Gasner, Anna

Nicholson, Aaron

Phillips, Steven T.

Tabibian, Mohsen

Weill, Jacob A.

English

University of Montana

University of MontanaScholarWorks at University of MontanaSyllabi Course SyllabiFall 9-1-2018STAT 216.00: Introduction to StatisticsSteven T. PhillipsUniversity of Montana - Missoula, steven.phillips@umontana.eduJacob A. WeillUniversity of Montana, MissoulaAnna GasnerUniversity of Montana, MissoulaMohsen TabibianUniversity of Montana, MissoulaAaron NicholsonUniversity of Montana, MissoulaLet us know how access to this document benefits you.Follow this and additional works at: https://scholarworks.umt.edu/syllabiThis Syllabus is brought to you for free and open access by the Course Syllabi at ScholarWorks at University of Montana. It has been accepted forinclusion in Syllabi by an authorized administrator of ScholarWorks at University of Montana. For more information, please contactscholarworks@mso.umt.edu.Recommended CitationPhillips, Steven T.; Weill, Jacob A.; Gasner, Anna; Tabibian, Mohsen; and Nicholson, Aaron, "STAT 216.00: Introduction to Statistics"(2018). Syllabi. 8205.https://scholarworks.umt.edu/syllabi/8205 1  STAT 216 - Introduction to Statistics – Syllabus Autumn 2018 Course Coordinator  Steven Phillips (steven.phillips@umontana.edu) Office: Math 301  STAT 216 on Moodle: All information for this course will be posted on Moodle.  Here you will find all handouts given in class and all notes covered during lecture.  This includes all lab worksheets, “hand-in” homework assignments, and the solutions to all assignments.  The lecture notes have significant amounts of material intentionally omitted so that we may cover this material during class.  If you miss a class, check Moodle for any announcements made that day, and copy the notes from a classmate.  I will not provide you with copies of notes you missed.  A course pack containing the blank notes is available in the bookstore.  Course Format: 3 lectures/week: MWF 10:00-10:50, North Underground Lecture Hall (NULH 101).  1 discussion/lab section per week: Thursday at the times listed below.  Attendance at discussion sections is mandatory.  Prerequisites:  M 115 (Probability and Linear Mathematics, previously labeled MATH 117), or 3 or more credit math course numbered 121 or above. Stat 216 Discussion Sections:  10:00am Lecture Section Time Room Instructor 1 8:00 MA 103 Jacob Weill 2 9:00 MA 103 Anna Gasner 3 10:00 MA 103 Anna Gasner 4 11:00 MA 103 Mohsen Tabibian 5 12:00 MA 103 Mohsen Tabibian 6 1:00 MA 103 Jacob Weill 7 2:00 MA 103 Aaron Nicholson 8 3:00 MA 103 Aaron Nicholson   Stat 216 Instructors: You may go to ANY of these instructors for help during their office hours, which will be posted on Moodle.  Name Office Email Steven Phillips Math 301 steven.phillips@umontana.edu Anna Gasner Corbin 359 anna.gasner@umontana.edu Mohsen Tabibian Math 206 mohsen.tabibian@umontana.edu Aaron Nicholson Math 011 aaron.nicholson@umontana.edu Jacob Weill Math 011 jacob.weill@umontana.edu  2   Learning Outcomes:  Upon successful completion of STAT 216, a student will: 1. be able to describe and explore sets of data both numerically and graphically. 2. know how to use the normal model for the distribution of a single variable and the linear regression model for the relationship between two variables. 3. know the basic principles of good experimental design and good sampling design. 4. know the fundamental ideas of statistical inference for means and proportions including both hypothesis testing and confidence intervals. 5. be able to interpret confidence intervals and P-values in the context of real problems. 6. be a critical consumer of statistical studies reported in the media.  Computing Information:  StatCrunch statistical software will be used by the instructors during class and will also be required for some homework problems throughout the course. This software can be accessed within MyStatLab.   Learning Catalytics: is a cloud-based learning and assessment system that we will use during the lecture portion of this course.  It is available through a link in Moodle, but in order to access it, you will need to have a device that can connect to the wireless internet in the lecture hall.  A device such as a smartphone, tablet, or laptop would work well.  This technology allows me to assess your understanding of the material, make the course more engaging, and collect data for our class to analyze.     Textbook:  Intro Stats (5th ed. Custom), by DeVeaux, Velleman, & Bock.  You automatically have access to the online textbook and resources through Moodle (unless you choose to ``opt out" of the tuition billing system). Calculators:  Since Math 115 is a pre-requisite for this course, it is expected that you already own a calculator suitable for this class and that you also know how to use it.  Your calculator should, among other things, be able to calculate probabilities from the Binomial, Normal, and t-distributions.  If you are thinking about purchasing a calculator for this course, students tend to be the most comfortable with the TI-83 or TI-83 Plus.  A list of other calculators is provided on the Moodle course page.  Please note that calculators with a QWERTY keyboard, smart watches, and cell phones are not allowed for use during exams.  Incomplete (I) Grades:   Incompletes (I's) are given at the discretion of the course coordinator (Steven Phillips) only.  See the 2018-2019 UM catalog for the conditions under which an "I" may be given. Credit/No-Credit Grades:  A D- grade is required to receive credit under the Credit/No-Credit option.  You will be allowed to change your grading option from Credit/No Credit to Traditional or vice versa up until December 7th.   Note: A course taken to meet a general education requirement (such as Stat 216) CANNOT be taken as Credit/No Credit.  See the 2018-2019 UM course catalog for more information.   3  Grading:  Your overall percentage for the course will be computed using the weights below.  We will also drop some of your lowest scores as indicated in the table. The +/- grading system will be used to assign final grades.  There is no strict grading scale for this course; however, the table below indicates the worst-case scenario for the letter grade breakdown.  For example, if you earn an 80% in the course, you are guaranteed no worse than a B-, and it could be higher.  No extra credit will be available. Grade Category Weight # Dropped MyStatLab Homework 10% 2 Hand-in Homework 8% 1 Worksheet/Lab Grade 7% 1 Exam #1 25% 0 Exam #2 25% 0 Exam #3/Final Exam 25% 0   Grade A- to A+ B- to B+ C- to C+ D- to D+ F CR (Credit) Less than % 90-100 80-89 68-79 57-67 57-100 57%  “Hand-in” Homework:  Weekly assignments will be posted on Moodle as a Word document.  It is expected that you type the answers into this document or neatly write in your work when appropriate.  This course largely focuses on teaching you how to interpret and analyze data.  We expect you to use correct grammar and spelling while providing clear and concise explanations.  These assignments will be handed in at the beginning of your lab on Thursdays.   NO LATE HOMEWORK WILL BE ACCEPTED FOR ANY REASON.  We will not regularly accept homework assignments via e-mail, but you may e-mail an assignment if you are sick and cannot make it to campus, with the approval of Steven Phillips.  Homework is not only a fairly substantial portion of your grade, but it is vital to your success in this class.  Working with other students on homework is allowed and encouraged, as long as you hand in your own work, and do not simply copy someone else's work.  Solutions to all problems from each assignment will be posted on Moodle.  OnlineHomework:  You will access your online homework problems and other textbook resources through Moodle (MyStatLab).  To initially gain access to this system, use the informational document posted at the top of our Moodle page.  Once you go through this initial login procedure, you will automatically have access to MyStatLab resources for the rest of the semester.  Any online assignments completed after the due date will only receive 50% of the total points earned. Recommended Problems:  Additional recommended problems will be assigned but not collected from each chapter, with answers provided in the back of the textbook. You are encouraged to work all of these problems.     4  Labs/Worksheets:  During the discussion sections, you will have the opportunity to ask questions about course material and work on problems with other students in small groups. You will complete a worksheet most weeks during the discussion section. The intent of the worksheets is to have you practice using statistical methods and to promote cooperative learning. Completing the worksheets in groups will allow you to discuss ideas and problems with other students. Your worksheet grade will reflect both your own work and the work of your group. *** Make-up worksheets will usually not be given, but notify Steven if you are unable to attend your lab due to a UM-sponsored activity. Tests: There will be 2 evening tests during the semester given in NULH and Urey lecture halls.    Test #1, Thursday, October 4th, 6:00-8:00pm   Test #2, Thursday, November 1st, 6:00-8:00pm  More will be said about the exams at a later date. If you cannot make it to an exam, you must let me know BEFORE the exam is given. No make-up exams will be given without a documentable reason for missing the exam.   Final exam:  The final exam will emphasize later material but will also cover some of the key elements from earlier chapters.  The final exam will take place on Thursday, December 13th at 8am.  Makeup exams will not be given due to travel plans.   Study Advice:  COME TO CLASS! STUDY THE NOTES! BE AN ACTIVE LEARNER! Read through the notes to be covered before coming to class and review them after class. Either print the notes from Moodle or purchase a course pack from the bookstore. This will reduce the volume of notes you will need to take in class and you will get more out of the lecture. Read the textbook to solidify your understanding of the topics introduced during lecture. Doing your homework as well as the additional recommended problems conscientiously will greatly increase your chance of success in this class. There will be STUDY JAM sessions held most Monday and Wednesday evenings in the U.C. commons (6:30pm-9:00pm). **Plan to spend 2 hours outside of class for each hour of class – Seriously!**  Adding/Dropping the Course:  September 5th is the deadline for students to drop or change a grading option via CyberBear.  September 17th is the deadline for students to drop/add a course or change the grading option using a course add/course change form.  After this date, a student is allowed to make changes by petition.  The final deadline for any and all changes is December 7th.  These policies are listed in the UM catalog at http://www.umt.edu/catalog.        5  Students with disabilities:  Students with disabilities may request reasonable modifications by contacting Steven Phillips.  The University of Montana assures equal access to instruction through collaboration between students with disabilities, instructors, and Disability Services for Students (DSS).  ``Reasonable" means the University permits no fundamental alterations of academic standards or retroactive modifications.  For more information, please consult http:www.umt.edu/disability.  Academic Misconduct:  All students must practice academic honesty.  Academic misconduct is subject to an academic penalty by the course instructor and/or disciplinary sanction by the University.  This includes, but is not limited to, copying answers on homework assignments, cheating on exams, and any type of dishonesty in your work.  All students need to be familiar with the Student Conduct Code.  The Code is available for review online at http://www.umt.edu/vpsa/policies/student_conduct.php       6  Tentative (Optimistic) Schedule  Date Day Chapter Topics Covered (Tentative!!) 8/27 Monday 1 Course Introduction, Data 8/29 Wednesday 2 Data, StatCrunch demo 8/31 Friday 2 Categorical Data Displays & Distributions 9/3 Monday  Labor Day (No Class) 9/5 Wednesday 3 Displaying & Describing Quantitative Variables 9/7 Friday 3 Mean and Standard Deviation 9/10 Monday 4 Comparing Distributions, Boxplots 9/12 Wednesday 5 z-scores 9/14 Friday 5 Normal Model and the Empirical Rule 9/17 Monday 5,6 More on Normal Models, Scatterplots 9/19 Wednesday 6 Scatterplots, Correlation 9/21 Friday 6,7 Correlation vs Causation, Linear Regression  9/24 Monday 7 Linear Regression 9/26 Wednesday 7 Linear Regression, Residual Analysis, & R2 9/28 Friday 8 Regression Wisdom, Multiple Regression 10/1 10/3 Monday Wednesday 8,9 8,9     Regression Wisdom, Multiple Regression Outliers,Multiple Regression   10/5 Thursday  EXAM 1 from 6:00pm to 7:30pm 10/8 Monday 10 Sample Surveys, Simple Random Samples 10/10 Wednesday 10 More Sampling Designs 10/12 Friday 11 Observational Studies, Experimental Design 10/15 Monday 11 More on Experimental Design 10/17 Wednesday 12 Randomness, Simulation, Probability 10/19 Friday           12 Randomness & Probability, Law of Large Numbers 10/22 Monday 13 Sampling Distribution of p̂  10/24 Wednesday 13 Confidence Interval for p 10/26 Friday 14 The Central Limit Theorem      10/29 Monday 14 Inferences about means, t-procedures 10/31 Wednesday 14 Confidence intervals for the Mean 11/2 Thursday  EXAM 2 from 6:00pm to 7:30pm 11/5 Monday 15 Hypothesis Testing for p, P-values 11/7 Wednesday 15 Hypothesis Testing for the Mean 11/9 Friday 15 Hypothesis Testing examples 11/12 Monday  Veterans Day Observed (No Class) 11/14 Wednesday 17 More about tests, P-values, Type I & Type II errors 11/16 Friday 17 Comparing two proportions 11/19 11/21 11/23 Monday Wednesday Friday 17   Comparing two proportions  Thanksgiving Travel Day (No Class) Thanksgiving Travel Day (No Class) 11/26 Monday 17 Comparing two proportions 11/28 Wednesday 17 Comparing two proportions 11/30 Friday 17 Comparing two means 12/3 12/5 12/7 Monday Wednesday Friday  17 17 17 Comparing two means Putting all Inference Procedures Together Putting all Inference Procedures Together  7   

STAT 216.00: Introduction to Statistics

https://scholarworks.umt.edu/cgi/viewcontent.cgi?article=9196&amp;context=syllabi

STAT 216.00: Introduction to Statistics

Abstract

Similar works

Full text

Available Versions

University of Montana