Learn to tame data by learning statistics using the R programming language, taught by an award-winning and innovative educator.
Learning Statistics: Concepts and Applications in R
Overview
Syllabus
- By This Professor
- 01: How to Summarize Data with Statistics
- 02: Exploratory Data Visualization in R
- 03: Sampling and Probability
- 04: Discrete Distributions
- 05: Continuous and Normal Distributions
- 06: Covariance and Correlation
- 07: Validating Statistical Assumptions
- 08: Sample Size and Sampling Distributions
- 09: Point Estimates and Standard Error
- 10: Interval Estimates and Confidence Intervals
- 11: Hypothesis Testing: 1 Sample
- 12: Hypothesis Testing: 2 Samples, Paired Test
- 13: Linear Regression Models and Assumptions
- 14: Regression Predictions, Confidence Intervals
- 15: Multiple Linear Regression
- 16: Analysis of Variance: Comparing 3 Means
- 17: Analysis of Covariance and Multiple ANOVA
- 18: Statistical Design of Experiments
- 19: Regression Trees and Classification Trees
- 20: Polynomial and Logistic Regression
- 21: Spatial Statistics
- 22: Time Series Analysis
- 23: Prior Information and Bayesian Inference
- 24: Statistics Your Way with Custom Functions
Taught by
Talithia Williams
