You have been presented with the following data and asked to fit statistical demand functions: PERIOD QUANTITY PRICE INCOME ADVERTISING 1 120 8.00 10 3 2 165 4.00 22 7 3 120 7.00 20 5 4 165 3.00 20 8 5 180 4.00 30 8 6 90 10.00 19 6 7 150 4.00 18 10.2 8 190 1.60 25 9.3 9 160 5.00 30 8 10 200 2.00 35
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You have been presented with the following data and asked to fit statistical demand
|
PERIOD |
QUANTITY |
PRICE |
INCOME |
ADVERTISING |
|
1 |
120 |
8.00 |
10 |
3 |
|
2 |
165 |
4.00 |
22 |
7 |
|
3 |
120 |
7.00 |
20 |
5 |
|
4 |
165 |
3.00 |
20 |
8 |
|
5 |
180 |
4.00 |
30 |
8 |
|
6 |
90 |
10.00 |
19 |
6 |
|
7 |
150 |
4.00 |
18 |
10.2 |
|
8 |
190 |
1.60 |
25 |
9.3 |
|
9 |
160 |
5.00 |
30 |
8 |
|
10 |
200 |
2.00 |
35 |
9.5 |
Non-linear relationship.
- Select and estimate any form of non-linear relationship.
- Is the estimated demand function “good”? Why or why not? Compare with the linear form above. Elaborate.
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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?Use the table of values you made in part 4 of the example to find the limiting value of the average rate of change in velocity.The manager of a travel agency has been using a seasonally adjusted forecast to predict demand for packaged tours. The actual and predicted values are as follows:Period Demand Predicted 1 132 113 2 192 200 3 152 150 4 87 102 5 82 80 6 127 135 7 122 128 8 133 124 9 98 109 10 153 150 11 108 94 12 93 80 13 128 140 14 138 128 a. Compute MAD for the fifth period, then update it period by period using exponential smoothing with α = 0.05.b. Compute a tracking signal…
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- A shop is planning an order for a popular Christmas festive season product. Demand for the product usually starts from first week of December till first week of January and reduces sharply thereafter. For this reason, and to stimulate sales for leftovers, the product is sold at a significantly reduced price from the second week of January to the fourth week of January. Any leftover after the fourth week of January goes waste. The table below gives past data on total demand for the period from first week of December to first week of January, and from second week of January to fourth week of January, together with their respective chances of occurrence. The product can be purchased at a wholesale price of GHS60 per unit for a pack containing 600 products, GHS57 per unit for a pack containing 800 products, and GHS52 per unit for a pack containing 1000 products. The shop plans to sell the product for GHS80 per unit from first week of December to first week of January, and at a reduced…The following table shows predicted product demand using your particular forecasting method along with the actual demand that occurred: FORECAST ACTUAL 1,515 1,585 1,415 1,515 1,715 1,615 1,755 1,680 1,805 1,730 Compute the tracking signal using the mean absolute deviation and running sum of forecast errors.Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the exponential smoothing method to forecast the number of sales to expect next month. Use the following value of alpha: 0.2. Month Demand Forecast 1 308 349.2 2 388 340.9 3 344 350.3 4 400 349.1 5 341 359.3
- The following table shows predicted product demand using your particular forecasting method along with the actual demand that occurred: FORECAST ACTUAL 1,500 1,550 1,400 1,500 1,700 1,600 1,750 1,650 1,800 1,700 Compute the tracking signal using the mean absolute deviation and running sum of forecast errors. Note: Negative values should be indicated by a minus sign. Round your "Mean Absolute Deviation" to 1 decimal place, "Tracking Signal" to 2 decimal places and all other answers to the nearest whole number.If you knew that the quantities of demand for wicker) and their prices (8) during the past 30 years had given the following data in Image. Find: 1.b^ 2.b^°The problem is based on the following data given. Observations of the demand for a certain part stocked at a parts supply depot during the calendar year 2013 were ( as shown ). Using a four-month moving average, determine the two-step-ahead forecast for July through December 2013. (Hint: The two-step-ahead forecast for July is based on the observed demands in February through May.)
