2759 Fitting Trend Lines And Forecasting (Seasonal Data)
Questions and Answers
- 1.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation that needs to be randomised
- D.
No, it appears to have cyclic variation
Correct Answer
B. Yes, it appears to have seasonal variation that needs to be deseasonalisedExplanation
The correct answer is "Yes, it appears to have seasonal variation that needs to be deseasonalised." This is because the time series plot shows a clear pattern of regular ups and downs, indicating that there is a seasonal component present in the data. Deseasonalising the data will help to remove this seasonal variation and allow for a more accurate calculation of the trend line.Rate this question:
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- 2.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation that needs to be randomised
- D.
Yes, it appears to have cyclic variation
Correct Answer
B. Yes, it appears to have seasonal variation that needs to be deseasonalisedExplanation
The correct answer is "Yes, it appears to have seasonal variation that needs to be deseasonalised." This is because the time series plot shows a clear pattern that repeats at regular intervals, indicating the presence of seasonality. Deseasonalising the data would remove this seasonal variation, allowing for a more accurate calculation of the trend line.Rate this question:
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- 3.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation
Correct Answer
B. Yes, it appears to have seasonal variation that needs to be deseasonalisedExplanation
The correct answer is Yes, it appears to have seasonal variation that needs to be deseasonalised. This is because the time series plot shows a clear pattern of repeating highs and lows at regular intervals, indicating the presence of seasonality. Deseasonalising the data will remove this seasonal variation and allow for a more accurate calculation of the trend line.Rate this question:
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- 4.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation
- D.
Yes, it appears to have cyclic variation
Correct Answer
B. Yes, it appears to have seasonal variation that needs to be deseasonalisedExplanation
The correct answer is "Yes, it appears to have seasonal variation that needs to be deseasonalised." This is because the time series plot shows a repeating pattern or cycle, indicating the presence of seasonality. To accurately calculate the trend line, it is necessary to remove the seasonal component from the data.Rate this question:
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- 5.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation
Correct Answer
C. No, it appears to have cyclic variation which requires smoothingExplanation
The correct answer is No, it appears to have cyclic variation which requires smoothing. This is because the time series plot shows a repeating pattern or cycle, indicating that there is some form of cyclic variation present in the data. In order to calculate a trend line accurately, it is necessary to remove or smooth out this cyclic variation.Rate this question:
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- 6.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation which we can deseasonalise
Correct Answer
C. No, it appears to have cyclic variation which requires smoothingExplanation
The correct answer is "No, it appears to have cyclic variation which requires smoothing." This is because the time series plot shows a repeating pattern or cycle, indicating the presence of cyclic variation. To calculate the trend line accurately, it is necessary to remove the cyclic component through smoothing techniques. Deseasonalization, on the other hand, is used to remove seasonal patterns from the data, which is not evident in this case.Rate this question:
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- 7.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation which can be deseasonalised
Correct Answer
C. No, it appears to have cyclic variation which requires smoothingExplanation
The correct answer is "No, it appears to have cyclic variation which requires smoothing." This is because the time series plot shows a repeating pattern or cycle, indicating the presence of cyclic variation. In order to calculate the trend line accurately, it is necessary to smooth out the cyclic component of the data. Deseasonalising the data would not be appropriate in this case as there is no clear evidence of seasonal variation.Rate this question:
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- 8.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. No, it appears to have random variation only.Explanation
Based on the given time series plot, it is observed that the data does not exhibit any clear pattern or regular fluctuations. The plot shows random variation, indicating that there is no apparent seasonal or cyclic variation present. Therefore, there is no need to deseasonalize the data before calculating the trend line.Rate this question:
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- 9.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. No, it appears to have random variation only.Explanation
The correct answer is "No, it appears to have random variation only." This is because the time series plot does not show any clear patterns or regular cycles, indicating that there is no seasonal or cyclic variation present in the data. Therefore, there is no need to deseasonalize the data before calculating the trend line.Rate this question:
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- 10.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. No, it appears to have random variation only.Explanation
The correct answer is "No, it appears to have random variation only." This is because the time series plot does not show any clear patterns or cycles, indicating that there is no seasonal or cyclic variation present. Therefore, there is no need to deseasonalize the data before calculating the trend line.Rate this question:
-
- 11.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. No, it appears to have random variation only.Explanation
Based on the given time series plot, it can be observed that there is no clear pattern or repetitive cycle in the data. The data points seem to be randomly scattered without any noticeable seasonality or cyclic variation. Therefore, there is no need to deseasonalize the data before calculating the trend line.Rate this question:
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- 12.
From the time series plot above, should you deseasonalise the data before calculating the trend line?
- A.
No, it appears to have random variation only.
- B.
Yes, it appears to have seasonal variation that needs to be deseasonalised
- C.
No, it appears to have cyclic variation which requires smoothing
- D.
Yes, it appears to have cyclic variation
Correct Answer
A. No, it appears to have random variation only.Explanation
The correct answer is "No, it appears to have random variation only." This is because the time series plot does not show any clear patterns or repetitive cycles. The data seems to fluctuate randomly without any noticeable seasonal or cyclic variations. Therefore, there is no need to deseasonalize the data before calculating the trend line.Rate this question:
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- 13.
Using the Deseasonalised data provided in the the table above, what was the raw (seasonalised) value for Monday of Week 3? (Whole Numbers)
Correct Answer
75 - 14.
Using the Deseasonalised data provided in the table above, what was the raw (seasonalised) value for Tuesday of Week 3? (Whole Numbers)
Correct Answer
78 - 15.
Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Wednesday of Week 3? (Whole Numbers)
Correct Answer
102Explanation
The raw (seasonalised) value for Wednesday of Week 3 is 102.Rate this question:
- 16.
Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Thursday of Week 3? (Whole Numbers)
Correct Answer
81Explanation
The raw (seasonalised) value for Thursday of Week 3 is 81.Rate this question:
- 17.
Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Friday of Week 3? (Whole Numbers)
Correct Answer
100 - 18.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 6? (Whole Numbers)
Correct Answer
78Explanation
The raw (seasonalised) value for Coded Day 6 is 78.Rate this question:
- 19.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 7? (Whole Numbers)
Correct Answer
83Explanation
The raw (seasonalised) value for Coded Day 7 is 83.Rate this question:
- 20.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 8? (Whole Numbers)
Correct Answer
82Explanation
The raw (seasonalised) value for Coded Day 8 is 82.Rate this question:
- 21.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 9? (Whole Numbers)
Correct Answer
87Explanation
The raw (seasonalised) value for Coded Day 9 is 87.Rate this question:
- 22.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 10? (Whole Numbers)
Correct Answer
106Explanation
Based on the information given, the raw (seasonalized) value for Coded Day 10 is 106.Rate this question:
- 23.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 11? (Whole Numbers)
Correct Answer
92 - 24.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 12? (Whole Numbers)
Correct Answer
91Explanation
The raw (seasonalized) value for Coded Day 12 is 91.Rate this question:
- 25.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 13? (Whole Numbers)
Correct Answer
106Explanation
The raw (seasonalised) value for Coded Day 13 is 106.Rate this question:
- 26.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 14? (Whole Numbers)
Correct Answer
79Explanation
The raw (seasonalised) value for Coded Day 14 is 79.Rate this question:
- 27.
Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 15? (Whole Numbers)
Correct Answer
97Explanation
The raw (seasonalised) value for Coded Day 15 is 97.Rate this question:
- 28.
Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Monday of Week 2? (Whole Numbers)
Correct Answer
95 - 29.
Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Tuesday of Week 2? (Whole Numbers)
Correct Answer
98 - 30.
Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Friday of Week 2? (Whole Numbers)
Correct Answer
100 - 31.
Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Thursday of Week 2? (Whole Numbers)
Correct Answer
81Explanation
The raw (seasonalised) value for Thursday of Week 2 is 81.Rate this question:
- 32.
Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Wednesday of Week 2? (Whole Numbers)
Correct Answer
108Explanation
The raw (seasonalised) value for Wednesday of Week 2 is 108.Rate this question:
- 33.
For the above seasonalised data, write the equation (using x & y variables) you would use to predict the value for Coded time 15. (2 decimal places)
Correct Answer
y=103.62-1.49xExplanation
The equation y = 103.62 - 1.49x represents a linear regression model for predicting the value of y (the dependent variable) based on x (the independent variable). In this case, x represents the coded time and y represents the predicted value. The coefficient -1.49 indicates that for every unit increase in x, y is expected to decrease by 1.49. The intercept term 103.62 represents the estimated value of y when x is equal to 0. Therefore, to predict the value for Coded time 15, we would substitute x = 15 into the equation and calculate the corresponding value of y.Rate this question:
- 34.
For the above seasonalised data, write the equation (using x & y variables) you would use to predict the value for Coded time 15. (2 decimal places)
Correct Answer
y=96.32 - 0.59xExplanation
The equation y=96.32 - 0.59x represents a linear regression model where y is the predicted value and x is the coded time. In this case, the equation suggests that for every unit increase in the coded time, the predicted value decreases by 0.59 units. The constant term of 96.32 represents the predicted value when the coded time is 0. Therefore, to predict the value for coded time 15, we substitute x=15 into the equation, resulting in y=96.32 - 0.59(15) = 87.77.Rate this question:
- 35.
For the above seasonalised data, write the equation you would use to predict the value for Coded time 15. (2 decimal places)
Correct Answer
y=97.28-0.54xExplanation
The equation y=97.28-0.54x represents a linear regression model where y is the predicted value for the dependent variable, and x is the coded time. The constant term 97.28 represents the y-intercept, indicating the predicted value of y when x is zero. The coefficient -0.54 represents the slope of the line, indicating the change in y for each unit increase in x. Therefore, to predict the value for Coded time 15, we substitute x=15 into the equation and solve for y.Rate this question:
- 36.
For the above seasonalised data, write the equation (using x & y variables) you would use to predict the value for Coded time 20. (2 decimal places)
Correct Answer
y=108.34-1.45xExplanation
The equation y=108.34-1.45x represents a linear regression model, where y is the predicted value for the dependent variable and x is the coded time. The coefficient -1.45 suggests that for each unit increase in the coded time, the predicted value for y decreases by 1.45. The constant term 108.34 represents the predicted value for y when the coded time is 0. Therefore, to predict the value for coded time 20, we substitute x=20 into the equation and solve for y.Rate this question:
- 37.
For the above seasonalised data, write the equation (using x & y variables) you would use to predict the value for Coded time 20. (2 decimal places)
Correct Answer
y=98.46-1.16xExplanation
The equation y=98.46-1.16x is a linear regression equation that can be used to predict the value of y (the dependent variable) based on the value of x (the independent variable). In this case, the equation can be used to predict the value for Coded time 20. The coefficient -1.16 indicates that for every unit increase in x, y is expected to decrease by 1.16 units. The constant term 98.46 represents the predicted value of y when x is 0. Therefore, by plugging in the value of x as 20, we can calculate the predicted value of y.Rate this question:
- 38.
The table above shows the raw data collected from a call centre relating to the number of calls received per day. For the above data, predict the raw value for Coded time 13. (nearest whole number) - Hint (deseasonalise, forecast using deseasonalised equation, reseasonalise)
Correct Answer
88 - 39.
The table above shows the raw data collected from a call centre relating to the number of calls received per day. For the above data, predict the raw value for Coded time 15. (nearest whole number) - Hint (deseasonalise, forecast using deseasonalised equation, reseasonalise)
Correct Answer
68 - 40.
The table above shows the raw data collected from a call centre relating to the number of calls received per day. For the above data, predict the raw value for Coded time 14. (nearest whole number) - Hint (deseasonalise, forecast using deseasonalised equation, reseasonalise)
Correct Answer
82 - 41.
The table above shows the raw data collected from a call centre relating to the number of calls received per day. For the above data, predict the raw value for Coded time 16. (nearest whole number) - Hint (deseasonalise, forecast using deseasonalised equation, reseasonalise)
Correct Answer
89 - 42.
The table above shows the raw data collected from a call centre relating to the number of calls received per day. For the above data, predict the raw value for Coded time 17. (nearest whole number) - Hint (deseasonalise, forecast using deseasonalised equation, reseasonalise)
Correct Answer
83 - 43.
The table above shows the raw data collected from a call centre relating to the number of calls received per day. For the above data, predict the raw value for Coded time 18. (nearest whole number) - Hint (deseasonalise, forecast using deseasonalised equation, reseasonalise)
Correct Answer
78Explanation
Based on the information given, the table shows the raw data collected from a call centre relating to the number of calls received per day. The question asks to predict the raw value for Coded time 18. Since there is no additional information or context provided, it is not possible to determine how the raw value for Coded time 18 was calculated or predicted. Therefore, an explanation for the given answer of 78 cannot be provided.Rate this question:
- 44.
The table above shows the raw data collected from a call centre relating to the number of calls received per day. For the above data, predict the raw value for Coded time 19. (nearest whole number) - Hint (deseasonalise, forecast using deseasonalised equation, reseasonalise)
Correct Answer
64Explanation
Based on the given hint, the process involves deseasonalizing the data, forecasting using the deseasonalized equation, and then reseasonalizing the forecasted value. However, without any further information or data provided, it is not possible to determine the exact steps or calculations involved in reaching the answer of 64.Rate this question:
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Mar 14, 2023Quiz Edited by
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Apr 27, 2014Quiz Created by
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