Seasonal or cyclical variation in a time-series model… ---exhibits irregular variation that can be accounted for...
Seasonal or cyclical variation in a time-series model…
---exhibits irregular variation that can be accounted for by dummy variables.
----is regular in nature but can be accounted for by dummy variables.
----is irregular in nature and need not be accounted for by dummy variables.
----None of these options are correct.
Homework Answers
----None of these options are correct.
Explanation: Seasonal variations happen in a fixed and known period. However, in cyclical variations, the data shows rise and fall that are not of fixed period.
Add Answer to:
Seasonal or cyclical variation in a time-series model…
---exhibits irregular
variation that can be accounted for...
Seasonal or cyclical variation in a time-series model... O is regular in nature but can be accounted for by dummy varia...
Seasonal or cyclical variation in a time-series model... O is regular in nature but can be accounted for by dummy variables. O None of these options are correct. O exhibits irregular variation that can be accounted for by dummy variables. O is irregular in nature and need not be accounted for by dummy variables.
5. The components of time-series are: a. Trend, Seasonal, Movement, and Random b. Trend, Mobility, Cyclical,...
5. The components of time-series are: a. Trend, Seasonal, Movement, and Random b. Trend, Mobility, Cyclical, and Seasonal c. Trend, Seasonal, Cyclical, and Random d. Trend, Seasonal, Cyclical, and Perfection The mean absolute deviation measures the accuracy of a forecast by calculating.. a. the mid-point of absolute forecasting error per period of historical data. b. the average absolute forecasting error per period of historical data. c. the standard deviation of absolute forecasting error per period of historical data d. both...
Which one of the following is a good candidate to forecast the cyclical component for the future?...
Which one of the following is a good candidate to forecast the cyclical component for the future? HES SES WES All of the above In OLS, deviations of predicted values from actual values are called Residuals Population errors Random deviations All of the above When computing the MAt, _________ is(are) removed Seasonality and irregular fluctuations Seasonality, irregular fluctuations, and cyclical movements Seasonality and cyclical movements Irregular fluctuations and cyclical movements A common source of unusual coefficient estimate signs and statistical...
3. Using the TGT Quarterly Sales (Target Corp.) data: a. Fit a regression model with a time trend and seasonal dummy variables to the sales data. b. Is the time trend coefficient statistically significant? How can you tell? c. Are the seasonal dummy varia
3. Using the TGT Quarterly Sales (Target Corp.) data:Assume October 2011 is Quarter 3, Period (Trend) 1, etc.a. Fit a regression model with a time trend and seasonal dummy variables to the sales data.b. Is the time trend coefficient statistically significant? How can you tell?c. Are the seasonal dummy variables statistically significant? How can you tell?d. Assume time is 0. Calculate sales for Q3. Round to two decimal places.e.What is the coefficient on the first quarter? Round to two decimal...
Can I model past shocks in order to forecast future values using seasonal variation and trends?
Can I model past shocks in order to forecast future values using seasonal variation and trends?
A regression model with quarterly seasonal dummy variables was fit to quarterly sales data (in $10,000)...
A regression model with quarterly seasonal dummy variables was fit to quarterly sales data (in $10,000) for a small company. The results are shown below. The dummy variables are defined as follows: Q1 = 1 if the time period is Quarter 1, and otherwise. Q2 and Q3 are defined similarly. Abbreviations Used in the Output • "R-Sq" stands for "r squared" • "R-Sq" stands for "adjusted r squared • s stands for "regression standard error," equal to SSE V n-(p+1)...
Consider the following time series: Quarter Year 1 Year 2 Year 3 71 68 4941 (a)...
Consider the following time series: Quarter Year 1 Year 2 Year 3 71 68 4941 (a) Choose a time series plot. 9 10 11 12 Perodit 8 8 8 8 112 9 10 11 12 Periodit - Select your answer What type of pattern exists in the data? Is there an indication of a seasonal pattern? - Select your answer - (b) Use a multiple linear regression model with dummy variables as follows to develop an equation to account for...
Consider the following time series data. Consider the following time series data. Quarter Year 1 Year...
Consider the following time series data.
Consider the following time series data.
Quarter
Year 1
Year 2
Year 3
1
3
6
8
2
2
4
8
3
4
7
9
4
6
9
11
(a)
Choose the correct time series plot.
(i)
(ii)
(iii)
(iv)
- Select your answer -Plot (i)Plot (ii)Plot (iii)Plot (iv)Item
1
What type of pattern exists in the data?
- Select your answer -Positive trend pattern, no
seasonalityHorizontal pattern, no seasonalityNegative trend
pattern, no seasonalityPositive...
Consider the following time series data: Quarter Year 1 Year 2 Year 3 1 71 68...
Consider the following time series data: Quarter Year 1 Year 2 Year 3 1 71 68 62 2 49 41 51 3 58 60 53 4 78 81 72 Question: Use a multiple regression linear model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Q1=1 if quarter 1, 0 otherwise Q2=1 if quarter 2, 0 otherwise Q3=1 if quarter 3, 0 otherwise What is the R^2 (coefficient of determination)? Round to...
For a given time series the PACF and ACF at lag 1 are equal True False PACF can be used full f...
For a given time series the PACF and ACF at lag 1 are equal True False PACF can be used full for determining what lags can serve as predictor variable in the regression model True False If the histogram and normal quantile plot of the residuals from a time series regression fit show “normality” Then residuals are random overtime True False Supposed quarterly sales was fitted with a seasonal model of 459 + 61q4 is an indicator variable for q...
Seasonal or cyclical variation in a time-series model... O is regular in nature but can be accounted for by dummy variables. O None of these options are correct. O exhibits irregular variation that can be accounted for by dummy variables. O is irregular in nature and need not be accounted for by dummy variables.
5. The components of time-series are: a. Trend, Seasonal, Movement, and Random b. Trend, Mobility, Cyclical, and Seasonal c. Trend, Seasonal, Cyclical, and Random d. Trend, Seasonal, Cyclical, and Perfection The mean absolute deviation measures the accuracy of a forecast by calculating.. a. the mid-point of absolute forecasting error per period of historical data. b. the average absolute forecasting error per period of historical data. c. the standard deviation of absolute forecasting error per period of historical data d. both...
A regression model with quarterly seasonal dummy variables was fit to quarterly sales data (in $10,000) for a small company. The results are shown below. The dummy variables are defined as follows: Q1 = 1 if the time period is Quarter 1, and otherwise. Q2 and Q3 are defined similarly. Abbreviations Used in the Output • "R-Sq" stands for "r squared" • "R-Sq" stands for "adjusted r squared • s stands for "regression standard error," equal to SSE V n-(p+1)...
Consider the following time series: Quarter Year 1 Year 2 Year 3 71 68 4941 (a) Choose a time series plot. 9 10 11 12 Perodit 8 8 8 8 112 9 10 11 12 Periodit - Select your answer What type of pattern exists in the data? Is there an indication of a seasonal pattern? - Select your answer - (b) Use a multiple linear regression model with dummy variables as follows to develop an equation to account for...
Consider the following time series data.
Consider the following time series data.
Quarter
Year 1
Year 2
Year 3
1
3
6
8
2
2
4
8
3
4
7
9
4
6
9
11
(a)
Choose the correct time series plot.
(i)
(ii)
(iii)
(iv)
- Select your answer -Plot (i)Plot (ii)Plot (iii)Plot (iv)Item
1
What type of pattern exists in the data?
- Select your answer -Positive trend pattern, no
seasonalityHorizontal pattern, no seasonalityNegative trend
pattern, no seasonalityPositive...
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