Define the residual method of estimating irregular components in a time series ?
Q: 1. Consider the following time series: a. Construct a time series plot. What type of pattern exists…
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Q: Consider the following time series. a. Construct a time series plot. What type of pattern exists in…
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Q: 15 Explain the semi average method of trend estimation in a time series and give its merits and…
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- An article in Quality Engineering presents viscosity data from a batch chemical process. A sample of these data is in the table. Reading left to right and up to down, draw a time series plot of all the data and comment on any features of the data that are revealed by this plot. Consider that the first 40 observations (the first 4 columns) were generated from a specific process, whereas the last 40 observations were generated from a different process. Does the plot indicate that the two processes generate similar results? Calculate the sample mean and sample variance of the first 40 and the second 40 observations.9. What are the time series characteristics you can observe in the graph below? And how to capture them in your model? Monthly milk production: pounds per cow. Jan 62 - Dec 75 АЛЛЛЛЛЛЛЛЛЛЛлл 900 800 700 600 1962-01 data 1963-09 1965-05 1967-01 1968-09 time 1970-05 1972-01 1973-09 1975-05Vehicles arrive at a car detailing service at a rate of 10 per minute according to a Poissondistribution. For simplicity, assume that there is only one lane and one worker who can serve atan average rate of 12 vehicles per minute. Service times are exponentially distributed. Part A: What is the average length of the queue? Part B: What is the average time a vehicle must spend to get through the system? Part C: What is the utilization rate of the worker? Part D: What is the probability that when you arrive at the shop, there will be three ormore vehicles ahead of you?
- 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 aboveQ. 14 Explain the Graphic method of trend estimation in a time series and give its merits and demerits.The figure below illustrates monthly data over 10 years. What method would you expect would perform the best at forecasting the data series in period 121? Explain the (i) strengths of your selected method for this data and the (ii) weaknesses of alternative methods for this data. Consider the concepts of level, seasonality, trend, and noise in your answer.
- 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.Relate cycles, seasonality and trends in time series What is their relevance in such type of forecasting?Define on empirical way and a statistical way to choose the best forecasting model for your time series data. Consider the following situations: 1. The data has no trend or seasonality 2. The data has trend but not seasonality 3. The data presents trend and seasonality but negligible noise 4. The data presents trend, seasonality and significant noise.
- Q.2. What is the use of differencing in a time series? Also, explain the importance of stationarity.When a time series is stationary, the appropriate forecasting technique(s) is (are). Multiple Cholce O simple exponential smoothing only simple moving averages or simple exponential smoothing simple moving averages only regression analysis or seasonal indexing
