STAT 501online – Methods of Applied Statistics
FALL 2023
Professor: Joanna Jeneralczuk
Email: jjeneral@umass.edu
Website: Moodle
Office hours (in person) -TBA
Zoom office hours – TBA
TAs office hours – TBA
R session – TBA
About the course:
• Course is online and asynchronous, synchronous lectures on TuTh, 11:30-12:45 are available.
• Lectures will be recorded for viewing at other times, but you can also attend the lectures synchronously through Zoom. You can attend the regular class meetings.
• All resources for the regular in-person Stat 501 section will be available for you. This include lectures on Zoom, office hours, R sessions, and reviews. I will also host a weekly meeting/office hours (optional) for the online section only. In addition to the recorded lectures, there will also be some short videos covering course material.
• There will be weekly homework assignments, submitted on Gradescope.
Textbook: Introduction to Probability and Statistics,
by Mendenhall, Beaver and Beaver, 14 th edition, Publishers: Brooks/Cole (pdf file posted on Moodle)
We will be occasionally using: Introductory Statistics with Randomization and Simulation”
http://www.openintro.org/stat/textbook.php. The book is available as a free download.
In a college course, the textbook is not just a reference to use after the instructor has presented new material but a source book to use at every stage of learning.
Course Description: An applied statistics course (three credits) for graduate students and upper-level undergraduates with no previous background in statistics who will need statistics in their further studies and their work. The focus is on understanding and using statistical methods in research and applications. Topics include descriptive statistics, probability theory, random variables, random sampling, estimation and hypothesis testing, basic concepts in the design of experimentsand analysis of variance, linear regression, contingency tables. The course has a large data-analytic component using R.
Prerequisites: high school algebra; junior standing or higher.
Computer software: In this course we will use a statistical software package R (RMarkdown).
The use of the R statistical environment with the RStudio interface (downloadable from http://rstudio.org) will be integrated into the course. RStudio is free software that can be installed using the version of R on your own machine (download information for R can be found at http://r-project.org). I will provide instructions for R.
Calculator: A calculator will be useful during class and while working through some homework problems and during exams. A graphing calculator is not necessary.
Moodle: The Moodle site will be regularly updated with lecture handouts, videos, assignments, readings and other course resources. I will create discussion forums for questions that arise in and out of class – we will use Piazza.
Grading: Final averages will be weighted as follows:
Homework – 25%
Homework with R – 15% (working in groups is OK)
Midterm Exam – 20%
Final Exam – 25%
Project -10%
Participation -5%
Grades will be assigned according to the following scale:
A 93-100; A-: 88-92; B+: 83-87; B: 80-82; B-: 75-79
C+: 70-74; C: 65-69; C-: 60-64; D+: 55-59; D: 51-54; F below 51.
Exams: There are two exams, given online : midterm exam and a final exam (dates TBA). There are no scheduled make-ups for exams. Make-ups without penalty are only offered to students with legitimate conflicts or emergencies that can be documented.
Homework– assigned weekly (submitted on Gradescope) must be written neatly and turned in on time. Unreadable words or figures are considered to be incorrect answers. In writing up homework, it is not sufficient to give only the answer to a problem; you must show how it was calculated (it is not necessary to show detailed calculations, just enough to show that you know what you are doing.). You are encouraged to discuss homework problems with other students, but you must write up solutions individually. No late homework will be accepted. Extensions may be possible but need to be requested before the deadline, please email me and cc Harsh Dubey(TA). There is also Homework with R. We are going to use RMarkdown. It is ok to do this part of homework in groups. The lowest two regular homework grades will be dropped.
Readings: In addition to homework, there are reading assignments that correspond to the topics to be covered in the following class. If you are going to participate in lectures on Zoom , do the assigned readings prior to coming to class, or watch the recorded videos. We will spend much of the class time doing examples and tackling problems together, applying the concepts you have learned.
Group Project:
A group project will be introduced soon. More details will be provided at that time. The project is designed to give you a better understanding of the statistical processes you learn about in class. Ideally, you’ll be working in groups of 2-5.You can form groups with the students from the regular Stat 501.
Disability statement: The University of Massachusetts Amherst is committed to making reasonable, effective and appropriate accommodations to meet the needs of students with disabilities and help create a barrier-free campus. If you have a disability and require accommodations, please register with Disability Services (161 Whitmore Administration building; phone 413-545-0892) to have an accommodation letter sent to your faculty. Information on services and materials for registering are also available on their website www.umass.edu/disability.
Student Responsibilities: Students are responsible for being aware of all announcements that are made, such as changes in exam dates, due dates of homework etc.
Students must check their UMASS email account regularly for information from the instructor. Failure to do so may result in missed deadlines or other consequences that might adversely affect students. Note that you can forward this email account to any other account of your choosing.
Academic honesty statement: 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/.
Drops, Withdrawals: The last day to drop with no record is Monday, Sept 11(undergrad) or Monday, Sept 18 (graduate students).
The last day to drop with a W or to request a Pass/Fail grading option is Monday, Oct 31
INC are a last resort offered in extreme situations and are only offered to students who are passing the course with a grade of C or higher.
List of Topics:.
- Exploratory Data Analysis (Chapters 1, 2): Frequency distribution of a variable defined on a population, empirical distributions, dot plots, stem and leaf plots, histograms, quantiles (including sample median, upper and lower quartiles), interquartile range, box plots, sample mean and variance, Tchebysheff’s Theorem and the Empirical Rule.
- Bivariate Data Analysis(Chapter 3): Graphical methods: side by side boxplots, bar charts and pie charts, scatter plots, fitting a straight line to a bivariate data set (least squares), correlation coefficient, Chi-square test.
- Probability theory (Chapters 4): sample space, events and their probabilities, independence, conditional probability and the multiplication rule, Bayes rule.
- Random sampling, random variables and their distributions, expected value and variance of a random variable (discrete or continuous), the binomial distribution, hypergeometric, Poisson, uniform and normal distribution (Chapter 5and 6)
- Sampling Distribution theory (Chapter 7): Simple random sample, Central Limit Theorem, sampling distribution of the mean and proportion.
- Estimation and hypothesis testing of means and proportions (Chapters 8, 9, 10):Point estimation, interval estimation, applications to hypothesis testing. Difference between two populations means and proportions
- Statistical Modeling (Chapters 3 and 11+): Design of experiments, analysis of variance, two- way contingency tables, linear regression.
The course fulfills the R2 general education requirement that addresses analytical reasoning. Upon completion of this course, you should be able to think critically about data, present graphical and numerical summaries of the data, understand basic probability models, and apply appropriate statistical inference procedures. Students should also become critical consumers of statistically based results reported in popular media and being able to report the results for inclusion in their thesis or papers. The course also fulfills the Basic Math Skills requirement (R1). Note that Stat 501 cannot be taken pass/fail to fulfill the R2 requirement.
Course Objectives:
GenEd Learning Outcomes
1) Content:
This course covers the mathematics techniques of descriptive statistics (summarizing, graphing and presenting data), basics of probability, random variables, random sampling, estimation and hypothesis testing, basic concepts in the design of experiments and analysis of variance, linear regression, contingency tables
2) Critical Thinking:
(i) The critical and analytical thinking skills will be beneficial and essential for students who are taking statistics to interpret and draw inferences about a population by evaluating statistical data sets and present and communicate with clarity, accuracy, and precision of the implications and consequences of their findings. It is a requisite to have critical thinking skills to select the right statistical analysis or the right statistical model for a particular problem. Then, students must have the same analytical and critical skills for interpreting, inferring, and evaluating the implication and consequences of the results.
(ii) The knowledge of basic statistical concepts such as probability distribution, random variables and their distributions
random sampling, estimation and hypothesis testing, basic concepts in the design of experiments and analysis of variance, linear regression, contingency tables.
3) Communication:
Students will learn how to present the conclusions of statistical studies to address the stated problem with clarity, accuracy and precision.
4) Application
(i) Students will develop the capacity to apply disciplinary perspectives and methods of analysis to real world problems, the ability to connect statistics and real-life situations. The course uses real datasets from a variety of disciplines, and we use Minitab for data analysis.
(ii) Appreciate and understand the role of statistics in your own field of study.
R1 and R2 Learning Outcomes
Stat 240 requires basic math skills as a prerequisite, and uses those skills throughout the semester, and thus satisfies the Basic Math Skills requirement (R1). The course also satisfies the following objectives of the Analytic Reasoning GenEd requirement (R2):
1) Advances a student’s formal or mathematical reasoning skills beyond the level of basic competence:
Students will learn how to collect, organize, summarize, and present numerical and categorical data. Students will also learn probability theory to understand the mathematics of estimation, hypothesis testing, Anova
Linear regression and chi-square test.
2) Increase the student’s sophistication as a consumer of numerical information:
The course is designed to provide students with the mathematical tools, vocabulary, and analytical skills that will allow them to understand results of statistical studies as they are presented in the mainstream media.