Exam 1

2
January 30, 2016 at 4:44 pm  •  Posted in s16-busad360 by  •  2 Comments

Exam 1 on Wed, Feb 17

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.

  1. Corn
  2. Soybeans
  3. Wheat
  4. Calves
  5. Cattle
  6. Hogs
  7. 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

Leave a Reply