Learning Statistics: Concepts and Applications in R

Overview

Learn to tame data by mastering statistics using the R programming language, taught by an innovative educator. “Learning Statistics: Concepts and Applications in R” encompasses 24 lectures that emphasize practical applications and real-world data analysis.

Course Instructor

The course is led by Professor Talithia Williams of Harvey Mudd College, who guides you through essential statistical concepts and methods while utilizing RStudio for hands-on practice.

Video Lessons

1. How to Summarize Data with Statistics
   • Duration: 30 min
   • Description: Discover how to describe data through statistical measures such as mean, median, and standard deviation.

2. Exploratory Data Visualization in R
   • Duration: 26 min
   • Description: Learn the fundamentals of R programming and how to visualize data effectively.

• Sampling and Probability
  • Duration: 25 min
  • Description: Understand the principles of sampling and calculate probabilities in statistical analysis.

4. Discrete Distributions
   • Duration: 30 min
   • Description: Delve into various discrete probability distributions and their applications.

5. Continuous and Normal Distributions
   • Duration: 30 min
   • Description: Focus on normal distribution and learn to calculate properties using R.

6. Covariance and Correlation
   • Duration: 26 min
   • Description: Measure the relationship between two variables through covariance and correlation coefficients.

• Validating Statistical Assumptions
  • Duration: 27 min
  • Description: Analyze the classic Iris Flower Data Set to validate statistical assumptions.

8. Sample Size and Sampling Distributions
   • Duration: 31 min
   • Description: Learn to estimate populations using bootstrapping techniques for sample data.

9. Point Estimates and Standard Error
   • Duration: 23 min
   • Description: Study point estimates and their significance in statistical inference.

• Interval Estimates and Confidence Intervals
  • Duration: 29 min
  • Description: Explore how confidence intervals provide a range of estimates for populations.

• Hypothesis Testing: 1 Sample
  • Duration: 28 min
  • Description: Learn the methodology to determine the validity of a hypothesized parameter.

12. Hypothesis Testing: 2 Samples, Paired Test
    • Duration: 27 min
    • Description: Extend hypothesis testing to two samples, evaluating their effects.

• Linear Regression Models and Assumptions
  • Duration: 27 min
  • Description: Investigate linear regression and its applications in analyzing relationships between variables.

14. Regression Predictions, Confidence Intervals
    • Duration: 27 min
    • Description: Learn to mitigate errors in your models by adjusting for variance.

15. Multiple Linear Regression
    • Duration: 34 min
    • Description: Explore the complexities of multiple variables in regression analysis.

16. Analysis of Variance: Comparing 3 Means
    • Duration: 30 min
    • Description: Use ANOVA for comparing means across multiple groups for statistical significance.

17. Analysis of Covariance and Multiple ANOVA
    • Duration: 32 min
    • Description: Combine regression and ANOVA techniques to analyze covariance impacts.

• Statistical Design of Experiments
  • Duration: 29 min
  • Description: Learn how proper experiment design optimizes data collection and analysis.

19. Regression Trees and Classification Trees
    • Duration: 28 min
    • Description: Delve into decision trees for analyzing categorical and continuous outcomes.

20. Polynomial and Logistic Regression
    • Duration: 34 min
    • Description: Understand the applications of polynomial and logistic regression techniques.

21. Spatial Statistics
    • Duration: 35 min
    • Description: Explore spatial analysis tools and their applications in geographic data.

22. Time Series Analysis
    • Duration: 34 min
    • Description: Examine time series data and its applications in forecasting trends.

23. Prior Information and Bayesian Inference
    • Duration: 35 min
    • Description: Study Bayesian statistics and how it differs from traditional methods.

24. Statistics Your Way with Custom Functions
    • Duration: 34 min
    • Description: Learn to create custom functions in R, enhancing your statistical analysis capabilities.