Stat 427: R Programming
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Course Logistics
Syllabus
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Lectures
Schedule
Module 1: Getting Started
Module 2: R Scripts
Module 3: Functions
Module 4: Basic Graphs
Module 5: Data Input and Output
Module 6: Iterative Data Processing
Module 7: Logic and Control
Module 8: R Markdown and Packages
Module 9: Mathematical Functions
Module 10: Data Validation, Cleaning, Combining Datasets
Module 11: Matrix Arithmetic
Module 12: Systems of Linear Equations
Module 13: Advanced Graphs
Module 14: Probability and Simulation
Module 15: Introductory Inferential Methods
Supplemental Topics
>
Module 16: Real World Examples
Module 17: Fitting Models to Data
Review
>
Intro Class lectures
Coursework
Instructions
Labs
Project
R
Code
Information
Mapping uidaho shared drive to your computer
(only works to access Courses folder in Shared drive)
normal distribution
sampling distributions
1-sample CIs (confidence intervals)
1-sample hypothesis tests
2-sample methods
categorical analyses
SLR (simple linear regression)
ANOVA (Analysis of Variance) and multiple comparisons
For more topics, see Stat 251
lectures
Home
Course Logistics
Syllabus
Contact
Dates
Links
Lectures
Schedule
Module 1: Getting Started
Module 2: R Scripts
Module 3: Functions
Module 4: Basic Graphs
Module 5: Data Input and Output
Module 6: Iterative Data Processing
Module 7: Logic and Control
Module 8: R Markdown and Packages
Module 9: Mathematical Functions
Module 10: Data Validation, Cleaning, Combining Datasets
Module 11: Matrix Arithmetic
Module 12: Systems of Linear Equations
Module 13: Advanced Graphs
Module 14: Probability and Simulation
Module 15: Introductory Inferential Methods
Supplemental Topics
>
Module 16: Real World Examples
Module 17: Fitting Models to Data
Review
>
Intro Class lectures
Coursework
Instructions
Labs
Project
R
Code
Information