RES-ECON 212: Introductory Statistics for the Social Sciences

Example syllabus only – exact content subject to change. Please see your instructor’s syllabus for the current term for your specific course’s guidelines

Instructor: Dr. Wayne-Roy Gayle

Office Hours: Mondays 5:45 – 6:45pm; Tuesdays 4:15 – 5:15pm

Office Location: Stockbridge Hall 217A

Email: @email

Head TA: Nima Rafizadeh

Email: @email

Teaching Assistants:

Supportive Work Environments

Email Policy

All material-related questions (i.e. - related to homework, clarification on statistical concepts, technical errors, etc.) should be posted on the public FAQ Canvas forums.

All administrative issues (grades, absences, etc.) should be sent to: @email

Inquiries sent to this address are only seen by the instructor and head TA.

Please allow 48 hours for replies from the instructor and TAs.

Course Description

This is a blended class. Lectures will be posted online. Students will complete online homework and will then attend a team-based learning (TBL) section once a week where they will work in teams on exercises that reinforce and apply the concepts from the online lectures and homework exercises. The online lectures and homework will be completed before attending the TBL section.

Course Objectives

Statistics is a field of study, or science, in which we make inferences about populations based on a sample of data. There are two major fields in statistics: mathematical statistics deals with the theoretical underpinnings of the subject and focuses on developing new statistical methods; applied statistics deals with the application of statistical methods to solve problems in other fields of study. This course will focus on the application of statistical methods to Social Sciences and Business. When you complete this course, you will have a working knowledge of the methods and skills needed to organize data, conduct meaningful analysis, and draw inferences from samples about a population.

The skills you will learn can be used in your everyday decision-making and communication.

Our broad goal is that you become an active consumer of statistics and a practitioner of statistical analysis. To accomplish this goal, you will need to learn:

• The meaning and appropriate uses of the two broad statistical methodologies: descriptive and inferential. When statistics are used in a misleading manner and what statistic, or statistics would be appropriate.
• The construct of different data sets, the types of data included, and which data are appropriate to answer research questions or support policy statements.
• The development and use of appropriate descriptive statistics for both qualitative and quantitative data analysis. The proper interpretations for qualitative and quantitative data displays (graphs, charts, etc.). The proper applications and interpretations of numeric summary statistics.
• How to appropriately use sample data and statistics to make inferences about the properties or characteristics of a population.

This is a four-credit course, and the structure of this course by which you will learn the above concepts fulfills the R1 and R2 designations of the General Education (Gen Ed) Program. In particular, you will be introduced to descriptive statistical methods of collecting, summarizing, analyzing and interpreting numerical data using visual displays and numeric measures. You will develop an understanding of the pitfalls of various summary statistics and be taught how to identify misleading visual displays and misuse of numerical measures. You will also be introduced to elementary probability theory, statistical estimation, and hypothesis testing. Mastery of these topics will advance your formal/analytical reasoning skills and improve your sophistication as a consumer of numerical information. You will apply these methods by working in teams to complete a semester project that addresses an empirical question of your design. This project development aims to improve your ability to think critically and analytically, obtain and process information using theoretical concepts and empirical methods, and demonstrate clear and effective writing skills. You will be introduced to Microsoft Excel, which will be the primary statistical software used to complete your semester project. This course will satisfy the R1 requirements upon completion because it presupposes knowledge of basic math skills.

Department Learning Objectives and Experiential Goals

This course contributes to the following student learning objectives for undergraduate students in the Department of Resource Economics:

Required Materials

CANVAS

The following will be posted on Canvas on a weekly basis:

• Pre-TBL videos and contents
• Project Components
• TBL class activities
• Question/Answer forum.

The following will be submitted to Canvas:

• SWE activities
• Project Drafts

STATISTICAL SOFTWARE

We use Microsoft Excel predominantly in this course.

As a UMass Student you can receive a free download of the Microsoft Office Program through IT at the following link: https://www.umass.edu/it/microsoft-office-365-education

Supplemental Materials

You will have the option between two textbooks.

The first is a FREE online statistics textbook developed by Rice University, University of Houston Clear Lake, and Tufts University. The textbook can be found on the Course Canvas page or at the following link: www.OnlineStatBook.com

The second is Applied Statistics in Business and Economics Volume 1, by David Doane and Lori Seward, Chapters 1-9 (4^th Edition).

Grading Policy

Minimum Grade Guarantee:

We abide by the following minimum grade cutoff points based on a percentage of the total points available: A = 95, A- = 90, B+ = 85, B = 80, B- = 75, C+ = 70, C = 65, C- = 60, D+ = 55, D = 50, and F<50

Description of Grading Components

Late in-class or out-of-class work is not accepted for credit without an acceptable and documented University excused absence. *

*Please visit the following for specifics on accepted University excused absences: http://www.umass.edu/registrar/students/policies-and-practices/class-absence-policy

Out-of-Class Work:

Lecture Quizzes

Progressing through online lectures requires you to complete quizzes associated with videos that cover specific concepts. These quizzes account for 18% of your final grade.

You must score a minimum percentage on each quiz to move forward. We recommend that you work with your peers and consult your TAs and instructor when working on the lecture quizzes.

OWL Homework

Weekly homework assignments are on OWL and are based on online material posted and TBL session content. Your score from these weekly homework assignments will account for 17% of your course grade.

You are welcome to work with your peers and consult your TAs and instructor when working on the homework problems. Homework assignments must be completed 1 hour before your weekly TBL session.

You have unlimited number of tries, but only the highest grade will count towards each homework grade. You may not see the same question on each trial or have the same questions as your peers. The lowest homework grade is dropped.

BEGIN HOMEWORK EARLY! YOU WILL NOT BE EXCUSED FOR A TECHINCAL ISSUE.

In-Class Work:

Individual Readiness Assessment Tests (IRAT)

At the beginning of select classes we will have an IRAT. The IRATs contain a few questions based on the videos and the textbook to test your preparation for the weekly TBL session.

You may not use your notes or computer to complete these assessments, nor are you allowed to consult with your classmates.

Your IRAT scores will count as 5% of you final grade. The lowest IRAT grade is dropped.

In-Class Work and Project Development

During this activity your table will work on applying the material learned for that week to empirical assignments and your group projects.

These sessions are graded on a check scale. Your combined score from these weekly in-class assignments accounts for 5% of your course grade. These activities are graded using the following scale:

For all team assignments, everyone on the team receives an individual grade, which is recorded on sign-in sheets handed out at the beginning of each session.

Print your name as recorded on Canvas on the sign-sheets. During select weeks your in- class work is submitted by the end of the TBL session. Activities that are not finished during a session should be completed for showing the next session.

FOR ALL IN-CLASS WORK - If you are absent, you receive an individual grade of 0 no matter what grade your team received. Makeup is not given without an acceptable University excused absence.

If you have an acceptable University excuse, you should attend one of the SWEs and provide the documentation to the TAs before your next TBL session. They will have you make up the activities you missed.

You fail the course if you are absent for 6 or more sessions without an acceptable University excuse, regardless of your performance on the other components of the course.

Text messaging and social media activities are not allowed in this class. The TBL sessions require that you complete activities as a team. If you are on social media or messaging during class, then you are not fully contributing to your team, in which case you will lose your in-class performance points. Also, these activities are distracting to me and the rest of the class. Therefore, you will be asked to leave the class. You may step out if you have an emergency which requires your immediate attention.

SWE Work

Your team will attend weekly SWEs to work on specific sections of your project and the TAs present during these sessions will check your work and record your attendance. You will be awarded 5% if you attend all these SWE sessions. However, you will lose all 5% if you fail to attend two or more of these sessions.

Final Project

The object of this course is to become consumers and practitioners of statistics. To help achieve this goal, you will complete a final project in the form of a statistical report with your team of 9. Your team will explore a research question that you all come up with using real world data. You have time in each TBL session to work on your project with your teams, but your teams may need to meet outside of class as well. At the end of the semester each table submits a report containing the following sections:

Further descriptions can be found in the Project section of the Canvas site. Your project will be turned in by your team in a series of drafts. The final project accounts for 30% of your final grade. The deadlines and grading breakdown for the project are as follows:

The project grading rubric (posted on the Canvas site) provides a detailed breakdown of the allocation of grades to different sections and subsections of your project. While your group project is worth up to 20% of your final grade, your individual level of contribution to its development will be graded as follows:

This means that if you are a marginal contributor, the highest grade you can earn in this course is 90%, and if you are a non-contributor, your highest possible is 80%. Along with each draft, you will submit a document listing the names of the contributors, the marginal contributors, and the non-contributors. Please refer to the project grading rubric for further details.

Exams

Three exams will be issued throughout the semester. Each exam is comprehensive but will mostly cover the material post previous exam.

The percentage towards your final grade that a particular exam is worth is based on the scores of all your exams. Your best of the three exams will count as 16% of your final grade, while the other two will count as 7% each.

Additional Course Policies

Academic Honesty

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University. Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. Instructors should take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair. Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent (http://www.umass.edu/dean_students/codeofconduct/acadhonesty/).

Academic Integrity

UMass Amherst is strongly committed to academic integrity, which is defined as completing all academic work without cheating, lying, stealing, or receiving unauthorized assistance from any other person, or using any source of information not appropriately authorized or attributed. As a community, we hold each other accountable and support each other’s knowledge and understanding of academic integrity. Academic dishonesty is prohibited in all programs of the University and includes but is not limited to: Cheating, fabrication, plagiarism, lying, and facilitating dishonesty, via analogue and digital means. Sanctions may be imposed on any student who has committed or participated in an academic integrity infraction. Any person who has reason to believe that a student has committed an academic integrity infraction should bring such information to the attention of the appropriate course instructor as soon as possible. All students at the University of Massachusetts Amherst have read and acknowledged the Commitment to Academic Integrity and are knowingly responsible for completing all work with integrity and in accordance with the policy: (https://www.umass.edu/senate/book/academic-regulations-academic-integrity-policy)

Learning Accommodations

The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements. For further information, please visit Disability Services (https://www.umass.edu/disability/)

Title IX Statement

In accordance with Title IX of the Education Amendments of 1972 that prohibits gender-based discrimination in educational settings that receive federal funds, the University of Massachusetts Amherst is committed to providing a safe learning environment for all students, free from all forms of discrimination, including sexual assault, sexual harassment, domestic violence, dating violence, stalking, and retaliation. This includes interactions in person or online through digital platforms and social media. Title IX also protects against discrimination on the basis of pregnancy, childbirth, false pregnancy, miscarriage, abortion, or related conditions, including recovery. There are resources here on campus to support you. A summary of the available Title IX resources (confidential and non-confidential) can be found at the following link: https://www.umass.edu/titleix/resources. You do not need to make a formal report to access them. If you need immediate support, you are not alone. Free and confidential support is available 24 hours a day / 7 days a week / 365 days a year at the SASA Hotline 413-545-0800.

AI is Prohibited

This course assumes that all work submitted by students will be generated by the students themselves, working individually or in groups. Students should not have another person/entity do the writing of any substantive portion of an assignment for them, which includes hiring a person or a company to write assignments and using artificial intelligence tools like ChatGPT.

Academic Alerts

I have partnered with Student Success and your academic advisors to assist you on your path to success. Throughout the semester, I will communicate with Student Success & academic advisors regarding your progress in the course. If you are contacted, please consider scheduling appointments such as tutoring or academic advising and with me. Referrals are not punitive and are meant to assist you in connecting with resources at UMass. Please email [email protected] if you have any questions or need assistance connecting with resources.

Attendance & Class Preparation

Completing and ensuring that you understand the out-of-class work is extremely important. Please take advantage of the numerous supportive work environment hours that are offered throughout the week. Additionally, attending TBL sessions (ON TIME) is expected and critical to success in this course.

Students who do not attend class cannot expect individual tutoring from teaching assistants or the instructor. If you miss a TBL session without an acceptable University excuse, you are still responsible for the material covered in and outside of class.

TA Project Support Leaders

These TAs will be your team’s project support and will be there to help you with any project related issues and questions. Your TAs are your academic lifeline; please get to know them. And, PLEASE, treat them with respect in person and in emails.

ResEcon 212 Course Outline

1. Variables – Definition, Types, and Levels of Measurement
   • Descriptive vs Inferential Statistics
   • Sample vs Population
   • Random sampling schemes
   • Data classification and levels of measurement
2. Univariate Data Analysis I
   • Grouping discrete variables
   • Grouping continuous variables
   • Visual displays for discrete variables
   • Visual displays for continuous variables
3. Univariate Data Analysis II
   • Measures of central tendency
   • Measures of dispersion
   • Quantiles and five number summaries
4. Bivariate Data Analysis
   • Visuals displays for bivariate data
   • Covariance
   • Correlation coefficient
5. Probability I
   • Random experiments
   • Probability definition
   • Set theory and laws of probability
   • Contingency tables
   • Two-way probability tables
6. Probability II
   • Tree diagrams
   • Tree diagrams and two-way probability tables
   • Bayes’ rule
   • Monte Hall problem
7. Discrete random variables (DRVs)
   • DRVs and discrete probability distributions
   • Probability mass functions and cumulative distribution functions
   • Expected value, variance, and standard deviation
   • Bernoulli experiment and distribution
   • Binomial experiment and distribution
8. Continuous random variables (CRVs)
   • CRVs and continuous probability distributions
   • Probability density functions and cumulative distribution functions
   • Expected value, variance, and standard deviation
   • Uniform distribution
   • Normal distribution
9. Point and interval estimation I
   • Sampling distribution and estimation
   • Central limit theorem for sample means
   • Central limit theorem for sample proportions
   • Point and interval estimation
   • Confidence intervals with known population standard deviation
10. Point and interval estimation II
    • Sampling distribution and estimation
    • T distribution and degrees of freedom
    • Confidence intervals with unknown population standard deviation
    • Sample size determination
11. Hypothesis testing I
    • Motivation and definition
    • Null and alternative hypotheses
    • Types of error and the level of significance
    • Critical values, rejection region, and decision rule
    • Significance vs power
12. Hypothesis testing II
    • Standardized test statistic
    • P-value
    • Testing difference in population means and proportions