Foundations in Data Science
Modules
01 — Intro to the Tools
02 — No Code Intro to Describing Data
03 — Data Visualization
04 — Data Wrangling
05 — Basic Rules of Probability
06 — Probability Distributions
07 — Sampling and Descriptive Statistics
08 — Quantifying Uncertainty in Descriptive Statistics
09 — Confidence Intervals for Descriptive Statistics
10 — Simple Linear Regression
11 — Multiple Linear Regression
12 — Quantifying Uncertainty in Regression Models
13 — Categorical Predictors
14 — Moderation
15 — Non-linear effects
16 — Null Hypothesis Significance Testing
17 — Checking Assumptions and Remediation
18 — Causal Inference
Apply and Practice Activities
Crude and Adjusted Statistics
Wealth Inequality
American Dream
ARCOS Buyers
ARCOS Compare States
ARCOS Maps
ARCOS gt Table
Pivoting the American Dream
Personal Pew Project Tutorial
NSDUH Cross Tabulations
NSDUH Confidence Intervals
Gatsby Curve SLR
Gatsby Curve MLR
Intro to Model Building
Blair Replication Set Up
Blair Replication Explore Logarithms
Blair Replication Explore Polynomials
Blair Replication Fit Models
Blair Replication Quarto APA Manuscript
The Come Back Kids
Model Building with MIDUS
Lectures
Week 1 — August 19, 2024
Week 2 — August 26, 2024
Week 3 — September 2, 2024
Week 4 — September 9, 2024
Week 5 — September 16, 2024
Week 6 — September 23, 2024
Week 7 — September 30, 2024
Week 8 — October 7, 2024
Week 9 — October 14, 2024
Week 10 — October 21, 2024
Week 11 — October 28, 2024
Week 12 — November 4, 2024
Posit Cloud
On this page
Quantifying Uncertainty in Regression Models Estimates
Warm-up Activity
Slide deck
Explore sample size in regression models
Practice estimating uncertainty for regression models (parametric/theory-based approach)
Lecture Materials for Week 9
October 14, 2024
Quantifying Uncertainty in Regression Models Estimates
Warm-up Activity
Please click here to begin the activity
Slide deck
View slides in full screen
Explore sample size in regression models
Please click here to begin the activity
Practice estimating uncertainty for regression models (parametric/theory-based approach)
Please click here to begin the activity