Weekly Details

Click each box to see the details for that week. Ignore the indentation level.

⚠️ Details are subject to change. Last Updated: Wed May 01 11:46:54 AM

Week 1: Introduction to the class

Learning Objectives

• Understand why faculty are striking.
  • Understand how they currently learn, and learn at least one new technique for learning.

How to prepare

• Familiarize yourself with our Canvas class and this website.
  • Review the syllabus
  • Make an account on Posit Cloud using your campus email.

Monday overview

• Strike day. No class

Wednesday overview

• Welcome and introductions
  • Expectations and overview of HW 1

Friday overview

• Motivating Example
  • Start your LJ. 321 bridge (analyzing data)

Week 2: Introduction to data, populations and sampling

Learning Objectives

• Describe how they can ensure their success in this class
  • Use R studio to create a reproducible analysis document.
  • Explain how Statistics is used in the service of science
  • Distinguish between the population and a sample
  • Distinguish between categorical and continuous data types Formulate a testable research hypothesis

How to prepare

• Finish HW 01
  • Watch PDS 2 (details on module page)
  • (Watch Optional) Developing a research topic that interests you
  • Identify a research area and a partner to work with.
  • Schedule a time outside of class to work with your analysis partner on a weekly basis.

Monday overview

• Research Project Overview (choose data)
  • Asking answerable questions (1.1)

Wednesday overview

• Group Quiz 1 (logistics)
  • Structure of Data (1.2)
  • Types of data (1.2.2)

Friday overview

• Populations, sampling (1.3)
  • Questions on HW02?

Week 3: Study Design and Importing data into R

Learning Objectives

• Differentiate between primary and secondary research sources
  • Properly cite relevant research
  • Identify the difference between an observational study and an experiment
  • Explain why causal conclusions can’t be made from an observational study

How to prepare

• Watch PDS Video 2 and 3
  • Read over HW 02 and the project requirements

Monday overview

• Group quiz 2 (Structure of data, population, samples)
  • Study Design (1.4)

Wednesday overview

• Preparing data for analysis (1.5) - Data import
  • Get your data into R (in class activity)

Friday overview

• Finish 1.5. Demo recoding data in Posit cloud. Setting preferences for your sanity

Week 4: Basic data management.

Learning Objectives

• Import data into R using code
  • Identify data types in R
  • Conduct a peer review in Google Drive
  • Explain why data preparation takes the majority of your time, but is crucially important
  • Identify mistakes and missing data in data using tables and summaries
  • Perform basic data management tasks such as creating new variables, renaming and recoding existing variables

How to prepare

• Watch PDS Video 4 and 5. Note these videos use R scripts instead of a quarto file, so they won’t show code chunks.

Monday overview

• Group quiz 3 (Study Design & Formulating RQ’s)
  • Intro to peer reviews
  • Start HW03. Live discussion on data management tactics

Wednesday overview

• open work day to finish hw03

Friday overview

• Summary statistics for quantitative data (2.1.1)

Week 5: Visualizing and describing distributions of a single variable

Learning Objectives

• Learn how to create univariate data graphics and summary statistics in R
  • Describe the distribution of a single variable in sentence form using summary statistics and pointing out specific features of the graphics as evidence to support your interpretation

How to prepare

• Watch PDS Video 6.
  • Read the instructions for HW04 and Project stage 2.

Monday overview

• Project Stage 2 (Intro and setup)
  • Overview of HW 04
  • Plots for quantitative data (2.1.2)

Wednesday overview

• Discuss how to summarize categorical data using summary statistics, plots and words. (2.2)

Friday overview

• Open work day to finish hw04

Week 6: Visualizing and describing relationships between two variables

Learning Objectives

• Create appropriate plots, grouped summary statistics to assess the relationship between two variables.
  • Describe the relationship between two variables in plain English

How to prepare

• Watch PDS video 7
  • Review feedback on your Hw04 so you don’t make the same mistakes again.

Monday overview

• Group quiz 4 (Describing Distributions)
  • Discuss methods to visualize associations between two categorical variables. (2.3.1)

Wednesday overview

• Assess and describe the association between a continuous variable and a categorical variable. (2.3.2)

Friday overview

• Assess and describe the association between two continuous variables (2.3.3)

Week 7: Foundations for Statistical Inference

Learning Objectives

• Describe the difference between exploratory and inferential statistics
  • Write a null, and competing (alternative) hypothesis statements
  • Explain how a statistic can vary under repeated sampling
  • Define a sampling distribution

How to prepare

• NA

Monday overview

• Group quiz 5 (Describing Relationships)
  • Learning journal work: Creating learning bridges. Reflect and update on your prior bridge entry.
  • Introduce Project Stage 3 (EDA)

Wednesday overview

• Motivating Example (3.1)

Friday overview

• Sampling Distributions (3.2)
  • Probability Distributions (3.3)

Week 8: Midterm, Probability and Research

Learning Objectives

• NA

How to prepare

• NA

Monday overview

• Midterm

Wednesday overview

• Normal Distribution (3.4)

Friday overview

• Group quiz 6 (Sampling Distributions and Probability)
  • Open work on project

Week 9: Confidence Intervals & Hypothesis Testing

Learning Objectives

• Calculate a point estimate on a data set
  • Describe the behavior of a point estimate as sample size increases
  • Construct and interpret a confidence interval
  • Calculate and explain the Margin of Error
  • Explain what can make the width of a confidence interval larger or smaller

How to prepare

• Read the course notes sections in order, before the day we discuss that topic.
  • Read the instructions for the Bivariate inference assignment
  • Make sure you are fully caught up on everything!

Monday overview

• Assumptions for Inference (3.5)
  • CLT (3.6)
  • Interval Estimation (3.7)

Wednesday overview

• Hypothesis Testing (4)

Friday overview

• Using CI to test hypothesis (4.1.1)
  • Decision errors (4.2)

Week 10: Comparing Means (T-test, ANOVA)

Learning Objectives

• Formally test the difference between two or more means.

How to prepare

• Read the instructions for the next assignment. Download the template and upload to your Posit cloud. Identify what variables you want to analyze and be sure they are clean and ready for analysis.

Monday overview

• Cesar Chavez Day - Campus closed

Wednesday overview

• Group quiz 7 (Inference and Intervals)
  • Model Framework (5.1)
  • Bivariate Inference - 2 sample T-tests (5.2)

Friday overview

• Bivariate Inference - ANOVA (5.3)

Week 11: Multiple comparisons, Chi-Squared test of equal proportions

Learning Objectives

• Formally test the difference between two or more proportions.
  • Formally test for a significant correlation between two numeric variables.

How to prepare

• Homework 06 - Complete the first analysis, and the setup for the other two.
  • Identify which of these analyses types you will be needing for your research question.
  • Fill out slide 9 in your research project slides.

Monday overview

• Bivariate Inference - ANOVA cont. (5.3)

Wednesday overview

• Bivariate Inference - Chi-Square (5.4)

Friday overview

• Group quiz 8 (Bivariate Modeling)
  • Work on Project Stage 4

Week 12: Linear Regression

Learning Objectives

• Fit a simple linear regression model on two quantitative measures.
  • Interpret both beta coefficients in context of the problem
  • Assess model fit using residual plots

How to prepare

• Have HW6 completely finished, HW7 template loaded into Posit Cloud, and watch the PDS videos for correlation and into to regression PDS 14: Multivariable Modeling and Confounding (19-37 minutes video before Monday.

Monday overview

• Linear regression modeling. Fit and interpretation (6.1)
  • [Updated 4/14 - Dr. D has no voice. So no lecture day. Work on this section on your own and ask questions as needed}

Wednesday overview

• R2 (6.2), outliers (6.3), predictions (6.4)

Friday overview

• LinReg Assumptions (6.5), Inference (6.6)

Week 13: Multiple Linear Regression

Learning Objectives

• Fit a multiple linear regression model on more than one predictor.
  • Interpret all beta coefficients in context of the problem
  • Explain the concept of confounding

How to prepare

• Watched up to minute 29 of PDS 14.

Monday overview

• Group quiz 9 (Regression)
  • How to write multiple regression models mathematically (7.1)
  • Model fitting (7.2)

Wednesday overview

• Interpreting continuous and binary regression coefficients (7.2.1-7.2.3)

Friday overview

• Interpreting categorical regression coefficients (7.2.4)

Week 14: Model Assessment, model building

Learning Objectives

• Build several competing models and use various measures of model fit to compare between models
  • Identify when a model has structural problems

How to prepare

• Finish PDS Chapter 14.

Monday overview

• Special Topic: Logistic Regression

Wednesday overview

• Group Quiz 10 (Multiple Regression)
  • Model assessment (7.3), building (7.5)

Friday overview

• open work day
  • Project stage 5