Homework Answers
Ans.- (A)
cyclical variation is a REGULAR variation that time-series data frequently exhibit.
Failure to take such regular variations into account when estimating a forecasting equation will lead to biased forecasting.We can use the dummy variable technique to correct this.
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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… ---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.
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...
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...
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)...
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 and other devede are defined as follows: Q1 = 1 if the time period is Quarter 1, and O otherwise. Q2 and 3 are defined similarly. Abbreviations Used in the Output • "R-So" stands for "r squared • "R-Sq" stands for "adjusted rsquared SSE • s stands for "regression standard error," equal...
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...
Assume you are analyzing a time series of 12 observations. Using a 3-period moving average, you...
Assume you are analyzing a time series of 12 observations. Using a 3-period moving average, you successfully calculate the Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) for each forecasted time period. You then sum the error calculations for each time period for each of the three error calculation techniques respectively. You end up with: MAE 53.5, MSE 547.25, and MAPE 44.15%. To complete your error calculation and compare the accuracy of the 3-period...
X Part I. Derive Bivariate Regression by hand. Again, we are using the same data set that we used in the in-cl...
X Part I. Derive Bivariate Regression by hand. Again, we are using the same data set that we used in the in-class assessment. Case Dietary Fat Body Fat 22 9.8 22 11.7 14 8.0 21 9.7 32 10.9 26 7.8 30 21 17 1. Step 1: Find the mean of dietary fat x = 2. Step 2: Find the mean of body fat y = 3. Step 3: Find the sum of (x1 - x)y- y) = 3316 4. Step...
HELLO, PLEASE TAKE TIME TO ANSWER THIS QUESTION. PLESE ENSURE ITS CORRECT AND SHOW THAT THE...
HELLO, PLEASE TAKE TIME TO ANSWER THIS QUESTION. PLESE ENSURE ITS CORRECT AND SHOW THAT THE PROGRAM RUNS. Task 1: Enforcing const-ness throughout Your first job will be to go through all of the code and decide which functions should be declared const. You should find several places throughout the program where this makes sense. We will also make the id data member in the Customer class const , as once a customer has been created their ID will never...
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)...
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 and other devede are defined as follows: Q1 = 1 if the time period is Quarter 1, and O otherwise. Q2 and 3 are defined similarly. Abbreviations Used in the Output • "R-So" stands for "r squared • "R-Sq" stands for "adjusted rsquared SSE • s stands for "regression standard error," equal...
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...
Assume you are analyzing a time series of 12 observations. Using a 3-period moving average, you successfully calculate the Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) for each forecasted time period. You then sum the error calculations for each time period for each of the three error calculation techniques respectively. You end up with: MAE 53.5, MSE 547.25, and MAPE 44.15%. To complete your error calculation and compare the accuracy of the 3-period...
X Part I. Derive Bivariate Regression by hand. Again, we are using the same data set that we used in the in-class assessment. Case Dietary Fat Body Fat 22 9.8 22 11.7 14 8.0 21 9.7 32 10.9 26 7.8 30 21 17 1. Step 1: Find the mean of dietary fat x = 2. Step 2: Find the mean of body fat y = 3. Step 3: Find the sum of (x1 - x)y- y) = 3316 4. Step...
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