Sections covered: All
Learning objectives: Expected knowledge from prior statistical education that will be reinforced in the beginning of the semester
Notes: This material covers topics that are heavily explored in the prereq Stat 51. We will not spend any time in class going over these topics. You may want to review the table of contents and bolded terms and main theorems of these chapters as a refresher.
Sections covered: 4.1 - 4.5
Learning objectives: Expected knowledge from prior statistical education that will be reinforced in the beginning of the semester
Notes: We will spend a little bit of class time reviewing some of the parts of this chapter with a particular emphasis on material that will be useful when we get to Ch 8. The material for this unit will be presented mainly in traditional lecture format but we will start to have regular time in class for group work and problem solving now.
Sections covered: All
Learning objectives: Expected knowledge from prior statistical education that will be reinforced in the beginning of the semester; 2) Work with asymptotic theorems to characterize the behavior of common types of estimators.
Notes: These are very short chapters but they are still important. In class we will emphasize material from Ch 5 on the Law of Large Numbers and Central Limit Theorem but you are expected to be familiar with all of the material in these chapters. We will start to work with a particular analytical tool called a “stakeholder analysis” to delve into these topics.
Sections covered: All
Learning objectives: 1) Understand frequentist and Bayesian methods for parameter estimation and common approaches to evaluate and compare estimators; 2) Work with asymptotic theorems to characterize the behavior of common types of estimators; 3) Understand how to analytically derive a Bayesian posterior distribution and how to interpret a Bayesian credible interval and demonstrate familiarity with different types of priors.
Notes: We are covering this chapter in a different order than the sections are presented in your textbook so please follow the course calendar. We will continue to use a stakeholder analysis to help reach the learning objectives.
Sections covered: 9.1 - 9.5, 9.10
Learning objectives: 4) Understand the important role of likelihood functions for hypothesis testing in both Bayesian and frequentist frameworks; 5) Understand the relationship between frequentist confidence interval estimation and hypothesis testing; 6) Familiarity with common types of optimal testing strategies; 7) Construct and interpret frequentist p-values for hypothesis tests and interpret the error rates.
Notes: We are going to spend at least a third of the semester on this chapter alone. Even though we won’t cover every single section of the chapter, the material here is deep and is very important for any statistical practitioner to understand. Here you will start to understand what it means to be a steward of statistics and why that is important.
Sections covered: 11.1 - 11.2, 11.4 - 11.5
Learning objectives: 8) Identify/define model parameters and state statistical inferential questions in terms of these parameters for various common, realistic study settings; 9) Contextualize statistical methods and theory in science and policy at large and develop a habit of mind informed by the stewardly application of such methods.
Notes: This is the first chapter where we get into more specific (but generally applicable) statistical tests and methods. We will introduce the American Statistical Association’s Guidelines for Ethical Practice here as we cover ways to compare two samples.
Sections covered: All
Learning objectives: 8) Identify/define model parameters and state statistical inferential questions in terms of these parameters for various common, realistic study settings; 9) Contextualize statistical methods and theory in science and policy at large and develop a habit of mind informed by the stewardly application of such methods.
Notes: We will continue to reference the ASA’s ethical guidelines with respect to ANOVA methods. At this point in the semester, there will be less material covered in lecture-format and more time in class working through problems together.
Sections covered: 13.1 - 13.4
Learning objectives: 8) Identify/define model parameters and state statistical inferential questions in terms of these parameters for various common, realistic study settings; 9) Contextualize statistical methods and theory in science and policy at large and develop a habit of mind informed by the stewardly application of such methods.
Notes: We will continue to reference the ASA’s ethical guidelines with respect to the analysis of categorical data. Most class time will be collaborative problem solving rather than lectures.
Sections covered: 14.1 - 14.4; 14.8
Learning objectives: 8) Identify/define model parameters and state statistical inferential questions in terms of these parameters for various common, realistic study settings; 9) Contextualize statistical methods and theory in science and policy at large and develop a habit of mind informed by the stewardly application of such methods.
Notes: We will continue to reference the ASA’s ethical guidelines with respect to linear regression. Most class time will be collaborative problem solving rather than lectures.