STAT 381

Text: Probability & Statistics for Engineers & Scientists  by Walpole, Myers, Myers, and Ye 9th Ed.

Prerequisite: Grade of C or better in Math 181.

Grades: The course grade is based on the total number of points from hour exams, homework, quizzes, and the final exam.  There are 500 points total.  Grades will be assigned as follows:  450 A, 400 B, 335 C, 285 D.

Homework:  Homework will be assigned for each section covered.  The homework will be worth 50 points.  No late homework will be accepted.
Assigned problems and due dates

Quizzes:  There will be quizzes during the course.  The quizzes in total will be worth 50 points. The lowest quiz will be dropped.  If you miss a quiz, that is the one that will be dropped.

Exams: There will be two exams during the course of the semester.  Each will be worth 100 points.  The exams will be on 10/3 and 11/7.

Final Exam:  The final exam will be cumulative and worth 200 points. There will be no makeup final.  You must take the exam at the scheduled time. The final exam is Tuesday December 11th from 8:00 to 10:00

Disability Policy: The University of Illinois at Chicago is committed to maintaining a barrier-free environment so that students with disabilities can fully access programs, courses, services, and activities at UIC. Students with disabilities who require accommodations for access to and/or participation in this course are welcome, but must be registered with the Disability Resource Center (DRC). You may contact DRC at 312-413-2183 (v) or 312-413-0123 (TTY) and consult the following: http://www.uic.edu/depts/oaa/disability_resources/faq/accommodations.html.

Topics Covered in STAT 381

Chapter 1

Histogram

Boxplot

Measures of Location

Sample Mean

Median

Percentiles

Measures of Variation

Sample standard deviation

Interquartile Range

Chapter 2

Probability Rules

1. 0 ≤ P(A)  ≤ 1
2. P(S) = 1
3. P(A or B) = P(A) + P(B) – P(A and B)
4. P(AC ) = 1 – P(A)

Compute probability using combinations

Conditional probability

Multiplication Rule

Independence

Law of Total Probability

Bayes’ Theorem

Chapter 3

Discrete Random variables

Properties of a pdf

Properties of a cdf

Continuous Random variables

Properties of a pdf

Properties of a cdf

Two variables

Use joint pdf

Marginal pdf

Conditional pdf

Chapter 4

Expectations, especially mean and variance

Mean and variance of linear combinations of independent variables.

Chapter 5

Binomial  and Multinomial: pdf, mean, and variance

Hypergeometric: pdf, mean, and variance

Negative Binomial:  Know pdf

Poisson :  pdf, mean, and variance, and effect of changing interval

Chapter 6

Normal Distribution

Given X find percentiles

Given Percentiles, find X

Normal approximation of the binomial

Exponential : Know pdf, mean, and variance

Weibull: pdf

Gamma: pdf, mean, variance

Time until kth event is Γ( k, 1/λ) for a Poisson process with parameter λ.

Mean and variance of linear combinations of independent variables.

Chi-Squared: mean, variance

Chapter 8

Sampling Distributions

Central Limit Theorem

Mean of

Standard deviation of

t-distributions

Chapter 9

Confidence interval for population mean, sigma known

Finding n for fixed error

Confidence interval for difference of population means, sigma known

Confidence interval for population mean, sigma unknown

Confidence interval for difference of population means, sigma unknown

Confidence interval for population variance

Confidence interval for proportions

Finding n for fixed error

Confidence interval for difference of proportions

Chapter 10

Significance tests for population mean, sigma known

Set up hypotheses

One sided vs two sided tests.

Determine if sample results are significant at a given significance level.

Significance tests for proportions.

Set up hypotheses

One sided vs two sided tests.

Determine if sample results are significant at a given significance level.