Welcome to Class!

Welcome to the Spring 2025 semester with Professor Baker. This blog post serves as your central hub for the first day of class and beyond. Here, you will find everything you need to get started, from administrative essentials to your very first set of lecture notes.

Essential Class Resources

Please ensure you review the following documents and links to prepare for a successful semester:

  • Class Syllabus (2pm Class): Your roadmap for the course, outlining policies, grading, and the schedule.
  • Hawkes Learning Link: Access your homework and tests here.
  • Class Notes: Download the PDF files to follow along with lectures.
  • Video Lectures & Review Tests: Bookmark this page for future study sessions.

Chapter 1: Introduction to Statistics and Problem Solving

To get our minds geared up for the semester, let’s look at the foundational concepts found in the Chapter 1 notes attached to this post. Statistics is not just about crunching numbers; it is about the meaning of data and how we interpret it.

1. Population vs. Sample

In statistics, we constantly move between two groups. Understanding the difference is crucial for the entire course:

  • Population ($N$): This is the total set of subjects or things we are interested in studying. For example, if we are studying voters, the population might be all registered voters in the country.
  • Sample ($n$): This is a subset of the population used to gain insight. Because studying the entire population is often impossible or too expensive, we select a sample size $n$ where $n < N$.

When we list all members of a population, this is referred to as a frame. If we survey every single element in that frame, we are conducting a census.

2. Parameters vs. Statistics

A helpful trick to remember these definitions is looking at the first letters:

  • Population Parameters: These are facts or numerical descriptions about the Population.
  • Sample Statistics: These are facts or numerical descriptions about the Sample.

3. Two Main Branches of Statistics

As we progress through the semester, we will engage in two distinct types of statistical analysis:

  1. Descriptive Statistics: This involves the collection, organization, analysis, and presentation of data. It describes what is.
  2. Inferential Statistics: The objective here is to make reasonable guesses or predictions about population characteristics using sample data.

4. Critical Thinking with Data

Finally, as we dive into "The Data Explosion," it is vital to be statistically literate. When presented with a study or a graph, you should always ask critical questions, such as:

  • Where did the data come from?
  • How was it sampled, and was the sample size large enough?
  • Are the reported statistics appropriate for this kind of data?
  • Do the claims make sense?

I look forward to exploring these concepts with you. Let's make this a great semester!