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- Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?consider the following time series data.Month 1 2 3 4 5 6 7Value 24 13 20 12 19 23 15a. compute MSe using the most recent value as the forecast for the next period. Whatis the forecast for month 8?b. compute MSe using the average of all the data available as the forecast for the nextperiod. What is the forecast for month 8?c. Which method appears to provide the better forecast?Which of the following time series forecasting methods would not be used to forecast seasonal data?
- Develop seasonal factors from the table below by following a 3-MA values for the time series data t 1 2 3 4 5 6 7 8 9 y 13 18 13 14 13 16 14 21 13Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 Construct a time series plot. What type of pattern exist in the data? Develop a three-week moving average for this time series. Compute MSE and forecast for week 7. Use a = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and forecast for week 7.The following plots have been obtained for a time series. a) Suggest an appropriate ARIMA model. b) The following ARIMA output has been obtained from R. Based on this output, which model would you recommend for forecasting?
- 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).1. Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.Consider the following time series data Month 1 2 3 4 5 6 7 8 9 10 11 12 Value 90 89 86 91 90 91 88 86 91 93 90 88 a) Construct a time series plot in Excel. Label the axes and graph. c) Develop a smoothing model forecast with α = 0.35. Compute MSE and forecast for month 13 d) Which model is the better predictor? Why?
- Consider the following time series data: 1 2 3 4 5 6 7 26 15 22 14 21 25 17 PART 1.Compute MSE using the most recent value as the forecast for the next period and then calculate the forecast for month 8. PART 2.Compute MSE using the average of all the data available as the forecast for the next period. What is the forecast for month 8?consider the following time series data.t 1 2 3 4 5 6 7yt10 9 7 8 6 4 4a. construct a time series plot. What type of pattern exists in the data?b. develop the linear trend equation for this time series.c. What is the forecast for t = 8?Below you are given the first five values of a quarterly time series. The multiplicative model is appropriate and a four-quarter moving average will be used. Year Quarter Time Series Value Yt 1 1 36 2 24 3 16 2 4 20 1 44 An estimate of the combined trend-cycle component (T2Ct) for Quarter 3 of Year 1 (used for estimating the de-trended values), when a four-quarter moving average is used, is a. 24. b. 26. c. 28. d. 25.
