Statistics II: Inference & Regression
Confidence intervals, hypothesis tests, and simple regression with a focus on intuition and practice.
What you’ll learn
- Link confidence intervals and hypothesis tests without memorising recipes
- Choose, run and interpret t-tests, proportion tests and chi-square tests
- Use simulation and bootstrapping to sanity-check analytic results
- Fit and interpret simple linear regression, including residual diagnostics
- Explain uncertainty, p-values and effect sizes in plain language
Curriculum
M1
From description to inference — 2 lessons
Show lessons
-
Review: summaries, normal model & CLTLesson outline – video coming soon.14m
-
From descriptive to inferential thinkingLesson outline – video coming soon.14m
M2
Sampling distributions (deeper) — 2 lessons
Show lessons
-
Sampling distributions for meansLesson outline – video coming soon.16m
-
Sampling distributions for proportionsLesson outline – video coming soon.16m
M3
One-sample confidence intervals — 3 lessons
Show lessons
-
Building confidence intervals (big picture)Lesson outline – video coming soon.16m
-
CI for a single mean (z/t)Lesson outline – video coming soon.16m
-
CI for a single proportionLesson outline – video coming soon.16m
M4
Hypothesis testing workflow — 2 lessons
Show lessons
-
Hypothesis tests: null, alternative & p-valuesLesson outline – video coming soon.16m
-
Decision errors, power & practical significanceLesson outline – video coming soon.16m
M5
Two-sample comparisons — 2 lessons
Show lessons
-
Two-sample t-tests (independent & paired)Lesson outline – video coming soon.18m
-
Two-sample tests for proportionsLesson outline – video coming soon.16m
M6
Counts & chi-square tests — 2 lessons
Show lessons
-
Chi-square goodness-of-fitLesson outline – video coming soon.18m
-
Chi-square tests for independenceLesson outline – video coming soon.18m
M7
ANOVA & multiple comparisons — 2 lessons
Show lessons
-
One-way ANOVA: idea & F-testLesson outline – video coming soon.18m
-
Multiple comparisons & interpretationLesson outline – video coming soon.14m
M8
Correlation & simple regression — 2 lessons
Show lessons
-
Correlation vs causation & lurking variablesLesson outline – video coming soon.16m
-
Least-squares regression: line & parametersLesson outline – video coming soon.18m
M9
Regression diagnostics — 2 lessons
Show lessons
-
Residuals, diagnostics & model assumptionsLesson outline – video coming soon.16m
-
Transformations & outliersLesson outline – video coming soon.16m
M10
Simulation & bootstrapping — 2 lessons
Show lessons
-
Simulation with Python / spreadsheetsLesson outline – video coming soon.18m
-
Bootstrap intuition (resampling)Lesson outline – video coming soon.16m
M11
Review & exam-style practice — 2 lessons
Show lessons
-
Putting it all together: study walkthroughLesson outline – video coming soon.18m
-
Exam-style mixed practiceLesson outline – video coming soon.18m
Projects
- Re-analyse a past lab dataset using a full inference workflow and write-up
- Compare classical tests with bootstrap or simulation-based alternatives
- Build a small regression case study with visuals and interpretation paragraphs
Prerequisites
- All core ideas from Statistics I (or an equivalent intro stats course)
- Comfort with basic probability, the normal model and z-scores
- Ability to use a calculator or software to compute basic statistics
Who is this for?
- Students currently in an inference / regression statistics course
- STEM majors heading toward data science or ML who need stronger inference foundations
- Analysts who already summarise data but want to make defensible claims
Outcomes
- Able to pick an appropriate inferential procedure for common study designs
- More confident interpreting confidence intervals, p-values and effect sizes
- Ready for upper-level statistics, econometrics or machine learning
Resources
- Starter project / template (ZIP)
- Setup & study checklists (PDF)
- Core formulas / syntax cheatsheet (PDF)
Tip: Right-click → “Save link as…” if your browser opens the file.
FAQ
Is this beginner-friendly?
Yes. We start from mental models and build up with guided practice and plenty of worked examples.
Will I need extra software?
We stick to free or standard tools. Any optional extras are clearly marked and have alternatives.
Do I get updates?
Yes—lifetime access with updates as the field, tools, and exam expectations evolve.