Statistics 5301 is a first course in a two-semester non-calculus sequence in data analysis covering descriptive statistics, design of experiments, probability, statistical inference, one sample t, goodness of fit, the two-sample problem, and one-way ANOVA.

Prerequisites: The sequence is intended for students with “limited” formal mathematics background (a solid grounding in high school algebra is beneficial). However, in terms of data analysis and interpretation, the conceptual level of the course is high. While many of the students in the course are graduate students (it is a required course in many programs), it is certainly an appropriate sequence for junior and senior level undergraduates.

By the end of this course, students should successfully be able to:

• deal with problems of data-gathering, presentation, and interpretation
• create graphical and numerical summaries of data
• develop an understanding of problems of measurement
• gain an understanding of the impact of statistical ideas in daily life and specific areas of study
• recognize the uses and misuses of statistics and related quantitative arguments
• understand fundamental concepts of probability and statistics
• utilize the use of computer programs in problems involving data analysis
• summarize data using summary measures and graphical techniques
• identify an appropriate analysis for data collected in a study
• carry out such an analysis
• examine whether the assumptions behind the analysis are reasonable
• recognize the strengths or weaknesses of the study based on how the data was collected
• understand how the design of a study affects the conclusions that can be made
• write and discuss what conclusions can be drawn from statistical analyses.

This course satisfies the General Education foundation requirement in Mathematical and Quantitative Reasoning or Data Analysis which has the following goals and expected learning outcomes:

Goals: Successful students will be able to apply quantitative or logical reasoning and/or mathematical/ statistical methods to understand and solve problems and will be able to communicate their results. 

Expected Learning Outcomes (ELOs): Successful students are able to: 

1. Use logical, mathematical and/or statistical concepts and methods to represent real world situations.
2. Use diverse logical, mathematical and/or statistical approaches, technologies and tools to communicate about data symbolically, visually, numerically and verbally.
3. Draw appropriate inferences from data based on quantitative analysis and/or logical reasoning.
4. Make and evaluate important assumptions in estimation, modeling, logical argumentation and/or data analysis.
5. Evaluate social and ethical implications in mathematical and quantitative reasoning.

The course objectives address the GE learning outcomes as follows:

Students in Statistics 5301 are expected to be able to identify an appropriate analysis for data collected in a study, carry out such an analysis, examine whether the assumptions behind the analysis are reasonable, and recognize the strengths or weaknesses of the study based on how the data were collected. Doing so requires understanding basic concepts in statistics and probability; the ability to create graphical and numerical summaries of data; understanding how the design of a study affects the conclusions that can be made; and the ability to carry out basic statistical analyses (by hand or using statistical software). Students will conduct analyses of data, including a discussion (in plain English) of what conclusions can be drawn. The goal of statistics is not calculation but gaining understanding from numbers. This means that the correct numerical answer will only receive partial credit. The remainder of the credit will be available for choosing the best method of solution and explaining why the method is appropriate. You will also need to interpret your answers in the light of the practical problem.

This course also satisfies the Legacy General Education requirement in Data Analysis which has the following goals and expected learning outcomes:

Goals: Students develop skills in drawing conclusions and critically evaluating results based on data.

Expected Learning Outcomes (ELOs):

1. Students understand basic concepts of statistics and probability.
2. Students comprehend methods needed to analyze and critically evaluate statistical arguments.
3. Students recognize the importance of statistical ideas

Required Textbook: [SS] The Statistical Sleuth: A Course in Methods of Data Analysis by Ramsey F. and Schafer D. (2012), 3rd Edition, Cengage Learning, ISBN-13:978-1-133-49067-8

The textbook for this course is being provided via CarmenBooks. Through CarmenBooks, students obtain publisher materials electronically through Carmen/Canvas, saving them up to

80% per title. The fee for this material is included as part of tuition and is listed as CarmenBooks fee on your Statement of Account. In addition to cost-savings, materials provided through CarmenBooks are available immediately on or before the first day of class.  There is no need to wait for financial aid or scholarship money to purchase your textbook.

Unless you choose to opt out of the program, you do NOT need to purchase any materials for this course at the bookstore. For more information on the program or information on how to opt out, please visit the CarmenBooks website.

Access this eBook through the CARMENBOOKS reader link in the course navigation of your Carmen course for this class.

Note: The Statistical Sleuth is also required for Stat 5302.

Supplemental Textbook: There is no required textbook for the first half of the course. You may find the following book useful, but it is optional. I do provide readings from this optional text.

[IPS] Introduction to the Practice of Statistics (5th Edition onwards) by D.S. Moore and G.P. McCabe

• This class requires you to use the statistical software package called R (The R Project for Statistical Computing) to illustrate certain aspects. Here is the information for obtaining R.
  • You can download R for Windows, Mac, and Linux, from the CRAN archive.
  • An in-depth introduction to R is available.
  • Hands-on tutorials are available in the Swirl system. In particular, “R Programming: The basics of programming in R” is an appropriate first tutorial for students who have never used R.
• An easier-to-use interface to R is available in the software package RStudio. This package is available for Windows, Mac, and Linux and can be downloaded for free. Note that RStudio requires R to be installed.
• It may be helpful to become familiar with the (free) R Markdown authoring framework as you take this class; its use is required in future courses in this sequence. An online guide with overview information is available.
• Microsoft Office 365 ProPlus: All Ohio State students are now eligible for free Microsoft Office 365 ProPlus through Microsoft’s Student Advantage program. Each student can install Office on five PCs or Macs, five tablets (Windows, iPad® and Android™) and five phones.
  • Students are able to access Word, Excel, PowerPoint, Outlook and other programs, depending on platform. Users will also receive 1 TB of OneDrive for Business storage.
  • Office 365 is installed within your BuckeyeMail account. Full instructions for downloading and installation are available.
• Course Website: Important announcements, course materials, homework problems, computing references, and other information about the class are posted on Carmen.

The course will consist of in-class lectures. You are expected to attend in-person lectures. In addition, there will be homework assignments done outside of class. Lecture material and homework assignments will be made available on Carmen. You will be responsible for studying the material that is assigned and reviewed in lecture. Sufficient time will be allotted to complete homework assignments. R statistical software will be used to complete aspects of the assignments. Knowledge of course content and its application will be assessed via two midterms and a final exam. Your knowledge of R will not be assessed on exams. However, you will be expected to be familiar with and know how to interpret R output reviewed in class and in lecture notes. The instructor will hold weekly office hours via according to the schedule provided.

Assignments

Homework and Quizzes: 25%

Midterm 1: 25%

Midterm 2: 25%

Final Exam: 25%

Late Assignments

Generally late assignments are not accepted. If you are unable to complete an assignment on time, please get in touch with me as soon as possible so we can discuss your situation.

Grading Scale

93–100: A

90–92.9: A-

87–89.9: B+

83–86.9: B

80–82.9: B-

77–79.9: C+

73–76.9: C

70 –72.9: C-

67 –69.9: D+

60 –66.9: D

Below 60: E