STATISTICS 10
Introduction to Statistical Reasoning

Spring 2001 Syllabus

 

INSTRUCTOR
Vivian Lew, Ph.D.
Office: 8142D Math Sciences
Phone: (310) 206-6474
Email: vlew@stat.ucla.edu
Mailbox: 8142 Math Sciences Building (usually open from 8:30am to 4:30pm M-F)
Office Hours: Monday 2:00pm - 3:30pm, Tuesday 3:30pm - 5:00pm and by appointment

TEACHING ASSISTANT

Office

Office Hours 

Trudy Poon

Math Sciences 3969

TBD

MEETING TIMES & PLACES
Lecture Section 1: Mondays, Wednesdays, & Fridays, 8:00am - 8:50am, Knudsen 1200B

SECTIONS:

ID 

DAY 

TIME 

ROOM 

TA 

DIS 1A 263-031-201

Tuesday 

8:00 - 8:50am 

Math Sciences 5203 

Poon

DIS 1B 263-031-202

Thursday 

8:00 - 8:50am 

Math Sciences 5203 

Poon

DIS 1C 263-031-203

Thursday 

9:00 - 9:50am 

Math Sciences 5203 

Poon 

TEXT
Statistics (3rd edition), by David Freedman, Robert Pisani, and Roger Purves.

SUMMARY
In this course, you will learn about the role of probability and statistics in describing, inferring, decision-making, and predicting from data. We will stress the ideas underlying statistical methods and will focus on applications rather than on abstract theory. No computer software is used but it is strongly suggested that you own a calculator with a square root key at minimum for your exams.

SECTIONS and HOMEWORK ASSIGNMENTS
Sections are your opportunity to work through statistical issues, raise questions, and get a lot of help from your TA. Your six homework assignments are due at the end of lecture on the dates listed below.

Homework assignments are given out during lecture and then posted on the web in case you miss the handout. Homework is due before the end of lecture on the specified days/times (last page).

Get help from people (me, your TA, classmates, friends, tutors) on your homework assignments. All I ask is that you attempt to solve the problems on your own and please turn in your own copy. Photocopied assignments will not be accepted (but you may wish to keep one for yourself).

Only your best 5 of the 6 assignments will count towards your final grade. Missed/late homework assignments will not be accepted unless you have made prior arrangements with me. Please do your homework, it cannot hurt you but only help you. Homework and labs are graded on the following scale

 

Homework and Labs

Complete and 100% correct

Complete with minor mistakes

Complete with major mistakes

Incomplete and correct

Incomplete with mistakes

Not turned in

Points Awarded

6

5

4

2

1

0

Not all homework questions assigned are graded. Questions are randomly selected for grading. Therefore, beware of incomplete assignments, they might be worth less than you think if we select questions for grading that you did not complete. Submit all homework grading complaints to the professor.

EXAMS
You will have two midterm exams and a final exam. The final exam code is 1 and it will be given on Wednesday, 6-13-2001, 3:00PM - 6:00PM in the lecture hall (unless you are told otherwise later in the quarter). The final exam is cumulative. Always bring a calculator and your student photo ID or some other kind of photo ID (e.g. driver's license, passport) to all exams. Identification will be checked and attendance will be taken. I will provide all answer forms and scratch paper. You will not need to purchase scantrons or bluebooks for any exam in this course.

MISSED EXAMS and GRADE APPEALS

Make-up examinations are given only when circumstances beyond a student's control make attendance during the scheduled examination period impossible. Documentation will be required to verify a student's claim and permission to take a make-up examination must be obtained from the instructor. If a make-up exam is permitted, it will be written individually for that student and will have a different format that the regular examination. In all cases, final determination of whether or not to give a make-up examination rests with the instructor.

There may be a time when we make a mistake grading one of your exams. If you think this is the case, write a note describing the error, attach it to the original exam, and give it to me within 3 business days of the return of your midterm. I will review your argument and the initial grading decision and return a decision to you in a timely manner.

FINAL GRADE
Your grade is based on your best 5 of the 6 homework assignments (total 10%), the 2 midterms (20% midterm 1, 30% midterm 2 -- total 50%) and the final (40%). The course is usually graded on a "curve". Grades are not e-mailed, posted, or given out over the phone, they can be found on URSA in a timely manner. If you believe your final grade is not correct after it has been issued, please contact me before the end of the second week of the next quarter. Requests for corrections after that time period will be denied unless it was due to a procedural or clerical error.

STAT 10 WWW PAGE
Class related material (i.e. lecture notes, handouts, practice exams) is available to you via the World Wide Web. The URL (``address'') of this course's page is:

 http://www.stat.ucla.edu/courses/stat10_1.php

SCHEDULE OF EVENTS

EVENT 

 

DATE 

Problem Set 1: 

 

Due by the end of lecture April 13

Problem Set 2: 

 

Due by the end of lecture April 20

Review Session (in lecture): 

 

April 23

Midterm I: 

 

April 25

Drop Date w/ No Notation

 

April 27

Problem Set 3: 

 

Due by the end of lecture May 4

Problem Set 4: 

 

Due by the end of lecture May 11

Last Day to change to P/NP

 

May 11

Review Session (in lecture): 

 

May 21

Midterm II: 

 

May 23

Problem Set 5: 

 

Due by the end of lecture May 25

Holiday

 

May 28

Problem Set 6: 

 

Due by the end of lecture June 8

Review Sessions (in lecture): 

 

June 6 and 8

Final: 

 

Wednesday June 13 -- 3:00pm - 6:00pm

 

Quick Overview of Stat10

Statistics is the science of collecting, presenting, and interpreting data to answer questions.

There are four primary issues:

1.Determining the question and the data that will help you answer the question.

2.Collecting the data.

3.Summarizing and presenting the data graphically and numerically.

4.Making generalizations from the data and drawing conclusions. Usually by making comparisons between groups of people, animals, or things.