Qualifying exam prep
Graduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
Probability review
- Measure-theoretic probability essentials
- Distribution theory and transformations
- Limit theorems and convergence modes
- Conditional expectation problems
- Characteristic functions exercises
Mathematical statistics
- Sufficiency, completeness, and UMVU estimation
- MLE asymptotics and Fisher information
- Hypothesis testing: Neyman–Pearson constructions
- Exponential families and conjugate priors
- Decision theory short proofs
Linear models and inference
- Gauss–Markov and normal theory derivations
- ANOVA decomposition problems
- GLM and exponential family review
- Time-permitting: survival and asymptotics previews
- Exam strategy and timed problem sets
STEM / applied
Exam practice and strategy
- Past qual exams from target departments
- Timed mock exams with rubrics
- Common proof templates and pitfalls
- Identifying weak areas by topic matrix
- Study groups and oral exam practice
- Balancing breadth vs depth for your program
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Content tailored to student program syllabus. Covers probability, mathematical statistics, and linear models at qualifying-exam depth.