Sample_Questions & answers

Sample_Questions & answers - Forecasting...

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Forecasting 1.   Here are monthly sales of Scratches Inc. (in thousands) from August to November 2005.  The seasonal index for each month is given as well. Answer the following questions: Month August September October Novembe r December Sales 751 621 840 982 ? Seasonal Index 0.91 0.73 0.96 1.09 1.21 (a)  Deseasonalize each month’s sales.       (b) Use the  appropriate na ï ve method to calculate December 2005 deseasonalized forecast. (c) Find the final forecast for December 2005. 2.   You are tracking annual revenues of your consulting firm since its inception in 1999, and  would like to predict this year’s revenues.  You remember 2 different techniques from  DS412, a 3 period Moving Average and Exponential Smoothing (with an alpha = .1)    year sales ($K) 3 period MA Exp smooth errors-MA errors- Exp 1999 290 2000 300 290.00 2001 320 291.00 2002 340 303.33 293.90 2003 330 2004 MAD = (a)  Fill in the table with the forecast for 2003 and 2004 (forecasts for 2002 and earlier  have been provided.) (b)  Fill in the table with the absolute values of the errors and calculate MAD (Mean  Absolute Deviation)  (c)  Given MAD, which technique seems to be better? Explain your reasoning in a single  sentence.   Month August September October Novembe r Deseasonalized Sales 1
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3.  A survey of the food served at Burger Queen Restaurants shows that the amount of fat in a  food item (in grams per serving) can be predicted by using the amount of protein in the food  item (in grams per serving). The regression line for the relationship is given as  y =  6.8 + 0.97 x. (a) What is the independent variable? (b) What is the dependent variable? (c) We pick a certain food item at one of these restaurants and found out that it has 10  grams of fat per serving. What is the protein amount in this food as suggested by the  above model? (d) If the correlation coefficient for this model is 0.87, what does this tell about the  model? 4.   You have decided that the annual lawn chair sales are affected by the annual number of  houses sold. You have done the research last year and now you want to continue the work  without redoing the calculations. After an exhaustive search, all you can find is a piece of  paper titled “Summary Results for Lawn Chairs vs. House Sales Study” which has the  following info: SUMMARY OUTPUT Regression Statistics Multiple R 0.931882 R Square 0.896883 Adjusted R  Square 0.87749 Standard Error 0.011091 Observations 5   Coefficient s Intercept 203 X Variable 1 0.07 Using this data, answer the following questions: (a)   The independent variable is : __________________________________ (b)  The dependent variable is : __________________________________ (c)  Write down the equation describing the relationship between the variables (d)  If the number of houses sold in 2003 is 6800, what is the forecast for 2003 lawn  chair sales?
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