Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

Department of Mathematical and Computational Sciences

STA215H5F LEC 9101

Course Syllabus - Fall Term 2021

STA 215H5F, Introduction to Applied Statistics

Calendar Description

Calendar Description

This course introduces the basic concepts, logic and issues that form statistical reasoning. Topics include descriptive statistics, ex-ploratory data analysis, elementary probability, sampling distribu-tions, point and interval estimation, hypothesis testing for normal and binomial data and regression analysis.

Exclusions

[STA218H5 or STA220H5 or STA256H5 or STA257H5 or STAB22H3 or ECO220Y5 or ECO227Y5 or PSY201H5 or PSYB07H5 or SOC350H5 (SCI)].

Learning Objectives

1. Students will demonstrate an understanding of fundamental concepts, logic, and issues that form statistical reasoning. Calculations, pattern recognition and rote learning are minimized.

2. Students will discover interconnections between academic disciplines.

3. Students will be able to

(a) Make conjectures about a population based on descriptive statistics and graphical sum-maries of a sample data set.

(b) Make inferences about means and proportions for one or two populations using confi-dence intervals and hypothesis tests, recognizing the necessary limitations and potential errors in such inferences.

(c) Make inferences about the linear relationship between two population characteristics using least squares regression, recognizing the necessary limitations and potential errors in such inferences.

4. Successful students will be prepared for further study in discipline specific statistics courses.

Expectations

You can expect me, your instructor, to:

● plan the course and alter that plan as needed;

● provide you with class notes (slides) and opportunities to practice applying the course mate-rial;

● be respectful, courteous and provide a good learning experience;

● give you feedback as the course progresses, primarily by returning your tests in a timely manner and posting test solutions;

● answer emails within 24-48 hours; if on the weekend, by next business day at 5pm.

What I expect from you:

● read the class notes and watch the lecture videos;

● ask questions when you have any, on Piazza, to myself or your TA;

● be courteous and respectful to myself, the TAs and your classmates, for instance in tutorials and on Piazza;

● check your UT email regularly for course related communication;

● check Quercus regularly for new course material and for any announcements and important information.

Course Materials

Textbook and My-Lab

The required textbook for this course is STATISTICS (13th edition) by Mc-Clave and Sincich. There are certain topics that will be explained differ-ently in the text; in these cases, please follow the course notes. We will also be using the digital platform MyLab, which is provided by the publisher of the book. Through the bookstore you will have the option to buy access to MyLab with the e-text (ISBN9780135834435), or access to just MyLab (ISBN 9780137668649).

Course Notes

The course notes (slides) are the primary component of the course ma-terial and will contain statistical concepts and information on how R software can be used in tandem with these concepts. These notes will be posted on Quercus in modules according to Chapters in the text book. As mentioned above, if there is ever a discrepancy (e.g. in notation, method-ology) between the textbook and course notes, please follow the course notes.

Videos

Lecture videos going over the course slides will be made available via Quercus. These videos will be posted in tandem with the course slides. While I tried to minimize background noises, there may some in the videos and please forgive me.

Practice Problems

Lists of suggested problems (from the textbook) will be posted on Quercus. Your TA will work through some of them in the tutorial. It is critical for you to learn how to write up solutions, so it is imperative you work on these problems and attend tutorials.

Course Evaluation

Your final mark for the course will be based on the following components:

Course Assessments

Quizzes

There will be 11 weekly quizzes on MyLab starting the week of September 13. They will be available each Thursday at 5pm EST and be available until Monday at 5pm EST. They will consist of 3-5 basic questions based on the most recent material and take place in MyLab. There will be a time limit for each question. The purpose of the quizzes is to help you keep on track with the course materials and to help you prepare for the tests and final exam. The best 10 of the 11 quizzes will count towards your grade. There are no makeup quizzes. If you miss a quiz for any reason, it will receive a grade of 0. There are no exceptions to this rule.

Tests

There will be four equally-weighted in-class tests to take place in Quercus. They are scheduled for September 29, October 20, November 10 and December 1. For announcement purposes only, you are required to be on Zoom during each test (your camera does not need to be on); the Zoom information will be provided prior to each test.

If you miss a test due to medical reasons or emergency, or some other rea-sons deemed legitimate by your instructor, please declare yourself absent on ACORN on the day of the missed test or the day after at the latest. The weight of any missed test gets shifted to the final exam.

In case you experience a technical problem, you may report yourself as being absent in ACORN. In this case, we will check your activity on Quercus to spot all inappropriate claims. If the claim appears to be inadequately substantiated, the mark assigned for this test will be zero. You will receive an email from us in such case. Alternatively, if your claim is found to be valid, then the policy on missed tests described above will be applied.

There will be no makeup tests. Further details about each test will be provided well in advance.

Final Exam

Your final exam will be 3 hours. If you cannot complete your final ex-amination due to illness or other serious causes, you must file an online petition within 72 hours of the missed exam. Late petitions will not be considered. You must also record your absence on ACORN on the day of the missed exam or by the day after at the latest. Upon approval of a deferred exam request, a non-refundable fee of $70 is required for each exam approved.

Important Note

Your online assessments are all open book and you are permitted to use R and a scientific calculator, however, you are advised to be informed about Academic Integrity and to know what constitutes an academic offence. Students failing to abide by these regulations will be subject to sanctions/penalties as laid out in the university’s Code of Behaviour on Academic Matters.

Remark Policy

If you feel that there is an issue with the marking of a test, you may request it to be re-marked. The course re-mark policy exists to correct mistakes, and any request should clearly identify the error (for example, a question that was nor marked or a total that was incorrectly calculated). Requests to correct such mistakes must be sent by email to your instructor and not your TA.

To be considered for a remarking request, your email:

1. must be received within three business days of the date of when the graded test was first available.

2. must include STA215 in the subject line of the email, must include your full name and student ID number and must give a specific, clear, and concise reason for each request, referring to a possible error or omission by the marker. Remarking requests without a specific reason will not be accepted.

Note that your entire test may be remarked when submitting a request.

Important Dates

Academic Integrity

Honesty and fairness are fundamental to the University of Toronto’s mission. Plagiarism is a form of academic fraud and is treated very seriously. The work that you submit must be your own and cannot contain anyone else’s work or ideas without proper attribution. You are expected to read the handout How not to plagiarize and to be familiar with the Code of behaviour on academic matters, which is linked from the UTM calendar under the link Codes and policies.

With regard to remote learning and online courses, UTM wishes to remind students that they are expected to adhere to the Code of Behaviour on Academic Matters regardless of the course delivery method. By offering students the opportunity to learn remotely, UTM expects that students will maintain the same academic honesty and integrity that they would in a classroom setting. Potential academic offences in a digital context include, but are not limited to:

1. Accessing unauthorized resources (search engines, chatrooms, Reddit,etc.) for assessments.

2. Using technological aids(e.g.software) beyond what is listed as permitted in an assessment.

3. Posting test, essay, or exam questions to message boards or social media.

4. Creating, accessing, and sharing assessment questions and answers in virtual ”course groups.”

5. Working collaboratively, in-person or online, with others on assessments that are expected to be completed individually.

All suspected cases of academic dishonesty will be investigated following procedures outlined in the Code of Behaviour on Academic Matters. If you have questions or concerns about what constitutes appropriate academic behaviour or appropriate research and citation methods, you are expected to seek out additional information on academic integrity from your instructor or from other institu-tional resources.

Use of Copyrighted Material

Please be aware of copyright laws during this course. All notes/slides, assessments and solutions are either my own intellectual property or that of the University. We may also use other copyrighted content in this course. I will ensure that the content I use is appropriately acknowledged and is copied in accordance with copyright laws and University guidelines. Copyrighted material must not be distributed in any format without permission, and this implies that you do not have permission to upload any course material (or other copyrighted material) to any note sharing website. As per copyright rules, special attention should be given to my videos. All of my video recordings belong to me and cannot be used for any other purposes by any other party without my permission.

More details are available online at Copyright at the University of Toronto Mississauga.

Accessibility Needs

The University of Toronto Mississauga is committed to accessibility. If you require accommodations for a disability, or have any accessibility concerns about the course, or require further information, please contact Accessibility Services as soon as possible. Academic accommodations must be ar-ranged for each term of study. In addition to arranging accommodation every term, students must inform accessibility of accommodations necessary for each and every test.

Technological Requirements

You will require the following minimum technological requirements:

1. A computing device where one can create and edit documents.

2. An internet connection capable of streaming videos and downloading software.

For a more complete list of requirements set by the university see here.

Additional Information

Statistical Computing: This course uses the statisti-cal package R. R is free statistical software and it can be downloaded from https://www.r-project.org/. Introduc-tions to R and the platform RStudio will be provided and computing examples will be done in the lecture videos.You should use R for all assessments.

Supplemental Readings: In addition to the required textbook, some other useful resources are:

● Introduction to the Practice of Statistics by Moore and McCabe

● Cartoon Guide to Statistics by Gonick and Smith

● Stats: Data and Models by DeVeaux, Velleman and Bock

Crowdmark: Some of your assessments may be marked using the Crowdmark software, an on-line grading tool. Theses assessments will be written by you on paper and then scanned and uploaded through a link you will be provided over email. While you may take a photo of your paper, due to the high quality of most camera phones, it is recommended that you use the app Cam Scanner (or something similar) to take the photos of your work. There will be a trial run of the software prior to the first assessment that uses it. Once graded, an electronically marked copy of your assessment will be emailed to your UT e-mail address. I will send out an email when the marked copies have been sent. Please check your spam folders if you do not see it in your inbox.

Piazza: Piazza is an online discussion platform. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to myself or your TA (which you are always welcome to do), I encourage you to post your questions on Piazza. Your TA and instructor will regularly check in. Please note the course page on Piazza is across all three sections.

Privacy and Use of Course Materials Notifications: This course, including your participa-tion, will be recorded on video and will be available to students in the course for viewing remotely and after any live video session. Course videos and materials belong to your instructor, the Uni-versity, and/or other source depending on the specific facts of each situation, and are protected by copyright. In this course, you are permitted to download session videos and materials for your own academic use, but you should not copy, share, or use them for any other purpose without the explicit permission of the instructor. For questions about recording and use of videos in which you appear please contact your instructor.

Email Policy: Any emails to your instructor must originate from your University of Toronto email account. The subject line should contain the course number and a relevant subject (indicating what the email is about). Be sure to include your full name and student ID number in the body of the message. You will not get a response if you send your email from other email addresses or do not follow the email policy. Before you send an email, make sure that you are not asking for information that is already available from the course syllabus/website/announcements, or questions about the course material that are more appropriate for discussing during office hours. If you do not get a response, this may well be a reason. In general, your instructor is unable to answer technical questions about the course material by email. In addition, if you email your instructor with a general question about the course (that is likely answered on the discussion board), you may not get a response.

Own your learning: You are responsible for your own learning. We really want to help you learn, but in the end it’s up to you! Use office hours early, and use them often. Make an appointment with your instructor. Keep asking questions until you’re satisfied. Ask about big concepts or small details - there is no such thing as a stupid question! Always take advantage of extra help - don’t wait until it’s too late!

Tentative List of Topics

Unit 1 – Examining Data

● obtaining data, types of variables: quantitative, categorical, nominal, ordinal

● graphs for categorical variables: bar charts, pie charts

● graphs for quantitative variables: stemplots, histograms

● describing distributions with numbers: mean, weighted mean, median, quartiles, per-centiles, interquartile range, range, variance and standard deviation

● five-number summary and boxplots

● outliers, the 1.5 × IQR rule for suspected outliers, outlier boxplots

● resistant measures

● introduction to R

Unit 2 – Scatterplots, Correlation and Regression

● association, response variable, explanatory variable

● examining scatterplots

● correlation

● least-squares criterion and least squares regression line

● r2

● residuals, outliers, influential observations

● cautions about correlation and regression

● association vs. causation, lurking variables

● extrapolation

Unit 3 – Randomness and Probability

● randomness, the language of probability

● probability models, sample space, events, unions, intersections

● some probability rules, independence, general addition rule

● conditional probability and Bayes’ theorem

Unit 4– Random Variables

● discrete random variables

● binomial setting and binomial distribution

● continuous random variables, density curves

● uniform distribution

● normal distribution

Unit 5 – Sampling Distributions

● sampling distribution of a sample mean

● bias and variability

● Central Limit Theorem

● sampling distributions for proportions

Unit 6 – Confidence Intervals for a Single Population Mean

● confidence intervals for σ known

● selecting sample size

● introduction to the t-distribution

● confidence intervals for σ unknown

● confidence intervals for p

Unit 7– Tests of Significance for Single Populations

● tests for a single population mean (σ known and unknown)

● test of significance for population proportions

Unit 8 – Inference for the Means of Two Populations

● matched pairs t procedures

● inference when population variances are equal

● inference when population variances are unequal