Exam 1
January 30, 2016
[highlight color=”options: yellow, black”]Exam 1 on Wed, Feb 17[/highlight]
Part 1:
Students will analyze agricultural commodity price data (link to Sheets data*). The data include monthly prices going back to 1960 for seven agricultural commodities, including corn, soybeans, wheat, calves, cattle, hogs and milk.
Each student has been randomly assigned 3 commodities (see Commodity Assignments below). For each assigned commodity students will (1) build a time series forecasting model using regression analysis; (2) prepare a graphic to illustrate data and model; (3) generate 8 price forecasts for March, June, September, December 2016 and 2017; and (4) write a paragraph describing your analysis and forecasts.
What to bring on Exam day:
- 1-page analysis for each assigned commodity (3 pages total).
- All activities and assignments printed, organized and neatly bound (paper clips, no staples).
- Exam is open book, open note, open calculator. Bring what you’ll want to use.
Part 2:
The in-class exam will be a combination of problem solving and short essay questions. All material covered since the beginning of term is fair game. We will have a review session during class on Mon, Feb 15.
Commodity Assignments:
Look up your 3 commodities corresponding to your PID (last 4 digits) in the table below.
- Corn
- Soybeans
- Wheat
- Calves
- Cattle
- Hogs
- Milk
Use the list above to determine your assignments. For example, if you see 1, 3 and 5 next to your 4-digit PID then you’ve been assigned Corn, Wheat and Cattle. Or, if you see 2, 4, 6 then you’ve been assigned Soybeans, Calves and Hogs. Etc.
| PID4 | Data 1 | Data 2 | Data 3 |
| 0398 | 3 | 2 | 4 |
| 0460 | 5 | 7 | 6 |
| 0488 | 6 | 7 | 5 |
| 0558 | 3 | 5 | 6 |
| 0813 | 4 | 5 | 3 |
| 0882 | 7 | 1 | 5 |
| 1005 | 3 | 4 | 1 |
| 1025 | 4 | 5 | 3 |
| 1241 | 2 | 3 | 5 |
| 1347 | 5 | 4 | 2 |
| 1348 | 5 | 6 | 1 |
| 1516 | 3 | 2 | 4 |
| 1637 | 2 | 1 | 3 |
| 1650 | 6 | 1 | 7 |
| 1680 | 7 | 5 | 1 |
| 1720 | 3 | 2 | 6 |
| 1903 | 4 | 5 | 6 |
| 1917 | 1 | 5 | 4 |
| 1922 | 6 | 3 | 7 |
| 1938 | 6 | 4 | 7 |
| 1943 | 7 | 5 | 3 |
| 2271 | 6 | 7 | 1 |
| 2284 | 6 | 5 | 4 |
| 2407 | 2 | 1 | 6 |
| 2409 | 2 | 3 | 1 |
| 2431 | 6 | 3 | 1 |
| 2583 | 2 | 7 | 4 |
| 2704 | 5 | 7 | 2 |
| 2951 | 6 | 5 | 4 |
| 2995 | 2 | 3 | 7 |
| 3275 | 3 | 1 | 2 |
| 3547 | 4 | 7 | 5 |
| 3772 | 7 | 1 | 3 |
| 3778 | 3 | 7 | 5 |
| 4096 | 5 | 4 | 1 |
| 4212 | 2 | 3 | 5 |
| 4289 | 5 | 2 | 1 |
| 4443 | 1 | 2 | 4 |
| 4673 | 3 | 2 | 4 |
| 4735 | 4 | 7 | 1 |
| 5118 | 7 | 2 | 1 |
| 5160 | 3 | 7 | 2 |
| 5210 | 2 | 4 | 5 |
| 5303 | 3 | 1 | 7 |
| 5484 | 5 | 3 | 7 |
| 5679 | 3 | 4 | 7 |
| 6122 | 1 | 6 | 5 |
| 6129 | 2 | 5 | 1 |
| 6243 | 5 | 2 | 6 |
| 6253 | 2 | 1 | 7 |
| 6261 | 2 | 1 | 3 |
| 6274 | 6 | 1 | 2 |
| 6383 | 6 | 7 | 5 |
| 6634 | 3 | 6 | 1 |
| 6880 | 3 | 7 | 1 |
| 7003 | 5 | 1 | 4 |
| 7097 | 5 | 1 | 6 |
| 7191 | 4 | 6 | 7 |
| 7201 | 5 | 6 | 4 |
| 7230 | 7 | 2 | 4 |
| 7253 | 4 | 7 | 3 |
| 7277 | 4 | 1 | 5 |
| 7351 | 5 | 6 | 7 |
| 7390 | 5 | 1 | 2 |
| 7444 | 1 | 3 | 4 |
| 7459 | 3 | 5 | 1 |
| 7497 | 5 | 2 | 7 |
| 7568 | 3 | 6 | 2 |
| 7763 | 5 | 3 | 2 |
| 7832 | 5 | 7 | 2 |
| 8154 | 6 | 7 | 4 |
| 8200 | 5 | 6 | 4 |
| 8287 | 4 | 7 | 5 |
| 8350 | 7 | 3 | 1 |
| 8542 | 6 | 7 | 2 |
| 8613 | 7 | 6 | 5 |
| 8746 | 4 | 7 | 5 |
| 8955 | 2 | 6 | 7 |
| 8962 | 5 | 7 | 2 |
| 9100 | 5 | 6 | 2 |
| 9178 | 1 | 6 | 7 |
| 9205 | 1 | 7 | 2 |
| 9218 | 5 | 7 | 6 |
| 9220 | 5 | 1 | 3 |
| 9256 | 6 | 7 | 2 |
| 9322 | 1 | 2 | 3 |
| 9380 | 1 | 3 | 4 |
| 9535 | 1 | 5 | 7 |
| 9556 | 3 | 1 | 2 |
| 9637 | 3 | 4 | 1 |
| 9740 | 1 | 7 | 2 |
| 9829 | 3 | 5 | 4 |
| 9963 | 6 | 4 | 2 |
* Data Source: University of Illinois farmdoc project
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