ECON 502

Economic Statistics Section M1, TR 8:00-9:50 am, 215 David Kinley Hall

Section M2, TR 1:30-3:20 pm, 119 David Kinley Hall

Department of Economics • UIUC

Course Syllabus Fall 2015

Compass site login page: https://compass2g.illinois.edu/

Instructor: Ali Toossi

Office: 205C DKH

Phone: 333-6777

E-mail: toossi@uiuc.edu

Office hours: MW 11:00-12:00 or by appointment

Assistant Instructor: Ruchi Singh

Office: 205 DKH

Phone: 333-7594

E-mail: rsingh39@illinois.edu

Office hours: MW:2:30-3:30 pm; TR:11:00-Noon

Weekly Sessions: Section M1: Fridays 8:00-9:20am Room 215 David Kinley Hall

Section M2: Fridays 9:30-10:50 am Room 119 David Kinley Hall

The first meeting will be on Friday August 28.

The Assistant Instructor will meet with you once a week on Fridays. These meetings will

provide you with an opportunity to review the material covered in class and to work

examples concerning the class.

This course is designed to teach you what statistics mean and how to use statistics

effectively in your own work and life. The text provides very good coverage of needed

material.

I will try to make effective use of the computer. The computer will serve several

different purposes. It will be employed as a tool to understand and describe data sets, to

compute statistical estimates and make inferences from data and finally, the computer

will help understanding of theoretical concepts by allowing us to see how those concepts

work.

Textbook: Mathematical Statistics with Applications (7th ed.), by Dennis Wackerly,

William Mendenhall III, Richard Scheaffer.

Note that an eBook option is available which is cheaper than the textbook. Go to: http://www.cengage.com/search/showresults.do?N=16+4294922413+4294966842+4294947185

Attendance: You are required to attend both the lectures during the week and the

recitation on Fridays. For excused absences, the student must provide an explanation and

supply supporting evidence.

Homework: There will be a required homework assignment approximately every two

weeks (7-8 homeworks). In some of the problems assigned you have to use APPLETS (a

short computer application especially for performing a simple specific task). You can

access the APPLETS in the following site: http://www.brookscole.com/cgi-

wadsworth/course_products_wp.pl?fid=M20b&flag=student&product_isbn_issn=9780495110811&discipli

nenumber=17

Exams: The class will have 4 quizzes, a midterm and a final examination.

Quiz: There will 4 quizzes quizzes on: September 17, October 6 and November 3

and November 19. The dates might change. The quizzes will be one hour

long in the first hour of the class. New lecture will be given in the second

hour.

Midterm: Tuesday, October 13, 7:00 - 9:00 pm in room 213 Gregory Hall

Review Session: Monday, October 12, 6:00 pm in room 213 Gregory Hall

Final: Regular Exam: Wednesday December 16 1:30-4:30 pm room 2 Education

Conflict Exam: Friday December 11 1:30-4:30 pm room 215 DKH

Grading: The course grade will be determined as follows:

Class participation, instructor's judgment, and homework: 15%

Quizzes: 20%

Midterm examination: 30%

Final examination: 35%

The average determined above will be adjusted to take into consideration the trend of

your performance and grades.

Academic Integrity: Violations of academic integrity as given in the Code of Policies

and Regulations will be taken extremely seriously, and students found cheating in the

course (or helping others to cheat) will be penalized according to the Code‘s guidelines.

The course outline lists the dates each topic will be covered. The dates are approximate & could change.

Lecture Date Topics Covered

1 August 25

Chapter 1: What is statistics? Descriptive & Inferential Statistics

Population or Process, Sample Experimental vs Observational Data, Sampling

errors, Sampling methods Types of Data: Quantitative vs Qualitative Types of Data: Cross section, Time series,

Panel Descriptive statistics: Quantiles,

2 August 27

Chapter 1: What is statistics? (Continued) Descriptive statistics: Mean, Median, mode, trimmed mean, Variance, CV , Interquartile

range, range, MAD, Empirical Rules, Skewness, Kurtosis, Normal probability plot,

JB test for normality

3 September 1

Chapter 2: Probability Set theory, random experiments, sample

space (Discrete , Continuous); event (simple, compound)

Def. of probability=> 3 approaches: 1-probability As proportion of desired to possible outcomes, 2- probability as relative frequency, 3- axiomatic approach,

Using axiomatic approach to derive some results

4 September 3

Chapter 2: Probability (Continued) Assigning probability of event: Sample point

method Tools for counting sample point:

multiplication rule, permutation, combination More examples on counting

Tuesday September 8 First Homework Due

5 September 8

Chapter 2: Probability (Continued) Conditional probability

Independence of events Multiplicative law of probability

additive law of probability Calculating probability of event: event

composition method

6 September 10 Chapter 2: Probability (Continued)

The law of total probability & Bayes’ rule

random sampling and random variable Chapter 3: Discrete random variables

Random variable and its realization P(Y=y)

Tuesday September 15 Second Homework Due

7

September 15

Chapter 3: Discrete random variables Discrete probability distribution expected value: mean, variance

mean & variance of a function of a random variable

Examples on expected value and variance Bernoulli experiment & related distributions

Bernoulli Distribution Binomial Distribution

Thursday September 17 Quiz 1

8 September 17

Chapter 3: Discrete random variables (continued)

Examples on Binomial Distribution Hyper Geometric

9 September 22

Chapter 3: Discrete random variables (continued) Geometric

Negative Binomial ; Poisson

10 September 24

Chapter 3: Discrete random variables (continued)

Poisson Moments around origin and about the mean Moment generating functions Tchebysheff's

Theorem

11 September 29

Chapter4: Continuous random variables Distribution function (CDF)

Discrete Y: CDF STEP function (right Continuous)

Continuous Y: CDF Continuous function Continuous Y : Probability Density Function

Example on PDF & CDF

12 October 1

Chapter4: Continuous random variables (continued)

Expected value & Variance Distributions: Uniform, Normal, Gamma

Friday October 2 Third Homework Due

Tuesday October 6 Quiz 2

13 October 6 Chapter4: Continuous random variables

(continued) Relationship between Gamma & Poisson

Gamma Special cases: Chi-square, Exponential Relationship between

Exponential & Poisson

14 October 8

Chapter4: Continuous random variables (continued)

Examples on exponential Hazard function Beta Distribution

MGF for continuous RV Tchebysheff's theorem for continuous RV

Chapter 5: Multivariate PD (discrete) Joint and cumulative probability distribution

Marginal & conditional probability distributions

Independent random variables, Expected value of a function of random

variables conditional expectations

Monday October 12

4th Homework Due (by 4:45 pm in Ruchi’s Mailbox)

Review Session 6:00 pm in room 213 Gregory Hall

Tuesday

October 13

Midterm Exam 7:00 - 9:00 pm in room 213 Gregory Hall

(NO CLASS)

15 October 15

Chapter 5: Multivariate PD (discrete) Example on bivariate discrete distributions

Covariance & Correlation Regression and correlation

expected value and variance of a linear function

16 October 20

Chapter 5: Multivariate PD Examples on expectation and variance of

linear functions of RV Law of Large Numbers

Chapter 5: Bivariate PD (continuous) Introduction to double integration

Joint Distribution function & density function Marginal & conditional probability

distributions Independent random variables

Expected value of a function of random variables

17 October 22

Chapter 5: Bivariate probability distributions (continuous)

Conditional expectations Bivariate normal

Chapter 6: Functions of random variables (sections 6.1-6.5)

Functions of random variables: 3 methods Distribution function Method

18 October 27

Chapter 6: Functions of random variables (sections 6.1-6.5)

Method of Transformations examples on distribution & transformation

method Method of MGFs

19 October 29

Chapter 7: Sampling distribution & the CLT Definition of statistic

sampling distribution of sample mean (when population variance is known)

sampling distribution of sample variance t-student distribution

sampling distribution of sample mean (when population variance is unknown)

F distribution Sampling distribution of ratio of two sample

variances (from two populations)

Friday October 30 Fifth Homework Due

Tuesday November 3 Quiz 3

20 November 3

Chapter 7: Sampling distribution & the CLT Examples on Sampling Distributions

Normal approximation to the binomial Central limit theorem

21 November 5

Chapter 7: Sampling distribution & the CLT Examples on CLT

Chapter 8: Estimation (sections 8.1 to 8.4) Point estimation, Estimators

Properties: Bias, mean square error Chapter 9: More on point estimates,

methods of estimation Relative efficiency

22 November 10

Chapter 9: More on point estimates Cramer-Rao theorem (page 448)

consistency sufficiency

Minimum Variance Unbiased Estimators (MVUE)

23 November 12

Example on MVUE Common MVUE

Chapter 9: methods of estimation Estimation methods: moments,

maximum likelihood

Tuesday November 17 Sixth Homework Due

24 November 17

Chapter 8 revisited Confidence intervals

large sample cl for the mean and proportion Small sample confidence interval for the mean difference of means and variance Small sample confidence interval for the

difference of means Small sample confidence interval for the

variance

Thursday November 19 Quiz 4

25 November 19

Chapter 10: Hypothesis Tests Introduction to Hypothesis Testing

How to construct RR Type I and Type II errors

Alpha, beta and Power of tests Power function

November 23-27 Thanksgiving Recess

Tuesday December 1 7th Homework Due

26 December 1

Chapter 10: Hypothesis Tests Neyman Pearson Lemma

Uniformly Most Powerful Tests

27 December 3

Chapter 10: Hypothesis Tests Likelihood ratio tests

large sample tests p-values

28 December 8

Chapter 10: Hypothesis Tests Relationships between HT & CI

Small sample tests HT concerning variances

Wednesday December 9 8th Homework Due

Final Exam: Regular Wednesday December 16 1:30-4:30 pm

room 2 Education

Conflict Friday December 11 1:30-4:30 pm

Room 215 DKH