Meeting:
Tues Thurs 1.40  3.00 p.m. Olson 146
Office Hours:
Monday afternoon 1.30 p.m.  3.00
p.m.
Wednesday afternoon 3.30 p.m.  5.00 p.m.
Teaching Assistants:
Luis Avalos Trujillo avalost@ucdavis.edu
Office hours: Monday 34 pm SSH 0118 and
Wednesday 12 pm SSH 0116
Ethan Feilich
eifeilich@ucdavis.edu Office hours: Wednesday
45 pm SSH 0118
Discussion Sections:
Ethan Feilich
B01: Wednesday 9.00  9.50 am 93 Hutchison
Luis Avalos Trujillo
B02: Wednesday 10.00 
10.50 am 93 Hutchison
Luis Avalos Trujillo B03: Wednesday 8.00  8.50 am 93
Hutchison
Course Goals:
Prerequisites:
Economics 1AB, Statistics 13 or 32 and Math 16AB or consent of
instructor.
The essential prerequisites are exposure to introductory
lowerdivision 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 Statistics 108 as a
prerequisite.
Note: Due to course overlap (all focus on regression)
only 10 units of credit are available for taking all three of
ECN 102, ECN 140 and STA 108.
We recommend stronger students serious about data
analysis skip ECN 102 and take STA 108 followed by ECN 140.
[Note: The current listed prerequisites for ECN 140 are
incorrect and are being changed to
(ECN 100 or 100A or ARE 100A); (ECN 102 or STA 108); or
Consent of instructor.
For more on possible data classes see http://cameron.econ.ucdavis.edu/e102/morestat.html
These include classes on machine learning and on time series and
financial data that are currently listed as ECN 190.
Topical Outline by Lecture Number:
For all topics an essential ingredient is that economics data be
analyzed throughout. This includes some case studies.
For some chapters we will not cover the entire chapter. The key
sections will be announced as we go along.
A. Introduction:
Lecture 1: Introduction and getting going on Stata (chapter
1 + Stata http://cameron.econ.ucdavis.edu/stata/stata.html).
B. Univariate: Analysis of a single economics variable
Lecture 2: Summarizing data using descriptive statistics and
visualizing data using charts (chapter 2.12.2)
Lecture 3: Visualizing data using charts and univariate data
transformation (chapter 2.32.4 and 4.2.4 and 4.3.1)
Lecture 4: The sample mean (chapter 5.15.4, 5.75.8)
Lecture 5: Statistical inference based on the sample mean
using estimation and confidence intervals (chapter 5.55.6,
6.16.3)
Lecture 6: Statistical inference based on the sample mean
using t tests (chapter 6.4, 6.6)
Lecture 7: Statistical inference generalizations
(ch.7.17.2)
Lecture 8: ***** First midterm exam *****
C. Bivariate: The relationship between two economic
variables
Lecture 910: Bivariate data summary: scatter plots,
correlation and regression (chapter 8.18.10)
Lecture 11: The least squares estimates (chapter 9.19.3, 9.5)
Lecture 12: Statistical inference on regression coefficients
(chapter 9.4, 10.110.9)
Lecture 13: Bivariate case studies (chapter 11.3)
Lecture 14: ***** Second midterm exam *****
Lecture 15: Data Transformations for bivariate regression
(chapter 12.312.4, 12.6, 4.14.3)
D. Multivariate: The relationship between more than two
economic variables
Lecture 16: Multiple regression (chapter 13.113.8)
Lecture 17: Inference for multiple regression (chapter 14.114.9)
Lecture 18: Case study (chapter 15.1)
Lecture 19: Data Transformations for multivariate regression
(chapter 16.116.6)
Lecture 20: Model Checking (chapter 17.317.8)
Required Materials:
Lecture Notes for Analysis of Economics Data 102. Available
at Davis Copy Maxx (Phone: (530)7582311 at 232
Third Street  corner of 3rd and University Avenue). This
material will not be posted on the web. These lecture
notes cover the entire course. (Earlier versions of the notes may
also be adequate if Fall 2012 or later, though there have been
substantial recent changes.)
The datasets and Stata programs used in the course notes are at http://cameron.econ.ucdavis.edu/ECN102SPRING/AED_DATA.html
Additional Materials:
Assignments are posted at the course Canvas website.
The website http://cameron.econ.ucdavis.edu/e102/e102.html
has datasets, Stata programs, Stata introduction and past exams
and solutions.
There are usually free tutors for 102: see http://economics.ucdavis.edu/undergradprogram/tutoring
The Khan Academy has excellent video tutorials and exercises. See
https://www.khanacademy.org/math/apstatistics
Reading List:
Topic  Lecture Notes  
1. Univariate  Chapters 12, 47, Appendix A.1A.2 

2. Bivariate  Chapters 812  
3. Multivariate  Chapters 1316 

4. Additional topics  Chapter 17 
Discussion Sections:
These are held in a university computer lab in Hutchison 93.
Computer Materials: Assignments use Stata.
Stata is installed in 93 Hutchison, 2101 SCC, and
the Virtual Lab (after 2060 SciLab closes  see http://virtuallab.ucdavis.edu)
To see whether 93 Hutchison and 2101 SCC are available see
http://computerrooms.ucdavis.edu/available/.
If you choose to purchase Stata go to http://www.stata.com/order/new/edu/gradplans/studentpricing/
Stata/IC is more than adequate and costs $45 (6 months), $89
(1 year); $198 (permanent copy). Note that ECN140 and some
other courses such as ECN 132 also use Stata.
To install Stata after it is
purchased: (1) Choose the correct operating system (e.g.
Windows or Mac); (2) Choose the correct version of Stata  the
student price version is Stata/IC; (3) When you first run
Stata after installation it will ask for an "authorization
code". These codes are given in a pdf attachment you will
received in the email from Stata following purchase (some are
lengthy and it is easiest to cut and paste them in).
Assignments:
5% Due 1.30 p.m.
Thursdays January
17, 24; February 7, 14; March 7, 14.
Quizzes:
5% At 1.30 p.m.
Thursdays January 17, 24; February 7, 14; February 28; March
7, 14.
Midterm Exam1: 22.5% Thursday
January 31
Midterm Exam2: 22.5%
Thursday February 21
Final
Exam:
45% Monday March 18
1.00 p.m.3.00 p.m.
Comprehensive (about half on material
up to 2nd midterm and about half the remainder).
Bring
SCANTRON for quizzes and for the final
exam.
Assignments:
are posted on Canvas under Files / Homeworks.
Assignments will
be graded satisfactory (2 points) or unsatisfactory (0 points).
Full solutions will be distributed. Satisfactory means a serious
attempt to answer at least 80% of the questions. The lowest of
the scores on the six assignments is dropped, i.e. no penalty
for not handing in one assignment if the other five are graded
satisfactory. No credit for late assignments.
Quizzes: these
will generally be five multiple choice questions. Bring
scantrons.
The lowest two of the
scores on the seven quizzes is dropped. (So e.g. no penalty for
not doing two of the quizzes if the other five received perfect
scores.)
Academic honesty is required  see below.
Exams are closed book with a mixture of short answer (about twothirds) and multiple choice (about onethird) questions.