Learning Statistics: Concepts and Applications in R

Learn to tame data by learning statistics using the R programming language, taught by an award-winning and innovative educator.

• 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