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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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
Please answer number 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.)?
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).
Q. 3 We are given the following data for a city: Population of the city on March 30, 2020 = 183,000 Number of new active cases of TB occurring between January 1 and June 30, 2020 26 Number of active TB cases according to the city register on January 1, 2020 = 264 Find the (i) Cumulative incidence of active cases of TB for the 6-month period3; (ii) Prevalence of active TB as of June 30, 2020.
Q. 3 We are given the following data for a city: Population of the city on March 30, 2020 = 183,000 Number of new active cases of TB occurring between January 1 and June 30, 2020 = 26 Number of active TB cases according to the city register on January 1, 2020 = 264 Find the (i) Cumulative incidence of active cases of TB for the 6-month period; (ii) Prevalence of active TB as of June 30, 2020.
Use Table 12-2 to calculate the present value (in $) of the ordinary annuity. (Round your answer to the nearest cent.) Nominal Annuity Payment Payment Frequency Time Interest Present Value Period (years) Rate (%) Compounded of the Annuity $4,000 every year 20 4 annually
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…
Please answer part a and b
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.)?

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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