Week One: Foundations of Statistics
Welcome!
In this coursework, you’ll get a refresher on the foundational components of statistics and data, investigate how statistics is used in your major, and think critically about the philosophy of statistical inference.
0.1 Learning Outcomes
By the end of this coursework you should be able to:
describe observations, variables, and data matrices
explain the different types of variables a study can have
illustrate the difference between explanatory and response variables
delineate the difference between a population and a sample
compare and contrast different sampling methods
outline what is necessary to make an experiment an “experiment”
characterize the differences between observational studies and experiments
1 Prepare
Let’s start off by reading a refresher on some of the foundational concepts for data!
1.1 Textbook Reading
📖 Required Reading: Introduction to Modern Statistics – Hello Data
📖 Required Reading: Introduction to Modern Statistics – Study Design
Reading Guide – Due Wednesday by noon
Note: There is one combined reading guide for both chapters.
Submit your completed reading guide to the Canvas assignment portal!
1.2 Concept Quiz – Due Wednesday by the start of class
1. What are the different types of variables that can appear in a dataset? Select all that apply!
- discrete numerical
- ordinal categorical
- nominal categorical
- continuous numerical
- discrete categorical
2. Just because a variable has numeric values, does not mean it is a numeric variable. Which of the following variables appear numerical but behave like a categorical variable? Select all that apply!
- zip code
- GPA
- height
- year in school
3. Which of the following statements are true about observational studies and experiments? Select all that apply!
- Experiments randomly assign the explanatory variable
- Observational studies randomly assign the explanatory variable
- Observational studies can make causal statements about the relationship between the explanatory and response variables
- Experiments can make causal statements about the relationship between the explanatory and response variables
4. What are different methods for sampling from a population? Select all that apply!
- simple random sampling
- stratified random sampling
- cluster sampling
- multistage sampling
- convenience sampling
5. Cluster sampling and stratified sampling both rely on grouping observations, but have important differences. Match each method to how observations are randomly sampled.
stratified sampling
cluster sampling
groups of observations are created, groups are randomly selected, every observation in the selected group is sampled
groups of observations are created, observations within a group are randomly sampled
2 R Tutorial – Due Wednesday by the start of class
💻 Required Tutorial: Language of Data
Submit a screenshot of the completion page to the Canvas assignment portal!
3 Statistics in Your Field
Now let’s investigate how statistics is used in your discipline.
Find a peer-reviewed article from a journal in your field that performs a statistical analysis somewhere in the paper. You do not need to be familiar with the analysis they do. That being said, if you want to find a paper that uses a statistical method we’ll cover in this class, look for a paper that uses “linear regression” or “ANOVA.”
The Library is a great resource for this! The following website (https://csu-calpoly.primo.exlibrisgroup.com/discovery/jsearch?vid=01CALS_PSU:01CALS_PSU) has a list of journals by discipline that are available through the library.
If you are having a hard time finding a peer reviewed journal article, here is a video of me talking through how I found one: https://www.youtube.com/watch?v=ui8ibJ0Pkhs
3.1 Statistical Critique Step 0 – Due Monday, April 8 by 5pm
Upload a PDF of your journal article to the Canvas assignment portal.