that influence this one variable. - ️️Regression
___________ analysis estimates the relationship of one variable with multiple variables that influence this one variable. - ️️Regression
______________ is t...
_______ analysis estimates the relationship of one variable with multiple variables that influence this
one variable. - ✔ ✔ Regression
___________ analysis estimates the relationship of one variable with multiple variables that influence
this one variable. - ✔ ✔ Regression
______________ is the process of creating statements about outcomes of variables that will only be
realized in the future and are currently uncertain. - ✔ ✔ Forecasting
A forecast should be ______, or, correct on average. - ✔ ✔ unbiased
A forecast that is, on average, correct is _blank_. - ✔ ✔ unbiased
A good forecast should come close to the real outcomes as measured by the mean _______ error. - ✔
✔ squared or absolute
A good forecast should come close to the real outcomes as measured by the mean _________ error. - ✔
✔ 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. - ✔ ✔ extrapolation
Automated forecasts are typically _blank_. - ✔ ✔ created by computers
Demand __________ is the process of creating statements about future realizations of demand. - ✔ ✔
forecasting or forecast
Double exponential smoothing _blank_. - ✔ ✔ does not work well when there is a seasonality
pattern to the data
Drag each business decision to the type of forecasting it would require.
Short-term----->
Mid-term------>
Long-term----> - ✔ ✔ 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---> - ✔ ✔ .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---> - ✔ ✔ 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---> - ✔ ✔ Recent data--->More weight
Older data--->Less weight
Drag each feature to the forecasting method it matches.
Naïve method----->
Moving average---->
Exponential smoothing----> - ✔ ✔ Naïve 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-------> - ✔ ✔ Automated forecasting------>Done by computers with little or
no human intervention.
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