Question 2The below output shows the results of an ARIMA model which was fitted to a time-seriesdata of Japan's GDP, and an excerpt of the data. Based on the below model andinformation:a) Explain what the time series is likely to look like (i.e., cyclical, seasonal, with a trend).b) Explain what is the predicted GDP value for Q4, 2021?Quarter, YearGDP (in trillions of dollars)Q1, 20215.5Q2, 20216.5Q3, 20217.0## Series: GDP## Model: ARIMA(1,1,0)## Coefficients:JELL##JELL##ar1 constant0.5 3

Answered: Image /qna-images/answer/9205ae5a-a6ab-41ec-86d3-28dd28830140.jpg

Answered: Question 2 The below output shows the…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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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.)?
The number of tourists who visit a coastal town is shown in the graph below. Number of tourists 16 000 15 000 14 000 13 000 12 000 11 000 10 000 9 000 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 ..ll. 2017 2017 2017 2017 2018 2018 2018 2018 Summer Autumn Winter Spring Summer Autumn Winter Spring Seasons/years Deseasonalise the time series data to remove any seasonal fluctuations, and display the results in a table.
Question 1: Find the trend for the time series using moving average method. Forecast Week 4 Wednesday Week Absence due to sickness Monday Tuesday Wednesday Thursday Friday 7 8 11 18 3 8 10 13 21 6. 13 17 28 2. 3,
q3 is for context, need help answering only the d section. Thank you in advance
question 6 only
150 30 0 www ii) 14 i) 21 ii) 28 days 35 42 (a) The Figure above shows simulated data on the number of people (in thousands) who use public transport every day in Melbourne. i) 49 56 Does this time series exhibit a time trend? If there is a time trend, explain what could explain this trend. (b) Consider the following estimated model of industrial production (Y), which uses quarterly data from 2001q1 to 2022q4 and includes a constant, a time trend (t) and 3 seasonal dummy variables (Q1, Q2, and Q3) in the equation. Does this time series exhibit seasonality? What statistical test could you perform to test whether there is seasonality? If there is seasonality, explain what could explain this seasonal pattern. log(Y) = 6.01 +0.05t -0.0201 -0.03Q2 -0.0103 Interpret the estimated coefficients for the time trend and three seasonal dummies. Based on the estimated equation above, what would be the forecast for industrial production for the first quarter of 2024?
5
QUESTION 8 Series A 300 250 200 150 100 50 1 2 3 4 5 6 7 8 9 10 11 12 13 Which forecasting method would be most appropriate for time series A? a. Winter's b. Holt's c. SARIMA
1. Which of the following is an example of time series problem? a. Estimating number of covid 19 patients in next 6 months. b. Estimating the total sales in next 3 years of an insurance company. c. Estimating your CGPA for Summer 2021. a) Only a b) Only b c) Only a & b d) All 3 of them 2. Which of the following can't be a component for a time series plot? a) Seasonality b) Trend c) Cyclical d) None of the above 3. If the demand is 100 during October 2016, 200 in November 2016, 300 in December 2016, 400 in January 2017. What is the 3-month simple moving average for February 2017? a) 300 b) 350 c) 400 d) Need more information 4. A regression equation for weight (y variable) and height (x variable) for 55 college students gave an error sum of squares (SSE) of 10.7 and a total sum of squares (SSTO) of 85.2. The proportion of variation explained by x, R³, is a) 11.2% b) 87.4% c) 88.6% d) None of the above
Discuss whether you believe time series to be an effective forecasting tool. Identify two of the most common mistakes made in completing a time series forecast and share an example of each mistake.
Question 3 A statistics instructor at a large western university would like to examine the relationship (if any) between the number of optional homework problems students do during the semester and their final course grade. She randomly selects 12 students for study and asks them to keep track of the number of these problems completed during the course of the semester. At the end of the class each student's total marks is recorded along with their final grade. The data follow in Table 2. Table 2 Problem Course Grade 62 51 58 62 68 66 65 68 76 77 66 67 72 73 72 78 73 76 78 78 84 85 91 75 Use the regression equation to preaict a student's final course grade if 75 optional homework assignments are done. ix. Use the regression equation to compute the number of optional homework assignments that need to be completed if a student expects an 85. vii.
Question 2: Find the trend for the time series using moving average method. Forecast for Year 4 quarter 4 Year Sales (000's) Quarter 1 Quarter 2 Quarter 3 Quarter 4 1 25 42 55 36 16 50 53 40 28 58 69 59 2N 3.

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