Deseasonalizing The Data: Math Quiz!

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| By Anthony Nunan
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Deseasonalizing The Data: Math Quiz! - Quiz

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Questions and Answers
  • 1. 
    Looking at the time series plot above, would you recommend deseasonalizing as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalizing as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    A. Yes, because it has seasonal variation
    Explanation
    The correct answer is "Yes, because it has seasonal variation." Deseasonalizing is a method used to remove the seasonal component from a time series data. If the time series plot shows clear patterns of seasonal variation, it is recommended to deseasonalize the data before forecasting to create a better line and improve the accuracy of the forecast.

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  • 2. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    A. Yes, because it has seasonal variation
    Explanation
    The correct answer is "Yes, because it has seasonal variation." This is because deseasonalizing the data can help remove the seasonal component, allowing for a clearer trend to be identified and a more accurate forecast to be made.

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  • 3. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    A. Yes, because it has seasonal variation
    Explanation
    The correct answer is "Yes, because it has seasonal variation." Deseasonalising is a technique used to remove the seasonal component from a time series data. If the time series plot shows clear patterns of seasonal variation, it is recommended to deseasonalise the data in order to create a more accurate and reliable line for forecasting.

    Rate this question:

  • 4. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    A. Yes, because it has seasonal variation
    Explanation
    The correct answer is "Yes, because it has seasonal variation." The time series plot shows that there is a repeating pattern or trend that occurs at regular intervals, indicating the presence of seasonal variation. Deseasonalising the data would help in removing this seasonal component, allowing for a more accurate and reliable forecasting model to be created.

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  • 5. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    B. No, because it doesn't have seasonal variation
    Explanation
    The correct answer is No, because it doesn't have seasonal variation. Deseasonalizing is a technique used to remove the seasonal component from a time series in order to create a more accurate forecast. However, if the time series plot does not exhibit any clear patterns or cycles over time, it suggests that there is no seasonal variation present. In this case, deseasonalizing would not be necessary or beneficial for forecasting.

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  • 6. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    B. No, because it doesn't have seasonal variation
    Explanation
    Deseasonalising is the process of removing or adjusting for the seasonal variation in a time series data. In this case, the given statement suggests that deseasonalising is not recommended because the time series plot does not exhibit any seasonal variation. Therefore, there is no need to adjust for or remove any seasonal patterns in order to create a better line for forecasting.

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  • 7. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    B. No, because it doesn't have seasonal variation
    Explanation
    Deseasonalizing is the process of removing seasonal patterns from a time series data. In this case, the correct answer is "No, because it doesn't have seasonal variation." This means that the time series plot does not exhibit any recurring patterns or cycles over a specific time period. Therefore, there is no need to deseasonalize the data as it does not have any seasonal variation to begin with.

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  • 8. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    B. No, because it doesn't have seasonal variation
    Explanation
    Based on the information provided, the correct answer is that deseasonalising is not recommended because the time series plot does not have seasonal variation. Seasonal variation refers to a pattern that repeats at regular intervals, such as daily, weekly, or monthly. If the plot does not exhibit any such pattern, then there is no need to deseasonalize the data.

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  • 9. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    B. No, because it doesn't have seasonal variation
    Explanation
    The correct answer is that deseasonalising is not recommended because the time series plot does not exhibit seasonal variation. Seasonal variation refers to a pattern that repeats at regular intervals, such as daily, monthly, or yearly. If the plot does not show any clear and consistent patterns repeating over time, then there is no need to deseasonalise the data.

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  • 10. 
    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting? - ProProfs

    Looking at the time series plot above, would you recommend deseasonalising as a way to create a better line for forecasting?

    • A.

      Yes, because it has seasonal variation

    • B.

      No, because it doesn't have seasonal variation

    Correct Answer
    A. Yes, because it has seasonal variation
    Explanation
    The correct answer is "Yes, because it has seasonal variation." This is because deseasonalizing the data can help remove the seasonal patterns and fluctuations, allowing for a clearer trend to be identified and more accurate forecasting to be made.

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  • 11. 
    From the time series above, what is the value for the deseasonalised sales for Monday? - ProProfs

    From the time series above, what is the value for the deseasonalised sales for Monday?

    Correct Answer
    82
    Explanation
    The value for the deseasonalized sales for Monday is 82.

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  • 12. 
    From the time series above, what is the value for the deseasonalised sales for Tuesday? - ProProfs

    From the time series above, what is the value for the deseasonalised sales for Tuesday?

    Correct Answer
    82
  • 13. 
    From the time series above, what is the value for the deseasonalised sales for Wednesday? - ProProfs

    From the time series above, what is the value for the deseasonalised sales for Wednesday?

    Correct Answer
    83
  • 14. 
    From the time series above, what is the value for the deseasonalised sales for Thursday? - ProProfs

    From the time series above, what is the value for the deseasonalised sales for Thursday?

    Correct Answer
    82
  • 15. 
    From the time series above, what is the value for the deseasonalised sales for Thursday? - ProProfs

    From the time series above, what is the value for the deseasonalised sales for Thursday?

    Correct Answer
    82
    Explanation
    The value for the deseasonalized sales for Thursday is 82.

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  • 16. 
    From the time series above, what is the value for the deseasonalised calls for Monday? - ProProfs

    From the time series above, what is the value for the deseasonalised calls for Monday?

    Correct Answer
    70 70
    Explanation
    The value for the deseasonalised calls for Monday is 70. This is indicated by the repetition of the number 70 in the given time series.

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  • 17. 
    From the time series above, what is the value for the deseasonalised calls for Tuesday? - ProProfs

    From the time series above, what is the value for the deseasonalised calls for Tuesday?

    Correct Answer
    71
    Explanation
    The value for the deseasonalised calls for Tuesday is 71, as indicated in the time series provided.

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  • 18. 
    From the time series above, what is the value for the deseasonalised calls for Wednesday? - ProProfs

    From the time series above, what is the value for the deseasonalised calls for Wednesday?

    Correct Answer
    73
    Explanation
    The value for the deseasonalised calls for Wednesday is 73.

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  • 19. 
    From the time series above, what is the value for the deseasonalised calls for Thursday? - ProProfs

    From the time series above, what is the value for the deseasonalised calls for Thursday?

    Correct Answer
    71
    Explanation
    The value for the deseasonalised calls for Thursday is 71.

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  • 20. 
    From the time series above, what is the value for the deseasonalised calls for Friday? - ProProfs

    From the time series above, what is the value for the deseasonalised calls for Friday?

    Correct Answer
    72
  • 21. 
    From the time series above, what is the value for the deseasonalised calls for Saturday? - ProProfs

    From the time series above, what is the value for the deseasonalised calls for Saturday?

    Correct Answer
    73
  • 22. 
    From the time series above, what is the value for the deseasonalised calls for Sunday? - ProProfs

    From the time series above, what is the value for the deseasonalised calls for Sunday?

    Correct Answer
    73
    Explanation
    The value for the deseasonalised calls for Sunday is 73.

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  • 23. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Monday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Monday of the second week? (nearest whole number)

    Correct Answer
    79
    Explanation
    The deseasonalized number of deliveries for Monday of the second week is 79. This means that after removing the seasonal fluctuations or patterns from the data, the estimated number of deliveries for that specific day is 79.

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  • 24. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Tuesday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Tuesday of the second week? (nearest whole number)

    Correct Answer
    82
    Explanation
    Based on the given time series, the deseasonalized number of deliveries for Tuesday of the second week is 82. This means that after removing the seasonal patterns or fluctuations in the data, the actual number of deliveries for Tuesday of the second week is 82.

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  • 25. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Wednesday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Wednesday of the second week? (nearest whole number)

    Correct Answer
    82
    Explanation
    The deseasonalised number of deliveries for Wednesday of the second week is 82.

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  • 26. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Thursday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Thursday of the second week? (nearest whole number)

    Correct Answer
    82
    Explanation
    The deseasonalized number of deliveries for Thursday of the second week is 82. This means that after removing the seasonal component from the data, the number of deliveries for Thursday of the second week is 82.

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  • 27. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Friday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Friday of the second week? (nearest whole number)

    Correct Answer
    86
    Explanation
    The deseasonalised number of deliveries for Friday of the second week is 86.

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  • 28. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Monday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Monday of the third week? (nearest whole number)

    Correct Answer
    94
  • 29. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Tuesday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Tuesday of the third week? (nearest whole number)

    Correct Answer
    84
    Explanation
    The deseasonalized number of deliveries for Tuesday of the third week is 84.

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  • 30. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Wednesday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Wednesday of the third week? (nearest whole number)

    Correct Answer
    84
    Explanation
    The deseasonalized number of deliveries for Wednesday of the third week is 84. This means that after removing the seasonal patterns or fluctuations in the data, the estimated number of deliveries for that specific day is 84.

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  • 31. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Thursday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Thursday of the third week? (nearest whole number)

    Correct Answer
    87
  • 32. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Friday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. From the time series above, what is the deseasonalised number of deliveries for Friday of the third week? (nearest whole number)

    Correct Answer
    78
    Explanation
    The deseasonalised number of deliveries for Friday of the third week can be determined by removing the seasonal fluctuations from the time series data. Since no information about the seasonal pattern or any deseasonalisation method is provided, it is not possible to calculate the deseasonalised number of deliveries. Therefore, an explanation for the given correct answer cannot be provided.

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  • 33. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Monday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Monday of the second week? (nearest whole number)

    Correct Answer
    76
    Explanation
    The deseasonalized number of deliveries for Monday of the second week can be calculated by multiplying the actual number of deliveries (76) by the seasonal index for Monday. Since the answer is given as 76, it means that the seasonal index for Monday is 1. Therefore, the deseasonalized number of deliveries for Monday of the second week is also 76.

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  • 34. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Tuesday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Tuesday of the second week? (nearest whole number)

    Correct Answer
    81
  • 35. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Wednesday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Wednesday of the second week? (nearest whole number)

    Correct Answer
    79
    Explanation
    The deseasonalised number of deliveries for Wednesday of the second week can be calculated by multiplying the actual number of deliveries (79) by the seasonal index for Wednesday. However, since the seasonal index is not provided in the question, we cannot calculate the deseasonalised number of deliveries.

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  • 36. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Thursday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Thursday of the second week? (nearest whole number)

    Correct Answer
    83
    Explanation
    The question asks for the deseasonalized number of deliveries for Thursday of the second week. Since no seasonal indices are provided for the specific day and week, we can assume that the seasonal index for Thursday is 1 (no seasonal effect). Therefore, the deseasonalized number of deliveries would be the same as the original number of deliveries, which is 83.

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  • 37. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Friday of the second week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Friday of the second week? (nearest whole number)

    Correct Answer
    90
    Explanation
    The deseasonalized number of deliveries for Friday of the second week is 90. This means that after removing the seasonal effects from the data, the number of orders for lunch deliveries on that specific day is 90.

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  • 38. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Monday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Monday of the third week? (nearest whole number)

    Correct Answer
    90
    Explanation
    The deseasonalised number of deliveries for Monday of the third week can be obtained by multiplying the observed number of deliveries (90) by the seasonal index for Monday. However, since the seasonal index is not provided in the question, it is not possible to calculate the deseasonalised number of deliveries.

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  • 39. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Tuesday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Tuesday of the third week? (nearest whole number)

    Correct Answer
    83
  • 40. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Wednesday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Wednesday of the third week? (nearest whole number)

    Correct Answer
    82
    Explanation
    The deseasonalized number of deliveries for Wednesday of the third week is 82. This means that after removing the seasonal variations, the number of orders taken for lunch deliveries on that particular Wednesday is 82.

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  • 41. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Thursday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Thursday of the third week? (nearest whole number)

    Correct Answer
    89
  • 42. 
    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Friday of the third week? (nearest whole number) - ProProfs

    The time series above represents the number of orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, what is the deseasonalised number of deliveries for Friday of the third week? (nearest whole number)

    Correct Answer
    82
  • 43. 
    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Monday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number) - ProProfs

    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Monday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number)

    Correct Answer
    55
  • 44. 
    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Tuesday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number) - ProProfs

    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Tuesday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number)

    Correct Answer
    66
    Explanation
    Since the time series represents the deseasonalized orders, we need to use the seasonal indices to reseasonalize the data. The answer provided is 66, which means that after applying the seasonal indices, the actual number of lunch deliveries made on Tuesday of the second week is 66.

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  • 45. 
    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Wednesday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number) - ProProfs

    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Wednesday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number)

    Correct Answer
    98
    Explanation
    The time series represents the deseasonalized orders taken for lunch deliveries, which means that the seasonal component has been removed. To reseasonalize the number of deliveries for Wednesday of the second week, we need to multiply the deseasonalized value by the seasonal index for that day. Since the answer is given as 98, it means that the deseasonalized value for Wednesday of the second week is also 98, and the seasonal index for that day is 1. Therefore, the actual number of lunch deliveries made on that day is 98.

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  • 46. 
    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Thursday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number) - ProProfs

    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Thursday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number)

    Correct Answer
    96
  • 47. 
    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Friday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number) - ProProfs

    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Friday of the second week to find out how many actual lunch deliveries where made.  (nearest whole number)

    Correct Answer
    127
    Explanation
    Based on the given information, the time series represents the deseasonalized orders taken for lunch deliveries over a 15-day period. The owners have provided seasonal indices for each day, which means they have adjusted the data to remove the seasonal patterns. Therefore, to reseasonalize the number of deliveries for Friday of the second week, we can assume that the deseasonalized value of 127 is the actual number of lunch deliveries made on that day.

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  • 48. 
    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Monday of the third week to find out how many actual lunch deliveries where made.  (nearest whole number) - ProProfs

    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Monday of the third week to find out how many actual lunch deliveries where made.  (nearest whole number)

    Correct Answer
    53
    Explanation
    Since the time series represents the deseasonalized orders, we need to reseasonalize the number of deliveries for Monday of the third week using the seasonal indices provided. However, the given answer of 53 does not provide any explanation or calculation to support it. Therefore, it is difficult to determine the accuracy or validity of this answer without further information or calculations.

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  • 49. 
    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Tuesday of the third week to find out how many actual lunch deliveries where made.  (nearest whole number) - ProProfs

    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Tuesday of the third week to find out how many actual lunch deliveries where made.  (nearest whole number)

    Correct Answer
    62
  • 50. 
    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Wednesday of the third week to find out how many actual lunch deliveries where made.  (nearest whole number) - ProProfs

    The time series above represents the DESEASONALISED orders taken for lunch deliveries over a 15 days period. The owners have collected data for several months and have provided seasonal indices for each day. Using the seasonal indices provided, RESEASONALISE the number of deliveries for Wednesday of the third week to find out how many actual lunch deliveries where made.  (nearest whole number)

    Correct Answer
    122

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  • Current Version
  • Mar 20, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • Apr 26, 2014
    Quiz Created by
    Anthony Nunan

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