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 67E

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The data for this problem are the air freight shipments (in ton-miles) each month (beginning in January for one year) in the U. S. for 10 years. The following is a time series plot of this data. What are the most important features of this plot? Ton-miles 3000- 2500- 2000- 1500- 1000- 1 Time Series Plot of Ton-miles ܚ ܪܘܐܐܐ ܘ ܚܐ ܀ 12 24 36 48 60 Index 72 84 96 108 120
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
Table 7 shows the seasonal factors for a monthly time series: Table 7 Month Seasonal Month Factor January -358.1 May February -196.3 June March 12.5 July April 187.4 August Describe the seasonal variation in this time series. Seasonal Factor 487.2 276.1 -27.9 -124.6 Month September October November December Seasonal Factor -136.7 92.6 70.4 -282.6
Answer subparts b,c,and e.Solve accurately.
S. The numbers of worldwide airline fatalities are given below. Use a time series chart to display the data. Year 1988 1989 1990 1991 1992 1993 1994 No, of fatalities 699 817 440 510 990 801 732
Please draw a time series graph for the information displayed,‘s thank you!!
Find the MSE in part (b) please.
Please answer parts b and c
Solve the following parts (a) and (b)
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 12 19 11 18 22 14 (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 6 to 18 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 11 to 23 on the vertical axis. The plot reaches its maximum time…

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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