What is the MAPE (mean absolute percentage error) and when would
I use it for a...
What is the MAPE (mean absolute percentage error) and when would
I use it for a model?
Solutions
Expert Solution
Answer :
The MAPE :-
The MAPE (Mean Absolute Percent
Error) measures the span of the mistake in rate terms.
It is determined as the normal of
the unsigned rate blunder,
as appeared in the precedent
underneath:
Numerous associations center
principally around the MAPE while evaluating figure precision.
The vast majority are happy with
deduction in rate terms, making the MAPE simple to translate.
It can likewise pass on data when
you don't have the foggiest idea about the thing's interest
volume.
For instance, telling your chief,
"we were off by under 4%" is more important than saying "we were
off by 3,000 cases," if your supervisor doesn't know a thing's run
of the mill request volume.
The MAPE is scale delicate and
ought not be utilized when working with low-volume
information.
Notice that on the grounds that
"Real" is in the denominator of the condition, the MAPE is unclear
when Actual interest is zero.
Moreover, when the Actual esteem
isn't zero, however very little, the MAPE will frequently take on
outrageous qualities.
This scale affectability renders
the MAPE near useless as a mistake measure for low-volume
information.
Describe what would be a type I error and what would be a type
II error:
A restaurant claims more than 42% of its new patrons return at
least once within a month.
What is Type I error? How do we correct for Type I error? What
happens when we correct for Type I error? What is Type II error?
How do we correct for Type II error? What happens when we correct
for Type II error? How can we correct for both Type I and Type II
error at the same time? Which error is considered the worst type of
error to commit?
2. Calculate Mean Absolute
Error ( MAD) for the data in question 1 for the
three methods used. Round MAD to two
decimal places. ( 4 marks)
Year
Revenue
4-Year Moving
Average
Absolute
Error
4 Weighted Moving Average
Weights 4,3,2,1
Absolute Error
Exponential
Smoothing
α = 0.6
Absolute Error
2010
75
2011
81
2012
74
2013
79
2014
69
2015
92
2016
73
2017
85
2018
90
2019
73
2020
Forecast
What does MAD measures? which of these...
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I get an error when im trying to run this java program, I would
appreciate if someone helped me asap, I will make sure to leave a
good review. thank you in advance!
java class Node
public class Node {
private char item;
private Node next;
Object getNext;
public Node(){
item = ' ';
next = null;
}
public Node(char newItem) {
setItem(newItem);
next = null;
}
public Node(char newItem, Node newNext){
setItem(newItem);
setNext(newNext);
}
public void setItem(char newItem){...
T/f: The mean absolute deviation is more sensitive to large
deviations than the mean square error.
T/f: A smoothing constant of 0.1 will cause an exponential
smoothing forecast to react more quickly to a sudden change than a
value of 0.3 will.
T/f:An advantage of the exponential smoothing forecasting method
is that more recent experience is given more weight than less
recent experience.
T/f: Linear regression can be used to approximate the
relationship between independent and dependent variables.
T/f:"Forecasting techniques...
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show the formula for ”Absolute Purchasing Power Parity” and
state the reasons very briefly why it may not hold in Short Run