• MATH 697
  • Syllabus
    • General Information
    • Course Description
    • Grade Distribution
    • Target Syllabus
      • Overview and Descriptive Statistics (Weeks 1-4)
      • Probability (Weeks 1-4)
      • Discrete Random Variables and Probability Distributions (Weeks 1-4)
      • Continuous Random Variables and Probability Distributions (Weeks 5-8)
      • Joint Probability Distributions (Weeks 5-8)
      • Sampling Distributions and Limits (Weeks 5-8)
      • Statistical Inference (Weeks 9-12)
      • Likelihood Inference (Weeks 9-12)
      • Regression and Correlation (Weeks 9-12)
  • Prerequisites
    • Install R and RStudio
    • R Packages
    • Introduction to R
    • Background Reading
  • Slides
  • Assignments
  • Quiz
  • R Code
    • 0.1 Central Limit Theorem in Action
    • 0.2 IMPC Dataset
  • Distribution Tables
    • 0.3 Standard Normal
    • 0.4 t-Distribution
  • I Part I
  • 1 Overview and Descriptive Statistics
    • 1.1 Populations and Samples
      • 1.1.1 Variable
      • 1.1.2 Branches of Statistics
    • 1.2 Pictorial and Tabular Methods in Descriptive Statistics
    • 1.3 Measures of Location
    • 1.4 Measures of Variability
  • 2 Probability
    • Introduction
      • Probability: A Measure of Uncertainty
    • 2.1 Sample Spaces and Events
      • 2.1.1 Sample Spaces
      • 2.1.2 Events
    • 2.2 Axioms, Interpretations, and Properties of Probability
    • 2.3 Counting Techniques
      • 2.3.1 Permutations
      • 2.3.2 Combinations
    • 2.4 Conditional Probability
      • 2.4.1 Law of Total Probability
      • 2.4.2 Bayes’ Rule
    • 2.5 Independence
  • 3 Discrete Random Variables and Probability Distributions
    • Introduction
    • 3.1 Random Variables
    • 3.2 Probability Distributions for Discrete Random Variables
    • 3.3 Expected Values of Discrete Random Variables
    • 3.4 Moments and Moment Generating Functions
    • 3.5 The Binomial Probability Distribution
    • 3.6 The Poisson Probability Distribution
  • 4 Continuous Variables and Probability Distributions
    • Introduction
  • R Tutorial
  • A Vectorization, *apply and for loops
    • A.1 Vectorization
    • A.2 Family of *apply functions
      • A.2.1 Loops vs. Apply
      • A.2.2 Descriptive Statistics using *apply
      • A.2.3 Creating new columns using sapply
      • A.2.4 Applying functions to subsets using tapply
      • A.2.5 Nested for loops using mapply
    • A.3 Creating dynamic documents with mapply
  • B Appendix B
  • References
  • Published with bookdown

MATH 697

Assignments

  1. A1 (due September 26, 2017)
  2. A2 (due October 12, 2017)
  3. A3 (due October 26, 2017)
  4. A4 (due November 9, 2017)