STA 101 Data Analysis and Statistical Inference
Duke University Fall 2026
Below is a prospective outline for the course. Due dates are firm, but topics may change with advanced notice.
| WEEK | DATE | TOPIC | MATERIALS | READING | MORE | DUE |
|---|---|---|---|---|---|---|
| 1 | Tue, Aug 25 | 🧑🏫 Welcome! | 🛝 slides 00 | |||
| Thu, Aug 27 | 🧑🏫 The bottom line, at the top | 🛝 slides 01 🎶 notes 01 |
📗 r4ds - intro 📘 ims - ch 1 📘 ims - ch 2 📘 ims - ch 3 |
🎥 Meet the toolkit :: R and RStudio 🎥 Meet the toolkit :: Quarto 🏋️ Tutorial: language of data 🏋️ Tutorial: types of studies 🏋️ Tutorial: experimental design |
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| Fri, Aug 28 | 💻 Lab 0 | 🔬 lab 00 | Intro survey @ 5PM | |||
| 2 | Tue, Sep 1 | 🧑🏫 Exploratory data analysis | 🛝 slides 02 🎶 notes 02 ⛴️ ARC info |
📗 r4ds - ch 1 📗 r4ds - ch 3 📘 ims - ch 5 |
🎥 Visualizing data 🎥 Building a plot with ggplot2 🎥 Grammar of graphics 🎥 Grammar of data transformation 🎥 Exploring numerical data 🏋️ Tutorial: viz for numerical data 🏋️ Tutorial: summarizing data |
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| Thu, Sep 3 | 🧑🏫 Exploratory data analysis | 📘 ims - ch 4 | 🎥 Exploring categorical data 🎥 Exploring relationships 🏋️ Tutorial: viz for categorical data |
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| Fri, Sep 4 | 💻 Lab 1 | 🔬 lab 01 | Lab 1 @ end-of-lab | |||
| 3 | Tue, Sep 8 | 🧑🏫 Exploratory data analysis | 🛝 slides 03 🎶 notes 03 |
📘 ims - ch 6 | 🏋️ Tutorial: case study 🏋️ Tutorial: visualizing two variables |
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| Thu, Sep 10 | 🧑🏫 Modeling data | 🛝 slides 04 🎶 notes 04 🧊 ae 03 🫵 practice! |
📘 ims - ch 7.1 | 🎥 The language of models 🏋️ Tutorial: correlation |
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| Fri, Sep 11 | 💻 Lab 2 | 🔬 lab 02 |
HW 1 @ 10 AM Lab 2 @ end-of-lab |
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| 4 | Tue, Sep 15 | 🧑🏫 Simple linear regression | 🛝 slides 05 🎶 notes 05 📈 play! |
📘 ims - ch 7.2 | 🎥 Simple linear regression 🏋️ Tutorial: SLR 🏋️ Tutorial: interpretation 🏋️ Tutorial: model fit |
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| Thu, Sep 17 | 🧑🏫 Multiple linear regression | 🛝 slides 06 🎶 notes 06 🐧 ae 04 |
📘 ims - ch 8.1 - 8.2 | 🎥 Linear regression with a categorical predictor 🎥 Linear regression with multiple predictors 🎥 Main and interaction effects 🏋️ Tutorial: additive models 🏋️ Tutorial: extensions 🏋️ Tutorial: multiple linear regression |
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| Fri, Sep 18 | 💻 Lab 3 | 🔬 lab 03 |
HW 2 @ 10 AM Lab 3 @ end-of-lab |
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| 5 | Tue, Sep 22 | 🧑🏫 \(R^2\) | 🛝 slides 07 🎶 notes 07 |
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| Thu, Sep 24 | 🧑🏫 Model selection | 🛝 slides 08 🎶 notes 08 💎 ae 05 |
📘 ims - ch 8.3 - 8.4 | |||
| Fri, Sep 25 | 💻 Lab 4 | 🔬 lab 04 |
HW 3 @ 10 AM Lab 4 @ end-of-lab |
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| 6 | Tue, Sep 29 | 🧑🏫 Logistic regression | 🛝 slides 09 🎶 notes 09 |
📘 ims - ch 9 | 🎥 Logistic regression 🏋️ Tutorial: logistic regression |
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| Thu, Oct 1 | 🧑🏫 Logistic regression | 🛝 slides 10 🎶 notes 10 ✉️ ae 06 |
🎥 Classification and decision errors 🎥 Overfitting and spending your data |
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| Fri, Oct 2 | 💻 Lab 5 | 🔬 lab 05 | Lab 5 @ end-of-lab | |||
| 7 | Tue, Oct 6 | 🧑🏫 Modeling wrap-up | 🛝 slides 11 🎶 notes 11 🌲 ae 07 |
📘 ims - ch 10 | 🏋️ Tutorial: case study | HW 4 @ 3 PM |
| Thu, Oct 8 | 📝 Midterm | 💫 guide | ||||
| Fri, Oct 9 | ❌ No Lab | |||||
| 8 | Tue, Oct 13 | ❌ Fall Break - No Lecture | ||||
| Thu, Oct 15 | 🧑🏫 Causality in experiments | 🛝 slides 12 🎶 notes 12 🍞 ae 08 |
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| Fri, Oct 16 | 💻 Project kick-off | |||||
| 9 | Tue, Oct 20 | 🧑🏫 Causality in observational studies | 🛝 slides 13 🎶 notes 13 |
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| Thu, Oct 22 | 🧑🏫 Intro to intervals | 🛝 slides 14 🎶 notes 14 🏠 ae 10 |
📘 ims - ch 12 📘 ims - ch 24 |
🎥 Quantifying uncertainty 🎥 Bootstrapping 🏋️ Tutorial: sampling uncertainty 🏋️ Tutorial: interval estimation |
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| Fri, Oct 23 | 💻 Project work period | Project proposal @ 5 PM | ||||
| 10 | Tue, Oct 27 | 🧑🏫 Intro to testing | 🛝 slides 15 🎶 notes 15 |
📘 ims - ch 11 📘 ims - ch 14 📘 ims - ch 24 |
🎥 Hypothesis testing 🏋️ Tutorial: permutation testing 🏋️ Tutorial: decision errors |
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| Thu, Oct 29 | 🧑🏫 Summary of inference | 🛝 slides 16 🎶 notes 16 👹 ae 11 |
🏋️ Tutorial: regression inference 🏋️ Tutorial: intervals for regression |
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| Fri, Oct 30 | 💻 Lab 6 | 🔬 lab 06 | Lab 6 @ end-of-lab | |||
| 11 | Tue, Nov 3 | 🧑🏫 Inference with CLT | 🛝 slides 17 🎶 notes 17 |
📘 ims - ch 13 📘 ims - ch 24 |
HW 5 @ 3 PM | |
| Thu, Nov 5 | 🧑🏫 Inference with CLT | 🏋️ Tutorial: t-test for the slope | ||||
| Fri, Nov 6 | 💻 Lab 7 | Lab 7 @ end-of-lab | ||||
| 12 | Tue, Nov 10 | 🧑🏫 Inference for proportions | 📘 ims - ch 16 | 🏋️ Tutorial: proportion inference | HW 6 @ 3 PM | |
| Thu, Nov 12 | 🧑🏫 Inference for proportions | 📘 ims - ch 17 📘 ims - ch 18 |
🏋️ Tutorial: two proportions 🏋️ Tutorial: independence 🏋️ Tutorial: goodness-of-fit |
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| Fri, Nov 13 | 💻 Lab 8 | Lab 8 @ end-of-lab | ||||
| 13 | Tue, Nov 17 | 🧑🏫 Inference for means | 📘 ims - ch 19 📘 ims - ch 20 |
🏋️ Tutorial: bootstrap anything 🏋️ Tutorial: t distribution |
HW 7 @ 3 PM | |
| Thu, Nov 19 | 🧑🏫 Inference for means | 📘 ims - ch 21 | 🏋️ Tutorial: two means | |||
| Fri, Nov 20 | 💻 Lab 9 | Lab 9 @ end-of-lab | ||||
| 14 | Tue, Nov 24 | 🧑🏫 ANOVA | 📘 ims - ch 22 | 🏋️ Tutorial: ANOVA | HW 8 @ 3 PM | |
| Thu, Nov 26 | ❌ Thanksgiving - No Lecture | |||||
| Fri, Nov 27 | ❌ Thanksgiving - No Lab | |||||
| 15 | Tue, Dec 1 | 🧑🏫 Data science ethics | 🎥 Misrepresentation 🎥 Data privacy 🎥 Algorithmic bias |
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| Thu, Dec 3 | 🧑🏫 The bottom line | |||||
| Fri, Dec 4 | 💻 Project presentations | |||||
| 16 | Mon, Dec 7 | Project report @ 5 PM | ||||
| Thu, Dec 10 | 📝 Final (2PM - 5PM) |