PSTAT 5AIntroduction to StatisticsInstructor: Stéphane GuerrierMWF 12:00-12:50 (IV THEA1)01/06/2014(Lecture 1) 01/06/2014 1 / 33General InfoInstructor Stéphane [email protected] Hall 5503Head TA Michael [email protected] Hall 5431NClass MWF 12:00-12:50 (IV THEA1)Office Hours W 13:30-16:30 (SH-5503)Course Website https://gauchospace.ucsb.edu/Textbook StatClass, 2nd Edition, Revised Printing,Dawn Holmes and Lubella Lenaburg, McGraw-Hill (2007)TA Go to your section to meet your TA(Lecture 1) 01/06/2014 2 / 33LecturesA lectures plan with the corresponding units from the book is posted onthe course website. It is recommended to read them before each lecture.Before each lecture, the slides will also be posted online.(Lecture 1) 01/06/2014 3 / 33Class Participation1Corresponds to 15% of the final grade.2We are going to use iClickers to answer questions related to the materialdiscussed during the lecture (please see Syllabus for details).3By answering all but one question during a lecture (right or wrong), youget 1 point for this lecture, otherwise 0. In the end, you can drop the fourlowest scores (that is up to four 0s).4Your participation grade will be:min100, 100 ×sum of your pointsmaximum number of possible points - 4(Lecture 1) 01/06/2014 4 / 33Class Participation1If you forget your iClicker you will not receive credit.2We will start using iClickers on Monday, January 13th.3We must register your iClicker every quarter.4If you do not register your iClicker before Friday, January 31st yourparticipation grade will be 0%.5The participation points will be uploaded at the end of every week. Youhave one week to make sure that grades have been posted correctly. Ifyou want to challenge a grade, contact Ya Luo ([email protected]).6If you will be missing a lecture due to your athletic obligations you mustcontact Ya Luo ([email protected]) in advance otherwise you will notreceive participation points for that lecture.(Lecture 1) 01/06/2014 5 / 33SectionsClassroom Section Lab SectionWritten assignments Online applets10% of final grade 10% of final gradeGeneral Comments1Sections begin next week!2Finish the assignment during the section and return it to your TA. We willreceive the assignments before the section.3Grades will be posted on Gauchospace. You have one week to makesure that grades have been posted correctly. If you want to challenge agrade, talk to your TA.4There are no make-up discussion sections.(Lecture 1) 01/06/2014 6 / 33Quizzes1Monday, January 27th (12:00-12:50 pm)2Wednesday, February 12th (12:00-12:50 pm)3Friday, February 28th (12:00-12:50 pm)Final1Tuesday, March 18th (12:00 -3:00 pm)General commentsClosed books/notes.It is your responsibility to ensure you are available at the designatedtimes.There are no make-up quizzes, unless you are on a sports team.If you want to challenge a grade, you must leave your exam to your TAand take an appointment with me.(Lecture 1) 01/06/2014 7 / 33Grading SchemeGradeClass Participation 15%Classroom & Lab Assignments 20%Quizzes 35% (equally weighted)Final Exam 30%TOTAL 100%(Lecture 1) 01/06/2014 8 / 33Grading Scheme (subject to change)A+ 100.00% - 99.00%A 98.99% - 93.00%A- 92.99% - 88.00%B+ 87.99% - 85.00%B 84.99% - 82.00%B- 81.99% - 78.00%C+ 77.99% - 75.00%C 74.99% - 72.00%C- 71.99% - 68.00%D+ 67.99% - 65.00%D 64.99% - 62.00%D- 61.99% - 60.00%F 59.99% - 0.00%(Lecture 1) 01/06/2014 9 / 33Where do I get help?1Ask me any questions during my office hours.IReview session: every Wednesday in South Hall 5421 from 1:30-2:30 pm(first: Wednesday, January 15th).I“Regular office hours” every Wednesday in SH-5503 from 2:30-4:30 pm(first: Wednesday, January 15th).2Ask your TAs during the weekly sections.3Work in groups!4CLAS −→ http://www.clas.ucsb.edu/ is great!5Approved tutorshttp://www.pstat.ucsb.edu/projects/leadtatraining/tutoring.htm(Lecture 1) 01/06/2014 10 / 33Where do I get help?Questions about:1Quizzes or final −→ Stéphane Guerrier ([email protected])2Assignments or lectures −→ your TA3Organisation of the sections −→ Michael Nava ([email protected])4iClicker −→ Ya Luo ([email protected])(Lecture 1) 01/06/2014 11 / 33Academic IntegrityYou should not go to another classroom or lab section.All assignments and exams should be your own. Bring your student ID atevery exam.Do not use anybody else’s clicker. If you are caught having twoclickers, you and your friend will loose all participation points andyou will be reported for cheating.Cheating is a very serious offense and at a minimum will resultin your receiving an F in this class.(Lecture 1) 01/06/2014 12 / 33Any questions?(Lecture 1) 01/06/2014 13 / 33StatisticsThe material of this course consists of three parts:1Probability- We use probability in order to build models for the data.2Descriptive Statistics- We summarize and visualize a dataset.3Statistical inference- We use probability models in order to draw conclusions from datasets.(Lecture 1) 01/06/2014 14 / 33Statistics(Lecture 1) 01/06/2014 15 / 33Hans Rosling’s 200 Countries, 200 Years, 4 MinutesA great example of data visualisation (descriptive statistics) ...(Lecture 1) 01/06/2014 16 / 33The Statistician’s Objectives1Ask the right questions2Collect useful data3Summarize the data4Make decisions and generalizations based on the data5Turn the data and decisions into new knowledge(Lecture 1) 01/06/2014 17 / 33Introduction to Probability(Lecture 1) 01/06/2014 18 / 33Modeling Random PhenomenaProbability ModelsOutcomes: possible results of an experimentSample space/ Universe/ Population: set of all possible outcomes(denoted by S or Ω).Event: set of outcomes (e.g., A in the picture)(Lecture 1) 01/06/2014 19 / 33Example: Fair Die Rolling- Sample space:Ω = {1, 2, 3, 4, 5, 6}- Examples of events:O = “rolling an odd number” = {1, 3, 5}E = “rolling an even number” = {2, 4, 6}A = “rolling a 5 and a 6” = {5, 6}F = “rolling a 5 ” = {5}.(Lecture 1) 01/06/2014 20 / 33Example: Fair Die Rolling- If the die is fair, then the chance of getting each side is the same, i.e.Probability (one) = Probability (two) = Probability (three)=Probability (four) = Probability (five) = Probability (six)or, in other words,P({1}) = P({2}) = P({3}) = P({4}) = P({5}) = P({6}).(Lecture 1) 01/06/2014 21 / 33Example: Fair Coin Tossing- Sample space:Ω = {H, T }- If the coin is fair, then the chance of it landing in Tail or Head is thesame, i.e.Probability (head) = Probability