Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Learning Statistics: Concepts and Applications in R

via The Great Courses Plus

Go to class Write review

Scrimba

Free courses from frontend to fullstack and AI

Coursera

AI, Data Science & Cloud Certificates from Google, IBM & Meta

Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off

One annual plan covers every course and certificate on Coursera. 40% off for a limited time.

Get Full Access

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

Syllabus

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
By This Professor

Taught by

Talithia Williams

Reviews

3.8 rating at The Great Courses Plus based on 60 ratings

Start your review of Learning Statistics: Concepts and Applications in R