Consider the following time series:Quarter Year 1 Year 2 Year 31696660244364636062554798273(a) Choose a time series plot.(i)100806040203.467Period(t)9101112(ii)100806040201 23.467Period (t)101112 Graph (ii)What type of pattern exists in the data? Is there an indication of a seasonal pattern?Horizontal pattern, with seasonality(b) Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtri = 1 if quarter 1, 0 otherwise; Qtrz = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0otherwise. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)QtriO Otrz +O Qtr3(c) Compute the quarterly forecasts for next year.Year QuarterFt4144344

Answered: Image /qna-images/answer/a289b56a-8448-46d6-b1f6-217100511e11.jpg

Answered: Consider the following time series:…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter5: Inverse, Exponential, And Logarithmic Functions

Section5.6: Exponential And Logarithmic Equations

Problem 6E

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Consider the following time series data. Choose the correct time series plot. (i) (ii) (iii) (iv) Plot (ii) What type of pattern exists in the data?Horizontal Pattern Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7. If required, round your answers to two decimal places. Week Time SeriesValue Forecast 1 18 2 13 3 16 4 11 fill in the blank 3 5 17 fill in the blank 4 6 14 fill in the blank 5 MSE: fill in the blank 6The forecast for week 7: fill in the blank 7 Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7. If required, round your answers to two decimal places. Week Time SeriesValue Forecast 1 18 2 13 fill in the blank 8 3 16 fill in the blank 9 4 11 fill in the blank 10 5 17 fill in the blank 11 6 14 fill in the blank 12 MSE: fill in the blank 13The forecast for week 7: fill in the blank 14
What type of variable is required when drawing a time-series plot? Why do we draw time-series plots? A is required when drawing a time-series plot. discrete variable quantitative variable at the interval level continuous variable quantitative variable at the ratio level qualitative variable qualitative variable at the ordinal level categorical variable quantitative variable qualitative variable at the nominal level
Answer subparts b,c,and e.Solve accurately.
For each of the below ACF plots which are obtained for a time series data of 4 different variables of interest: a) Explain the ACF plot. b) Describe what the raw data is likely to look like over time. c) What kind of variable is this plot likely to characterise (e.g., stock prices, exchange rates, temperature, etc.)?
Consider the following time series data. Month 1 2 3 4 5 6 7 Value 25 14 21 13 20 24 16 (a) Construct a time series plot. -A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 8 to 20 on the vertical axis. The plot reaches its maximum time series value at month 1. -A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 8 to 20 on the vertical axis. The plot reaches its maximum time…
Consider the following time series data: Month 1 2 3 4 5 6 7 Value 23 15 20 12 18 22 15 (a) Choose the correct time series plot. (i) (ii) (iii) (iv) What type of pattern exists in the data? (b) Develop a three-month moving average for this time series. Compute MSE and a forecast for month 8. If required, round your answers to two decimal places. Do not round intermediate calculation. MSE: The forecast for month 8: (c) Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for month 8. If required, round your answers to two decimal places. Do not round intermediate calculation. MSE: The forecast for month 8: (d) Compare the three-month moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE? (e) Use trial and error to find a value of the exponential smoothing…
Consider the following time series data. a. Show the four-quarter and centered moving average values for this time series (to 3 decimals if necessary). b. Compute seasonal indexes and adjusted seasonal indexes for the four quarters (to 3 decimals).
group of observations measured at successive time intervals is known as a(n) _____. a. forecast b. trend component c. additive time series model d. time series
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13 15 13 15 15 (a) Choose the correct time series plot. (i) (ii) (iii) (iv) What type of pattern exists in the data? (b) Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7. Do not round intermediate calculations. If required, round your answers to two decimal places. Week Value Forecast 1 18 2 13 3 15 4 13 5 15 6 15 MSE: The forecast for week 7: (c) Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7. Do not round intermediate calculations. If required, round your answers to two decimal places. Week Value Forecast 1 18 2 13 3 15 4 13 5 15 6 15 MSE: The forecast for week 7: (d) Compare the three-week moving average forecast with the…
The time series pattern that exists when the data fluctuate around a constant mean is the ______________. A. horizontal pattern. B. seasonal pattern. C. cyclical pattern. D. trend pattern.
Question 2 For each of the below ACF plots which are obtained for a time series data of 4 different variables of interest:a) Explain the ACF plot. b) Describe what the raw data is likely to look like over time. c) What kind of variable is this plot likely to characterise (e.g., stock prices, exchange rates, temperature, etc.)?
(a) Compute MSE using the most recent value as the forecast for the next period. What is the forecast for month 8? The MSE, mean squared error, is a measure of forecast accuracy and is the average of the squared forecast errors. Before this can be found, we first need to find the forecast values, forecast error, and squared forecast errors. Forecasts will be made using the most recent values known. There will be no forecast for the first month. The forecast value for month 2 will be the observed value from month 1, so the forecast value for month 2 is The forecast value for month 3 will be the observed value from month This pattern will continue for the rest of the months. 2, so the forecast value for month 3 is Complete the column for the forecast value in the table below. Month Value Forecast Value 1 18 2 26 3 22 4 27 5 23 9 22 22 7 27 27 Find the forecast for month 8 using this pattern. Type here to search L W ☑ 3 8 200m 3:36 PM 72°F 5/9/2024 10

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Consider the following time series: Quarter Year 1 Year 2 Year 3 69 66 60 44 36 46 60 62 55 4 79 82 73 (a) Choose a time series plot. (1) 100 80 60 40 20 0 ++ + 1 2 3. 4 6 7 Period (t) 10 11 12 (ii) 100 80 60 40 20 4 5 6 9 10 11 12