Reference no: EM13393757

1. The file P14_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.

2. The file P14_02.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.

3. The file P14_04.xlsx lists the monthly sales for a company (in millions of dollars) for a 10-year period. a. Fit an exponential trend line to these data. b. By what percentage do you estimate that the company will grow each month? c. Why can't a high rate of exponential growth continue for a long time? d. Rather than an exponential curve, what type of curve might better represent the growth of a new technology?

4. Management of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P14_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.

5. Suppose you are an analyst for a company that produces four products, and you are trying to decide how much of each product to produce next month. To model this decision problem, you need the unit variable production cost for each product. After some digging, you find the historical data on production levels and costs in the file P14_12.xlsx. Use these data to find estimates of the unit costs you need. You should also find an estimate of the fixed cost of production. Will this be of any use to you in deciding how much of each product to produce? Why or why not?

6. A trucking company wants to predict the yearly maintenance expense (Y) for a truck using the number of miles driven during the year (X1) and the age of the truck (X2, in years) at the beginning of the year. The company has gathered the data given in the file P14_13.xlsx. Note that each observation corresponds to a particular truck. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Also, interpret the standard error of estimate and the R-square value for these data.

7. An antique collector believes that the price received for a particular item increases with its age and with the number of bidders. The file P14_14.xlsx contains data on these three variables for 32 recently auctioned comparable items. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Is the antique collector correct in believing that the price received for the item increases with its age and with the number of bidders? Interpret the standard error of estimate and the R-square value for these data.