The file PI2_01.xlsx contains the monthly number of airline tickets sold by a travel agency. a. Does a linear trend appear to fit these data well? If so, estimate and interpret the linear trend model...

The file PI2_01.xlsx contains the monthly number of airline tickets sold by a travel agency. a. Does a linear trend appear to fit these data well? If so, estimate and interpret the linear trend model for this time series. Also, interpret the R2 and se values. b. Provide an indication of the typical forecast error generated by the estimated model in part a. c. Is there evidence of some seasonal pattern in these sales data? If so, characterize the seasonal pattern. 10. The file PI2_10.xlsx contains the daily closing prices of Walmart stock for a one-year period. Does a linear or exponential trend fit these data well? If so. estimate and interpret the best trend model for this time series. Also, interpret the R2 and se values. The file P12_11.xlsx contains monthly values of the U.S. national debt (in dollars) from 1993 to early 2010. Fit an exponential growth curve to these data. Write a short report to summarize your findings. If the U.S. national debt continues to rise at the exponential rate you find, approximately what will its value be at the end of 2020? 12. The file P12_12.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise. its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the company's suspicion. b. Fit the appropriate regression model to the data. Report the resulting equation and state explicitly what it says about the percentage growth per month. c. What are the RMSF and MAPE for the forecast model in pan b? in words. what do they measure? Considering their magnitudes, does the model teem In he &line a mod iob?
El Consider the proportion of Americans under die age of 18 living below the poverty level. The data are in the file PO2 44.xlsx. a. Find the first six autocorrelations of this time series. b. Use the results of part a to specify one or more promising autoregression models. Estimate each model with the available data. Which model provides the best fit to the data? c. Use the best autoregression model from part b to produce a forecast of the proportion of American children living below the poverty level in the next year. Also, provide a measure of the likely forecast error. 28. The file P02_ 25.x1xx contains monthly values of two key Interest rates, the federal funds rate and the prime rate. a. Specify one or more promising autoregression models based on autocorrelations of the federal funds rate series. Estimate each model with the available data. Which model provides the best fit to data? b. Use the best autoregression model from ;Rut a to produce forecasts of the federal funds rate in the next two years. c. Repeat parts a and b for the prime rate series.
54. The file P02_55.xlsx contains monthly retail sales of beer, wine, and liquor at U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern and then deseasonalize this time series using the ratio-to-moving-average method. b. If you decided to deseasonalize this time series in part a. forecast the deseasonalized data for each month of the next year using the moving average method with an appropriate span. c. Does Holt's exponential smoothing method, with optimal smoothing constants, outperform the moving average method employed in part b? Demonstrate why or why not. ggi Continuing the previous problem, how do your responses to the questions change if you employ Winters' method to handle seasonality in this time series? Explain. Which forecasting method do you prefer, Winters' method or one of the methods used in the previous problem? Defend your choice.