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Exam 3 QMB3250 With 100% ACCURATE QUESTIONS AND ANSWERS GRADED A+
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JACKLINEExam 3 QMB3250 With 100% ACCURATE QUESTIONS AND ANSWERS
GRADED A+
Practice questions for this set
, 8/22/24, 1:03 AM
Terms in this set (84)
when we collect data over time to determine patterns for future outcomes
Time Series
ex: Daily, weekly, monthly, quarterly, yearly.
'Predicting' a quantitative response based on multiple inputs of quantitative variables
(and possible categorical = indicator variable, or variables multiplied = interaction terms)
Multiple Regression Models (for time series)
Additive - used for simple linear model.
Exponential - logarithm is taken
Quarters - adding indicator variables
Random walks the best prediction is the most recent point.
• creates a time series by averaging adjacent values.
MA(L) = Sum of L consecutive series values)/L
Moving averages
• Longer periods have MA that respond slower, while shorter periods respond faster
• Predicting the next unknown value in the time series is equal to the last predicted value.
Simple Exponential Smoothing Effective technique if time series shows little or no trend and no seasonal variation.
Equation is "~yt"=alpha"y"t + (1-alpha) "~yt-l"
--> alpha is the smoothing constant which is typically between 0 and 1
--> The closer alpha is to 1 the more weight is given to the more recent value and will 'react' faster to
Simple Exponential Smoothing [Equation] irregular components.
--> The closer alpha is to 0 the more weight is given to the historic value and will 'react' slower and
be more of a stable time series.
--> Predicting the next unknown value in the time series is equal to the last predicted value.
how much error there is proportional to the original value
MAPE
[Mean Absolute Percentage Error]
uses regression by using lagged variables. Each lag will move all the data forward a
Auto regressive Model
time period. Is a good model when you have seasonal components.
Auto regressive Model [equation] "~y"= b0 + b1(lag1) + b2(lag2) +...+ bp(lag p)
Exam 3 QMB3250
researchers do not try to change or manipulate anything, just observe.
Observational Studies
[You can find an association, but not causation]
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