Statistical Reasoning : 90-707 Fall, 2017 Course Information Instructor: Kathy Smith Office Hours: 3013 Hamburg Hall Monday: 3:00 5:00 pm Phone: (412) 268-4408 or by appointment Email: ks54@andrew.cmu.edu Teaching Assistants: Mallory Nobles e-mail: mnobles@andrew.cmu.edu Stephen Munchel e-mail: smunchel@andrew.cmu.edu Haley Townsend e-mail: htownsen@andrew.cmu.edu Recitation and TA Office Hours: Friday Recitation: 9:00 10:20 a.m. TA Office Hours : to be announced Course Materials: Weiss, Neil A. Introductory Statistics, 10 th edition, Pearson, 2016. You may use an older edition. However, all page numbers, chapters, and problem numbers on the syllabus, assignments and outline sheets refer to the current edition. If you are using an older edition it is your responsibility to be certain that you are using the correct information. Course Description: This course will provide an introduction to the principles of data collection, description and analysis. You will learn the basic tools of statistical inference and modeling, as well as how to interpret results, measure the uncertainty in result, and understand the limitations of results. You will also learn how to interpret statistical output, and how not to be fooled by statistical studies. Course Objectives: At the end of this course, you should be able to Present data visually in tabular and graphic form Summarize a set of observations by reporting a measure of center and dispersion Syllabus Statistical Reasoning - Fall, 2017 Page 1
Explain how and why sample data can be used to estimate descriptive measures of populations when census data is unavailable, and how we measure the accuracy and precision of the estimate Apply the basic rules of probability Find and interpret the probability for a random variable which has a normal distribution Explain how to take a proper scientific sample that can be used to make inferences about the larger population Explain what sampling error is and why it exists Classify data by type and use the proper summary statistics and tests for the data type Interpret the p-value, test statistic and other Minitab and/or Excel output from a test of hypotheses, confidence interval, and linear regression Course Organization I have divided the course into four units. Unit 1 focuses on tools for summarizing and describing a set of data. Graphical, tabular and numeric tools will be examined. We will also study probability in this unit, which is the tool that allows us to make scientific, sound estimates and conclusions from sample data. Unit 2 focuses on probability. Probability theory is the foundation that allows us to take the step from describing data that we have observed, to making projections, estimates and/or conclusions about the larger set of data in the population. Exam #1 will be given at the end of Unit 2. In Unit 3, you will learn how to make inferences about the larger population. You will make estimates about the population, with some degree of confidence and allowance for error. You will also test hypotheses about characteristics of the larger population and come to scientific, reproducible conclusions. Exam #2 will follow Unit 3. In the final unit, Unit 4, we will use regression analysis to model relationships between variables. Correlation analysis and Analysis of Variance (ANOVA) will also be studied. The Final Exam will cover the material in Unit 4, plus measure your understanding of how the various components of the class are related and used. Class Format and Preparation At the beginning of each class, I will distribute Class Notes, including Class Examples that will be done during that class, and Practice Problems for you to work on after class. Supplementary readings, Minitab and/or Excel instructions and other materials will be posted to Canvas to be printed or read at your convenience. Class Recordings: Class will be digitally captured during the semester. Links to the lectures will be typically available within 48 hours. You may use these recordings to review material that was covered in class, make up material that you missed due to illness, an interview, or similar reason, and to prepare for the exams or assignments. The recorded lectures are not to be used as a substitute for attending class or for being attentive in class. If I notice these changes in student behavior, I will discontinue posting of the recordings. Syllabus Statistical Reasoning - Fall, 2017 Page 2
Policies Attendance Attendance is not required; however, it is strongly recommended that you attend class meetings. If you must miss a class, you can catch up by watching the video for the day. Classroom Etiquette and Behavior You should arrive on time for class and be seated and ready to go when the class begins. I start class promptly at 1:30. Once you have arrived at class, you are expected to remain in the room until class has ended. No one should leave the classroom except for an emergency. All laptops, cell phones, ipads and similar devices must be stored away, off the table tops, and may not be used during class. Exams Exams must be taken on the scheduled exam date. Do not schedule travel, interviews, or other activities during the days or times when exams will be given. You may use a one-page, handwritten page of notes for each exam. More details TBA. You may use a non-messaging calculator during exams. I have loaners if you need one. Assignments Read all assignments carefully and follow the directions! You will lose points or receive no credit at all if you do not follow the directions. Type your name at the top of the first page. The TAs will print the assignments to grade them, and they must be able to read your name on the printed copy. Assignments should be submitted to Canvas by the due date and time in order to receive full credit. Keep your receipt when you submit in case there is any problem. Assignments submitted within 12 hours following the deadline will be accepted, but you will receive a 10% penalty (i.e. a 4 point deduction) for being late. No credit will be given for an assignment submitted more than 12 hours late, unless you have made special arrangements with me in advance. Even then, I will accept late assignments only under extreme circumstances. Submitted Work: Exams must be done individually. No communication with anyone else is permitted. You may work with other students on assignments; however, you must submit your own homework assignment created entirely by you. No copying, cutting and pasting from another students work or from any other source. Checking Canvas: Announcements and other information will be relayed via Canvas. You should check Canvas on a regular basis, as you will be responsible for and for any information or changes that are posted to Canvas. Syllabus Statistical Reasoning - Fall, 2017 Page 3
Cheating The following (non-exhaustive) items are situations that will be considered cheating: Receiving information from, or giving information to another person during an exam Receiving information from any other source (other than your note sheet) during an exam Copying words from another source (a published source, another student, or a class handout) and submitting them as your own on an assignment or exam. Using all or part of a Word document or Minitab/ Excel output that was created by someone else Allowing another student to use a Word document or Minitab/Excel output that you created If you do any of the above or otherwise cheat on an exam or assignment, the following will occur: You will receive a zero on the assignment or exam. If it is an assignment, you will not be able to drop the zero grade. In addition, for lesser offenses (e.g. copying on an assignment), your final grade for the course will be dropped by one full letter grade, (e.g. from a B+ to a C+) For more serious offenses, you will fail the course In either case, a letter describing the infraction will be sent to your Program Director, the Associate Dean, and the Dean of Student Affairs at the university. They will determine if expulsion or other further action is recommended. For more information on cheating and your rights, refer to http://www.cmu.edu/policies/student-and-student-life/academic-integrity.html Grading and Returning Assignments and Exams The TAs and I are committed to returning work to you as quickly as possible. Our goal is have assignments graded and ready for within 1 week after the due date. Graded assignments will be available for pick up from my faculty assistant, Natalia Pascal. Further details will be forthcoming. Exams will graded and scores available within one week. Exams will not be returned; however, you may come to my office hours or to the TA office hours to view your exam paper and ask questions. You are not permitted to photograph or otherwise make copies of your exam paper. Doing so is considered cheating, and will be treated as such. Grading Disputes and Appeals All disputes and appeals for grading on assignments must be made in writing within 2 weeks of the due date of the assignment no exceptions! The written appeal must include the number of the question and the specific reason why you believe that the grade that you received was incorrect. To dispute a score, you must submit the original graded paper, along with your written appeal, to one of the TAs before or after class or during their office hours. When you submit an assignment or exam for dispute, the entire paper will be re-graded. Your resultant score may be higher, the same, or lower than the original score on the paper. Syllabus Statistical Reasoning - Fall, 2017 Page 4
Performance Measures Assignments: Eleven assignments, each worth 40 points, will be made during the term. I will drop the lowest remaining homework score at the end of the semester. All assignments, with the exception of Assignment #1 are due Sunday evenings at midnight. Assignments should be submitted via Canvas. The TAs will give you further instructions on homework submissions. Exams: Two exams will be given in class on October 2 and November 6. The final exam will be given in finals week, Dec. 11-15, during the time period scheduled by the university. You will be permitted to bring one handwritten sheet of notes with you to use during the exams. More details will be given near the exam dates. The three exams are not comprehensive and will focus on the material covered since the prior exam. However, the material in the course is, by nature, comprehensive. Performance Evaluation Your final grade for the course will consist of grades from the items mentioned above. They will be weighted as follows: 15 % Homework 25 % Exam # 1 30 % Exam # 2 30 % Final Exam Each assignment and exam grade will be weighted as above and you will end up with a score between 0 and 100. (Numbers will be rounded in the usual way.) Your final grade will be determined by your placement on the following scale: 90 100 A 65 69 C+ 85 89 A- 60 64 C 80 84 B+ 55 59 C- 75 79 B Below 55 R 70 74 B- Notice that I do not curve grades, nor do I grade to the average. You must master a minimum of 55% of the material (as measured by the points that you receive on assignments and exams) in order to pass the course, 75% to receive a grade of a B, etc. Syllabus Statistical Reasoning - Fall, 2017 Page 5
Final Notes Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress. All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful. If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help. Note: The above statement was the result of a university task force headed by Provost and Chief Academic Officer, Farnam Jahanian. The text is above has been copied (with permission) and pasted from an email by Provost Jahanian. If you have questions about this or your coursework, please let me know. get overwhelming. Don t wait until things Syllabus Statistical Reasoning - Fall, 2017 Page 6
Introductory Statistical Methods Syllabus Fall, 2017 ** Topics and the Lecture dates are tentative. I will make changes or adaptations as necessary throughout the semester. The Assignment due dates and Exam dates will remain the same as shown below. Any changes to this syllabus other updates posted to Canvas throughout the semester will supersede those printed below. Week Date Topic 1 Aug. 28 Graphical Methods Relevant Materials Ch. 1: Skim Ch. 2: All Aug. 30 Numeric Summaries Ch. 3: All Due 2 3 4 5 Sep. 4 NO CLASS LABOR DAY Sep. 6 Numeric Summaries Ch. 3: All Sep. 11 Probability Rules Ch. 4: 4.1 4.6 Sep. 13 Probability Rules Ch. 4: 4.1 4.6 Sep. 18 Bayes s Rule Ch. 4: 4.7 Sep. 20 Random Variables Probability Distributions, Binomial D. Ch. 5: 5.1 5.3 Ch. 4: 4.8 Sep. 25 Normal Distribution Ch. 6: 6.1 6.3 Sep. 27 Normal Distribution Ch. 6: 6.1 6.3 Assignment # 1 due Sep. 4 at 11:59 p.m. (midnight Monday) Assignment # 2 due Sep. 10 at 11:59 p.m. (midnight Sunday) Assignment # 3 due Sep. 17 at 11:59 p.m. Assignment # 4 due Sep. 25 at 11:59 p.m. 6 7 8 Oct. 2 EXAM # 1 Oct. 4 Sampling Distributions Ch. 7: All Ch. 12: pp. 546-547 Oct. 9 Confidence Intervals for Means Ch. 8: All Oct. 11 Confidence Intervals for Proportions Ch. 12: 12.1 Oct. 16 Hypothesis Tests 1 Sample Ch. 9: 9.1, 9.3-9.5 Oct. 18 Hypothesis Tests 1 Sample Ch. 12: 12.2 Assignment # 5 due Oct. 8 at 11:59 p.m. Assignment # 6 due Oct. 15 at 11:59 p.m. Syllabus Statistical Reasoning - Fall, 2017 Page 7
Week Date Topic 9 10 Relevant Materials Oct. 23 Hypothesis Tests 2 Samples Ch. 10: 10.1 10.3 Oct. 25 Oct. 30 Hypothesis Tests 2 Samples Hypothesis Testing Critical Value Approach Ch. 10: 10.5 Ch. 12: 12.3 Ch. 9: 9.2 Nov. 1 Sampling and Experimental Designs Ch. 1: 1.2 1.4 Due Assignment # 7 due Oct. 22 at 11:59 p.m Assignment # 8 due Oct. 29 at 11:59 p.m 11 12 13 Nov. 6 EXAM # 2 Nov. 8 Analysis of Variance Ch. 16: 16.1 16.3 Nov. 13 Correlation and Simple Regression Ch. 14: All Nov. 15 Simple Regression Ch. 14: All Nov. 20 Inferences in Simple Regression Ch. 15: 15.2 15.4 Nov. 22 NO CLASS - THANKSGIVING Assignment # 9 due Nov. 12 at 11:59 p.m Assignment # 10 due Nov. 19 at 11:59 p.m 14 Nov. 27 Multiple Regression Module A Nov. 29 Multiple Regression Module A 15 Dec. 4 Dec. 6 Final Topics Final Topics Module B: Multi-collinearity Assignment # 11 due Dec. 3 at 11:59 p.m 16 FINAL EXAM Syllabus Statistical Reasoning - Fall, 2017 Page 8
INTRODUCTORY STATISTICAL METHODS Student Information Sheet Fall, 2017 Name Preferred Name (if different) Area of Interest at Heinz Past Experiences or Work / Undergrad Degree Home Country (International Students) Anything Else of Interest that you would like to Share: Syllabus Statistical Reasoning - Fall, 2017 Page 9