2759 Fitting Trend Lines And Forecasting (Seasonal Data)

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  • IEEE 1849
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| By Anthony Nunan
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Anthony Nunan
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Quizzes Created: 132 | Total Attempts: 47,819
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1) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 8? (Whole Numbers)

Explanation

The raw (seasonalised) value for Coded Day 8 is 82.

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About This Quiz
2759 Fitting Trend Lines And Forecasting (Seasonal Data) - Quiz

This quiz titled '2759 Fitting Trend Lines and Forecasting (Seasonal Data)' evaluates understanding of time series analysis, focusing on the necessity of deseasonalizing data before trend calculation. It tests skills in identifying seasonal versus cyclic variations, essential for accurate data forecasting.

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2) For the above seasonalised data, write the equation you would use to predict the value for Coded time 15. (2 decimal places)

Explanation

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.

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3) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 9? (Whole Numbers)

Explanation

The raw (seasonalised) value for Coded Day 9 is 87.

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4) 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)

Explanation

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.

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5) 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)

Explanation

not-available-via-ai

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6) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 10? (Whole Numbers)

Explanation

Based on the information given, the raw (seasonalized) value for Coded Day 10 is 106.

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7) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 11? (Whole Numbers)

Explanation

not-available-via-ai

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8) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 12? (Whole Numbers)

Explanation

The raw (seasonalized) value for Coded Day 12 is 91.

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9) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 13? (Whole Numbers)

Explanation

The raw (seasonalised) value for Coded Day 13 is 106.

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10) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 14? (Whole Numbers)

Explanation

The raw (seasonalised) value for Coded Day 14 is 79.

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11) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 15? (Whole Numbers)

Explanation

The raw (seasonalised) value for Coded Day 15 is 97.

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12) 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)

Explanation

not-available-via-ai

Submit
13) 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)

Explanation

not-available-via-ai

Submit
14) From the time series plot above, should you deseasonalise the data before calculating the trend line?

Explanation

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.

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15) From the time series plot above, should you deseasonalise the data before calculating the trend line?

Explanation

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.

Submit
16) 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)

Explanation

not-available-via-ai

Submit
17) 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)

Explanation

not-available-via-ai

Submit
18) 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)

Explanation

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.

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19) 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)

Explanation

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.

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20) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 6? (Whole Numbers)

Explanation

The raw (seasonalised) value for Coded Day 6 is 78.

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21) Using the Deseasonalised data from the table above, what was the raw (seasonalised) value for Coded Day 7? (Whole Numbers)

Explanation

The raw (seasonalised) value for Coded Day 7 is 83.

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22) From the time series plot above, should you deseasonalise the data before calculating the trend line?

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.

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23) From the time series plot above, should you deseasonalise the data before calculating the trend line?

Explanation

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.

Submit
24) From the time series plot above, should you deseasonalise the data before calculating the trend line?

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.

Submit
25) From the time series plot above, should you deseasonalise the data before calculating the trend line?

Explanation

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.

Submit
26) From the time series plot above, should you deseasonalise the data before calculating the trend line?

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.

Submit
27) From the time series plot above, should you deseasonalise the data before calculating the trend line?

Explanation

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.

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28) From the time series plot above, should you deseasonalise the data before calculating the trend line?

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.

Submit
29) From the time series plot above, should you deseasonalise the data before calculating the trend line?

Explanation

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.

Submit
30) From the time series plot above, should you deseasonalise the data before calculating the trend line?

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.

Submit
31) From the time series plot above, should you deseasonalise the data before calculating the trend line?

Explanation

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.

Submit
32) 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)

Explanation

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.

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33) Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Monday of Week 2? (Whole Numbers)

Explanation

not-available-via-ai

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34) Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Tuesday of Week 2? (Whole Numbers)

Explanation

not-available-via-ai

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35) Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Friday of Week 2? (Whole Numbers)

Explanation

not-available-via-ai

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36) Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Thursday of Week 2? (Whole Numbers)

Explanation

The raw (seasonalised) value for Thursday of Week 2 is 81.

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37) Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Wednesday of Week 2? (Whole Numbers)

Explanation

The raw (seasonalised) value for Wednesday of Week 2 is 108.

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38) 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)

Explanation

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.

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39) 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)

Explanation

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.

Submit
40) Using the Deseasonalised data provided in the the table above, what was the raw (seasonalised) value for Monday of Week 3? (Whole Numbers)

Explanation

not-available-via-ai

Submit
41) Using the Deseasonalised data provided in the table above, what was the raw (seasonalised) value for Tuesday of Week 3? (Whole Numbers)

Explanation

not-available-via-ai

Submit
42) Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Wednesday of Week 3? (Whole Numbers)

Explanation

The raw (seasonalised) value for Wednesday of Week 3 is 102.

Submit
43) Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Thursday of Week 3? (Whole Numbers)

Explanation

The raw (seasonalised) value for Thursday of Week 3 is 81.

Submit
44) Using the Deseasonalised data from the table above, what is the raw (seasonalised) value for Friday of Week 3? (Whole Numbers)

Explanation

not-available-via-ai

Submit
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Using the Deseasonalised data from the table above, what was the raw...
For the above seasonalised data, write the equation you would use to...
Using the Deseasonalised data from the table above, what was the raw...
For the above seasonalised data, write the equation (using x & y...
The table above shows the raw data collected from a call centre...
Using the Deseasonalised data from the table above, what was the raw...
Using the Deseasonalised data from the table above, what was the raw...
Using the Deseasonalised data from the table above, what was the raw...
Using the Deseasonalised data from the table above, what was the raw...
Using the Deseasonalised data from the table above, what was the raw...
Using the Deseasonalised data from the table above, what was the raw...
The table above shows the raw data collected from a call centre...
The table above shows the raw data collected from a call centre...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
The table above shows the raw data collected from a call centre...
The table above shows the raw data collected from a call centre...
The table above shows the raw data collected from a call centre...
The table above shows the raw data collected from a call centre...
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Using the Deseasonalised data from the table above, what was the raw...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
From the time series plot above, should you deseasonalise the data...
For the above seasonalised data, write the equation (using x & y...
Using the Deseasonalised data from the table above, what is the raw...
Using the Deseasonalised data from the table above, what is the raw...
Using the Deseasonalised data from the table above, what is the raw...
Using the Deseasonalised data from the table above, what is the raw...
Using the Deseasonalised data from the table above, what is the raw...
For the above seasonalised data, write the equation (using x & y...
For the above seasonalised data, write the equation (using x & y...
Using the Deseasonalised data provided in the the table above, what...
Using the Deseasonalised data provided in the table above, what was...
Using the Deseasonalised data from the table above, what is the raw...
Using the Deseasonalised data from the table above, what is the raw...
Using the Deseasonalised data from the table above, what is the raw...
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