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Overview
Learning Statistics: Concepts and Applications in R is a comprehensive course designed to introduce learners to foundational statistical concepts while applying them practically using the R programming language. Ideal for students, researchers, and professionals, this course bridges theory with hands-on data analysis, empowering users to make informed decisions based on statistical reasoning. The course begins with core topics such as descriptive statistics, probability theory, and data visualization. Learners will then explore inferential statistics, including hypothesis testing, confidence intervals, and regression...
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
Instructor
Talithia Williams
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