Lesson 16: WPR I

WPR I Information

WPR I — Today!

Covers: Concepts from Lessons 6–14
Time: 55 minutes
Authorized: Course Statistics Reference Card (SRC) and the issued calculator
No technology — no R, no internet, no electronic devices
Round all numbers to three significant digits

Topics Covered (Lessons 6–14)

Block I: Probability & Random Variables

Probability (Lessons 6–8)

Sample spaces, events, Kolmogorov Axioms
Complement rule, Inclusion-Exclusion
Conditional probability, Bayes’ Rule, Law of Total Probability
Counting (permutations, combinations), Independence

Discrete Random Variables (Lessons 9–11)

PMF, CDF, Expected Value, Variance
Binomial: \(X \sim Bin(n, p)\) — \(\mu = np\), \(\sigma^2 = np(1-p)\)
Poisson: \(X \sim Pois(\lambda)\) — \(\mu = \sigma^2 = \lambda\)

Continuous Random Variables (Lessons 12–14)

PDF, CDF via integration, piecewise CDF
Normal: \(X \sim N(\mu, \sigma^2)\) — z-scores, table lookup
Exponential: \(T \sim Exp(\lambda)\) — \(\mu = 1/\lambda\), memoryless property

Reminders

Fully specify distributions: state the family AND parameter values (e.g., \(X \sim Bin(12, 0.75)\))
Show your work clearly — partial credit is available
Use proper notation: \(P(X = k)\), \(E[X]\), \(Var(X)\), \(\sigma\)
Check your piecewise CDFs: they should have all three regions
For Normal problems: standardize first, then use the z-table

Before You Leave

Today

WPR I — Lessons 6–14
You’ve got this!

Any questions?

Next Lesson

Lesson 17: Block II — Inference Begins

Block II: Inference
New topics ahead!

Upcoming Graded Events

WPR II - Lesson 27