Meeting:
Tues Thurs 9.00 - 10.20 a.m.  Wellman 2

Office Hours:
Tuesday afternoon     3.30 p.m. - 5.00 p.m.
Thursday morning     10.30 a.m. - noon.

Teaching Assistants:

3. Sections A05-A06 Tues 1.10-2pm and Tues 2.10-3pm  in Hutchison 93
    Zhiyuan Li   zhyli@ucdavis.edu
    Office hours: Mon 10.40-11.30am  Tues 10.40-11.30am  SSH 0120

Course Goals:
(1) Locate economics data and provide meaningful economic analysis of these data.
(2) Use a standard spreadsheet (MS Excel) to produce charts, scatter plots, descriptive statistics and simple linear regression.
(3)  Provide a bridge between introductory statistics and more advanced data analysis courses (e.g. between Statistics 13 and Economics 140).

Pre-requisites:
Economics 1A-B, Statistics 13 or 32 and Math 16A-B or consent of instructor.
The essential pre-requisites are exposure to introductory lower-division courses in economics and statistics.

Relationship to Economics 140
Economics 140 (Econometrics) is a more advanced course that also covers the methods of Economics 102.
Economics 140 has Economics 102 or any upper division statistics class as a pre-requisite. i.e. Those wanting to take Economics 140 do not have to take 102, but can instead take an upper division statistics class.

Topical Outline:

For all topics an essential ingredient is that economics data be analyzed throughout. This includes case studies that include key introductory economics relationships, such as the Phillips curve.

A. Introduction:
  1.- Introduction and getting going on Excel.

B. Univariate: Analysis of a single economics variable
  2. - Visualizing data using histograms, pie charts, graphs and time series plots.
  3. - Summarizing data using descriptive statistics
  4. - Statistical inference for population mean using confidence intervals and t tests
  5. - Statistical inference continued
  6. - ***** First midterm exam  *****
  7. - Data transformations
  8. - Further univariate

C. Bivariate: The relationship between two economic variables
  9. - Visualizing data using scatter plots, summarizing data using correlation and regression
10. - Case Study: Health Expenditure and Health Outcomes
11-12. - Statistical inference on regression coefficients
13. - Prediction; Data transformations for bivariate regression
14. - ***** Second midtem exam *****

4. Multivariate: The relationship between more than two economic variables

15. - Visualizing data, summarizing data and begin statistical inference
16. - Further statistical inference
17. - Case Study: Augmented Phillips curve
18. - Model misspecification
19. - Further Topics
20. - Further Topics

Required Materials:
On sale at Davis Copy Shop (231 Third) is: Lecture Notes for Analysis of Economics Data 102.
These lecture notes cover the entire course.
Most instructors of 102 use these notes so you may be able to use the lecture notes from previous quarters. 

Recommended:
There is no good text for this course. In the past I had Gary Koop, "Analysis of Economics Data", Wiley, 2000 (first edition) or 2003 (second edition), as a recommended text but it does not provide enough detail. Going the other way, standard econometrics texts such as James H. Stock and Mark W. Watson, "Introduction to Econometrics", are more advanced than this course. My lecture notes should do. If they are not enough then consider purchasing a book or borrowing one from the library.

Additional Materials:
The Excel handouts plus some Excel extras are also at cameron.econ.ucdavis.edu
Relatively inexpensive text books on using Excel for data analysis are available. It should not be necessary to purchase one.  

Reading List:
 

Topic	Lecture Notes	Koop (optional)
1. Introduction	Chapter 1	Chapter 1
2. Univariate	Chapters 2-4	Chapter 2
3. Bivariate	Chapters 5-8	Chapters 3-5
4. Multivariate	Chapters 9-10	Chapter 6
5. Additional topics	Chapter 11-12	Chapter 7
6. Case Studies	Chapters 13-16	-

Computer Materials:
1. Assignments use MS Excel on PC or MAC.
We will use Excel 2007 in the labs, but any version of Excel from Excel 97 on will do.
You will also need to be able to download data from the web.
2. Discussion sections will be held in a university computer lab in Hutchison 93.
For university computer lab availability (also has Excel) go to lm.ucdavis.edu
3. The first discussion section will be on getting going in Excel.
4. You need an account to use computers in the university labs. Any questions call IT-CAP at 752-2548.
Allow time to get a new account set up or to get a new password if old one forgotten.

Course Grading:
Assignments:        10% 6 assignments.    Best 5 out of the 6.   Due 9.00 a.m.
                               Jan 13 (Tu), 20 (Tu); Feb 5 (Th), 17 (Tu); March 5 (Th), 12 (Th).
Midterm exam 1:  22.5%  Thursday Jan 22  (class 6)
Midterm exam 2:  22.5%  Thursday Feb 19  (class 14)
Final exam:            45%  Tuesday March 17  8.00 a.m. - 10.00 a.m.   (comprehensive)

Scores are posted at Smartsite. You have one week from when work is first returned in class to raise any questions about grading.
AFTER THE FINAL EXAM IS TAKEN NO CHANGES WILL BE MADE FOR ANY REASON TO ANY SCORES RECORDED ON SMARTSITE.

Given the course total score I form a curve and allocate grades using this course score curve and to fit in with the department policy: "To ensure fairness and consistency in grading, the Department expects that the GPA in Economics 1A, 1B, 100, 101, and 102 will average 2.4. (For example, a distribution with 20% A's, 30% B's, 30% C's, 10% D's, and 10% F's could be consistent with an overall GPA of 2.4.)''

Assignments must be handed in on time, so that solutions can be discussed in class and distributed in a timely manner. No credit for late assignments. Assignments are graded on a 2 point scale.
THE ASSIGNMENTS ARE THE ESSENTIAL WAY TO LEARN THE MATERIAL AND PASS THE EXAMS.

Exams will be closed book. Exams will include questions on use of Excel.
A formula sheet will be provided. Note that the final exam is comprehensive.

Regrading: Assignment scores and exam scores will be posted on myucdavis. Any concerns about scores must be raised within one week of when graded work is first returned in class.

Academic integrity is required. What is academic integrity? From the UCD Student Judicial Affairs website http://sja.ucdavis.edu/integ.htm, examples of Academic Misconduct include:
Cheating - includes receiving or providing unpermitted assistance on exams; using unauthorized materials during an exam; altering an exam and submitting it for regrading; taking an exam for another; failing to stop working on an exam when time is called; providing false excuses to postpone tests or due dates; fabricating data or references.
Unauthorized Collaboration - working with others on graded coursework without specific permission of faculty (on in-class or take-home tests, papers, labs, or homework assignments).
Plagiarism - using another's work without giving credit. You must put others' words in quotation marks and cite your source, and must give citations when using others' ideas, even if paraphrased in your own words.
Repeated Work: Submitting the same work in more than one course, unless authorized by the instructor.
Exams: "Wandering eyes," talking during exams, having notes visible, or leaving the exam room without permission.

Variation for my class: For my class I am willing to allow some collaboration in doing assignments, but the work handed in must be your own. Each person must create their own Excel output and write up their own answers. And you need to write on your assignment the name of the person(s) you worked with on that assignment.