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o Seasonality and heteroskedasticity
Heteroscedasticity (also spelled heteroskedasticity) refers to the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it. A scatterplot of these variables will often create a cone-like shape, as the scatter (or variability) of the dependent variable (DV) widens or narrows as the value of the independent variable (IV) increases.
The graph above seem to have all characteristics of heteroskedastic varaible.
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Time Series and the Seasonal-Means+ Polynomial Trend 10 15 Time Figure 5...
Please explain your answer Suppose that we use least-squares to fit a seasonal-means trend to this...
Please explain your answer
Suppose that we use least-squares to fit a seasonal-means trend to this time series. Figure 3 displays the original time series plot along wtih the fitted values. Time Series and Seasonal-Means Fit 10 15 Time Figure 3 Which of the following characteristics is the model able to capture? Trend Seasonality ● Trend and seasonality Seasonality and heteroskedasticity
Please explain your answer Suppose that we use least-squares to fit a polynomial trend to this...
Please explain your answer
Suppose that we use least-squares to fit a polynomial trend to this time series. Figure 4 displays the original time series plot along with the fitted values. Time Series and Polynomial Fit of the Trend 10 15 Time Figure 4 Which of the following characteristics is the model able to capture? Trend Seasonality Trend and seasonality Seasonality and heteroskedasticity
Please explain your answer Consider the time series plot of the differenced data in Figure 2....
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Consider the time series plot of the differenced data in Figure 2. Time Series Plot of the Differenced Data 10 15 20 Time Figure 2 Which of the following characteristics are present in the time series plot of the differenced data (refer figure 2)? Periodicity Seasonality Periodicity and seasonality Periodicity and heteroskedasticity
In this practice problem, you will work with the following data 5.0610.32 5.09 1213.05 9.58 13...
In this practice problem, you will work with the following data 5.0610.32 5.09 1213.05 9.58 13 10.24 4 6.07 14 2.32 6.4315 14.86 16 13.56 7.26 171.92 6.8118 16.75 9 13.161913.29 10 8.76 20 15.06 6 Time Series Plot of the Data 10 15 20 Time Figure 1 Which of the following characteristics are present in the time series plot of the original data (figure 1)? Periodicity and seasonality Trend and seasonality Trend and heteroskedasticity Periodicity and trend
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...
QUESTION 27 Suppose the following are the seasonal indices for the first three quarters of the...
QUESTION 27 Suppose the following are the seasonal indices for the first three quarters of the year for a quarterly series: Quarter Seasonal Index Q1 71.6 Q2 83.2 Q3 106 Remember that the seasonal indices should average 100 so you should be able to infer the seasonal index for Q4. Furthermore, suppose that the estimated coeffcients from a regression of the deseasonalized series on Time are given below: Coefficients Intercept 2,942 Time 67.1 What is the trend projection of the...
quantitative QUESTION 15 Given is a time series. What is the time series demand pattern as...
quantitative
QUESTION 15 Given is a time series. What is the time series demand pattern as below? Date Price 6/26/2012 121.34 6/27/2012 102.56 6/28/2012 98.67 6/29/2012 99.6 7/2/2012 102.32 7/3/2012 95.23 7/5/2012 89.34 7/6/2012 82.37 79.56 7/10/2012 81.23 7/11/2012 72.67 7/12/2012 69.23 7/13/2012 62.85 7/16/2012 57.87 7/17/2012 58.23 7/9/2012 Horizontal Trend-Downward O Seasonal Trend and Seasonal Cyclical Trend-Upward com/webapps/assessment/take/launch.jsp?course_assessmen
Q7 We have observed the time series shown in the following line chart: 60 50 40...
Q7 We have observed the time series shown in the following line chart: 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time Which would be the most appropriate forecasting model to use for this time series? Select one: a. Regression b. Single exponential smoothing O c. Holt-Winters (including both trend and seasonality) O d. Double exponential smoothing
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 5 8...
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 5 8 10 2 1 3 7 3 3 6 8 4 7 10 12 (d) Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for Quarter 1 in Year 1, t =...
Consider the following time series data. 15 13 10 12 a. Which of the following is a correct time series plot for this data? TimeSeries Value 14 12 10 TimePeriod (t) TimeSeries Value 14 12 10 TimePeri...
Consider the following time series data. 15 13 10 12 a. Which of the following is a correct time series plot for this data? TimeSeries Value 14 12 10 TimePeriod (t) TimeSeries Value 14 12 10 TimePeriod(t) TimeSeries Value 14 12 10 TimePeriod (t) 2 TimePeriod(t) select your answer- What type of pattern exists in the data? -select your answer b. Develop the linear trend equation for this time series (to 1 decimal). Tt = c. What is the forecast...
Please explain your answer
Suppose that we use least-squares to fit a seasonal-means trend to this time series. Figure 3 displays the original time series plot along wtih the fitted values. Time Series and Seasonal-Means Fit 10 15 Time Figure 3 Which of the following characteristics is the model able to capture? Trend Seasonality ● Trend and seasonality Seasonality and heteroskedasticity
Please explain your answer
Suppose that we use least-squares to fit a polynomial trend to this time series. Figure 4 displays the original time series plot along with the fitted values. Time Series and Polynomial Fit of the Trend 10 15 Time Figure 4 Which of the following characteristics is the model able to capture? Trend Seasonality Trend and seasonality Seasonality and heteroskedasticity
Please explain your answer
Consider the time series plot of the differenced data in Figure 2. Time Series Plot of the Differenced Data 10 15 20 Time Figure 2 Which of the following characteristics are present in the time series plot of the differenced data (refer figure 2)? Periodicity Seasonality Periodicity and seasonality Periodicity and heteroskedasticity
In this practice problem, you will work with the following data 5.0610.32 5.09 1213.05 9.58 13 10.24 4 6.07 14 2.32 6.4315 14.86 16 13.56 7.26 171.92 6.8118 16.75 9 13.161913.29 10 8.76 20 15.06 6 Time Series Plot of the Data 10 15 20 Time Figure 1 Which of the following characteristics are present in the time series plot of the original data (figure 1)? Periodicity and seasonality Trend and seasonality Trend and heteroskedasticity Periodicity and trend
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...
quantitative
QUESTION 15 Given is a time series. What is the time series demand pattern as below? Date Price 6/26/2012 121.34 6/27/2012 102.56 6/28/2012 98.67 6/29/2012 99.6 7/2/2012 102.32 7/3/2012 95.23 7/5/2012 89.34 7/6/2012 82.37 79.56 7/10/2012 81.23 7/11/2012 72.67 7/12/2012 69.23 7/13/2012 62.85 7/16/2012 57.87 7/17/2012 58.23 7/9/2012 Horizontal Trend-Downward O Seasonal Trend and Seasonal Cyclical Trend-Upward com/webapps/assessment/take/launch.jsp?course_assessmen
Q7 We have observed the time series shown in the following line chart: 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time Which would be the most appropriate forecasting model to use for this time series? Select one: a. Regression b. Single exponential smoothing O c. Holt-Winters (including both trend and seasonality) O d. Double exponential smoothing
Consider the following time series data. 15 13 10 12 a. Which of the following is a correct time series plot for this data? TimeSeries Value 14 12 10 TimePeriod (t) TimeSeries Value 14 12 10 TimePeriod(t) TimeSeries Value 14 12 10 TimePeriod (t) 2 TimePeriod(t) select your answer- What type of pattern exists in the data? -select your answer b. Develop the linear trend equation for this time series (to 1 decimal). Tt = c. What is the forecast...
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