Question

If there is a violation of the independence of error assumption for time series data, this could most likely cause _________. Question 1 options: autocorrelation. collinearity. a deflation of the standard error of the slope. unequal error variances.

Answer #1

**If there is a violation of the independence of error
assumption for time series data, this could most likely cause:
autocorrelation**

[ we know that,

Serial correlation , which is known as autocorrelation is sometimes a byproduct of a violation of linearity assumption,for example, in the case of a simple trend line fitted to data which are growing exponentially over time. extreme serial correlation is a symptom of a badly mis-specified model ]

***in case of doubt, comment below. And if u liked the solution,
please **like.**

which of the following is a violation of the
independence assumption?
negative correlation
a pattern of cyclical error terms over time
positive autocorrelation
a pattern of alternating error terms over time
all of the other choices are correct

Consider the following gasoline sales time series data. Click on
the datafile logo to reference the data.
Week
Sales (1000s of gallons)
1
16
2
20
3
19
4
23
5
19
6
15
7
19
8
17
9
23
10
20
11
15
12
21
a. Using a weight of (1/2) for the most recent
observation, (1/3) for the second most recent observation, and
(1/6) third the...

Consider the following time series
data.
Week
1
2
3
4
5
6
Value
18
14
16
12
17
14
Using the naive method (most recent value) as the forecast for
the next week, compute the following measures of forecast accuracy.
Round the intermediate calculations to two decimal places.
Mean absolute error (to 1 decimal).
Mean squared error (to 1 decimal).
Mean absolute percentage error (to 2 decimals).
%
What is the forecast for week 7 (to the nearest whole...

Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
14
17
12
17
14
Calculate the measures of forecast error using the naive (most
recent value) method and the average of historical data (to 2
decimals).
Naive method
Historical data
Mean absolute error
Mean squared error
Mean absolute percentage error
Which method provides the most accurate forecasts?
SelectNaiveHistorical dataItem 7

Consider the following gasoline sales time series data.
Week
Sales (1000s of gallons)
1
18
2
22
3
20
4
24
5
17
6
15
7
19
8
17
9
23
10
19
11
14
12
23
a. Using a weight of 1/2 for the most recent
observation, 1/3 for the second most recent observation, and 1/6
third the most recent observation, compute a three-week weighted
moving average...

2. We will be creating a monthly time series plot from raw data
from the internet.
2.1 Using data at from the Bureau of Labor Statistics (BLS),
create a MONTHLY time series dataset. You may use site
http://data.bls.gov/cgi-bin/surveymost?bls (Links to an external
site.)Links to an external site. which is a list of "Top Picks";
select the first CPI for all urban consumers (Price Indexes), and
click "Retreive Data". On the resulting data page,
click on the "More Formatting Options" button,...

Consider the following time series data.
Week
1
2
3
4
5
6
Value
19
14
16
11
18
15
Using the naive method (most recent value) as the forecast for
the next week, compute the following measures of forecast accuracy.
Round the intermediate calculations to two decimal places.
Mean absolute error (to 1 decimal).
Mean squared error (to 1 decimal).
Mean absolute percentage error (to 2 decimals).
%
What is the forecast for week 7 (to the nearest whole...

1.
Using the data below, what is the value of RMSE?
Week
Time Series Value
Forecast
1
3
7.00
2
5
4.00
3
2
5.00
4
8
8.00
2.
Using the data below, calculate the squared error for the 4th
week. Use the 2 period moving average to create the forecast.
Week
Time Series Value
1
19.00
2
11.00
3
22.00
4
7.00
Please, do the all the work and show processes

Consider the following time series data.
Week 1 2 3 4 5 6
Value 19 14 17 10 17 13
Using the naive method (most recent value) as the forecast for
the next week, compute the following measures of forecast
accuracy.
(a) mean absolute error MAE =
(b) mean squared error MSE =
(c) mean absolute percentage error (Round your answer to two
decimal places.) MAPE = %
(d) What is the forecast for week 7?

Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
12
17
11
17
14
Using the naive method (most recent value) as the forecast for
the next week, compute the following measures of forecast
accuracy.
(a)
mean absolute error
MAE =
(b)
mean squared error
MSE =
(c)
mean absolute percentage error (Round your answer to two decimal
places.)
MAPE = %
(d)
What is the forecast for week 7?

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