Compute mad and sse for each model to determine which was

Reference no: EM131091787

Analysis and Forecasting

1. To forecast future cigarette sales, the last four years' quarterly sales were recorded in the Excel file Tute10-Q1.xls. Calculate the 3-quarter and 5-quarter moving averages. Show the relevant graphs. Interpret the results.

2. Apply the exponential smoothing technique with ω = 0.2 and ω = 0.7 to the data in question 1. Show the relevant graphs. Interpret the results.

3. Annual sales for a pharmaceutical company are believed to change linearly over time. Based on the last 10 years' sales records (refer to data file Tute10-Q3.xls), measure the trend component. Interpret the results. Forecast sales in 2014 (assume no seasonal, cyclical or random variation).

4. Use the data in Excel file "Tute10-Q4.xls" and simple linear regression to estimate the regression line. Calculate the quarterly seasonal indexes for hotel occupancy rate, in order to measure seasonal variation. Interpret what is meant by seasonal Indexes.

5. Two forecasting models were used to predict the future values of a time series. These are shown in the following table, together with the actual values.

Forecast Value t F₁			Actual Value y_t
Model 1 Model 2
8.2	7.7	7.6
7.8	8.5	8.2
7.0	8.5	7.6
9.6	9.0	10.3

Compute MAD and SSE for each model to determine which was more accurate.

6. The actual and forecast values of a time series are shown below.

Actual values y_t	Forecast values F_t
135	140
162	165
155	150
182	191
174	168
194	190
233	220
280	240

(a) Calculate the mean absolute deviation (MAD).

(b) Calculate the sum of squares for forecast error (SSE).

Reference no: EM131091787

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