STAT 302 – Statistical MethodsLecture 1Dr. Anirban BhattacharyaAssistant ProfessorDepartment of StatisticsTexas A&M UniversityOutline• Preliminaries• What is Statistics• Course objectives• Key definitions2Course basics• Instructor: Dr. Anirban Bhattacharya (email: [email protected])• Office: 401D Blocker Building• Office hours: Monday 1:30 – 3 pm, Wednesday 2 – 3 pm or by appointment. These times are subject to change. Announcements will be made accordingly.• Syllabus: posted in Howdy and ecampus3Course basics• TA : Riddhi Ghosh(email: [email protected])• Office: TBA• Office hours: TBA4Prerequisites• Math 141, 147 or Math 166 or their equivalent is a prerequisite. • This course requires students to use elementary mathematics (algebra) and logical reasoning.• To do well in this course, you must be able to apply logical thinking and critical analysis to statistical problems.• However, if didn’t have a quantitative course recently, may need extra preparation.5Help Session• Help Session: You can get help with your homework and course notes in 162 Blocker. During the help session hours, graduate statistics students (at least 2) will be available to help you with your homework. • Hours: Monday – Thursday: 10 am – noon and 5 – 7 pm and Sunday: 3 – 5 pm. 6Course materials• All STAT 302 material will be disseminated via ecampus• Log in to www.ecampus.tamu.edu Alternatively, log into Howdy, you can find the ecampus tab on the top right corner• I have opened the course for you, so you should be able to view it• Go to the content tab on the top left corner. I shall create specific folders for lecture notes, quizzes, practice exams etc7Homework • Posted on Thursdays and due the next Thursday at 11:59 pm unless mentioned otherwise.• Lowest grade will be dropped !• Submitted online through WebAssign• Details will be given in class and also posted in ecampus• Constitutes 15 % of the total grade• Prepare you for the exams8Quizzes/Class participation• Regular quizzes in class on Thursdays• Constitute 5 % of the total grade• final ~ 10 - 15 minutes • Work out 3-4 problems• You can use your lecture notes during the quiz9Exams• There will be two midterms & one final exam• Midterm I: Tuesday, March 3: In class • Midterm II: Tuesday, April 14: In class • Final Exam: Tuesday, May 12: 1-3 pm in class • Details posted in syllabus about excused absence – if you miss an exam, your responsibility to contact me asap10Grade distribution 15% Homework20% Midterm 120% Midterm 240% Final exam5% Classroom quizzesA 90.00 - 100%B 80.00 – 89.99%C 70.00 – 79.99%D 60.00 – 69.99%F 0 – 59.99%11Required materials• The course notes posted periodically in ecampus will be self-contained. You can use The Practice of Statistics in the Life Sciences by Brigitte Baldi and David Moore, ed 2 for supplemental reading, though you are not required to buy this or any other book. • A calculator that has a square root function and can do calculations to at least 5 decimal places. Advanced calculators are allowed but you may not use the advanced features in an exam.• 3 large grey scantrons to be used for the 3 major exams. 12How to do well in this class• To do well in this course, students must be able to apply logical thinking and critical analysis to statistical problems.• The homework problems and practice exam problems will help you practice these skills. It is important to your success in this class that you put serious effort into your homework. The practice exams are meant for testing concept and you shouldn’t expect same type of questions in the exams. 13What is Statistics• “Statistics is a branch of mathematics dealing with the collection, analysis interpretation, and presentation of masses of numerical data.”Source: http://www.merriam-webster.com/dictionary/statistics• Statistics may be defined as “a body of methods for making wise decisions in the face of uncertainty”. WA Wallis• Statistics, as a subject matter, is a set of scientific principles and techniques that are useful in reaching conclusions about populations and processes when the available information is both limited and variable; that is, statistics is the science of learning from data.14Importance of Statistics• New York Times article from August 5, 2009:For Today’s Graduate, Just One Word: Statistics• “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write!” Samuel Wilks said this in his presidential address to the American Statistical Association in 1950.• "I keep saying the sexy job in the next ten years will be statisticians. ... The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades ...." Google’s chief economist Hal Varian: McKinsey Quarterly, January 2009Learning objectives• Descriptive Statistics: numerical and graphical summary of data• Probability: Mathematical foundation• Inferential Statistics: drawing conclusion about a population from data• Data collection: how to design a study16Language of Statistics (Key Definitions)Individuals: Data sets contain information about one or more groups of individuals. Individuals may also be called "subjects" or "experimental units" or "observational units." They may be people, animals, places, or things.Variables: Information is organized into variables. A variable is a characteristic of an individual. A variable can take different values for different individuals.Population: Group of individuals about which inferences are to be made.Sample: A subset of the population of interest. We gather information from the subjects in our sample to make inferences about the population.Language of Statistics (Key Definitions)• Simple random sample(SRS) of n subjects: Asample in which everysubset of n subjects ofthe population has anequal chance of beingselected.18Parameters and Statistics• A parameter is a number that describes the population. In practice, the value of a parameter is not known, because we cannot examine the entire population.• A statistic is a number that can be computed from the sample data without making use of any unknown parameters.• In practice, we use a statistic to estimate an unknown