R - Statistics

R is used for statistical computing and data analysis. It provides built-in functions and packages that help perform statistical calculations, analyze datasets and visualize results.

• Perform descriptive and inferential statistical analysis
• Apply statistical tests and probability distributions
• Analyze datasets for research, data science and machine learning

Getting Started with R

This section introduces the basics of R, including how to install R and RStudio and understand the fundamental data types and data structures used for statistical analysis.

• How to Install R and R Studio
• Introduction to R language
• Data Types
• Data Structures in R

Probability in R

This section covers how we can calculate and visualize probabilities using R functions and distributions.

• Probabilities
• Conditional Probability
• Probability Distributions
• Cumulative Frequency and Probability Table
• Plot Probability Distribution Function
• Expected Value

Descriptive Statistics in R

We will learn how to compute and interpret measures of central tendency, variability and data distribution.

• Measures of Central Tendency
• Measures of Dispersion
• Summary Statistics
• Skewness
• Kurtosis

Inferential Statistics in R

We will explore how we can make inferences about populations using statistical tests, confidence intervals and ANOVA.

• Central Limit Theorem
• Confidence Intervals
• Parametric Tests
• Non-Parametric Tests
• ANOVA (Analysis of Variance)
• Hypothesis Testing

Sampling Techniques in R

We will cover various sampling methods for drawing samples from a population using R.

• Random Samples
• Sample from a Population
• Systematic Sampling
• Stratified Sampling
• Cluster Sampling

Statistical Errors and Model Evaluation

We will see different types of errors and how we can calculate them using R.

• Type I Error
• Type II Error
• Root-Mean-Square Error
• Mean Absolute Error
• ROC
• AUC

Covariance & Correlation

We will explore how we can measure the relationship between two variables using covariance and correlation in R.

• Covariance and Correlation
• Regression Analysis
• Covariance Matrix
• Spearman's Rank Correlation Measure
• Pearson Correlation
• Kendall Correlation Testing

Graphical Representation

We will learn how we can create various plots in R to visualize data distributions and relationships.

• Normal Probability Plot
• Quantile Quantile plots
• Kernel Density Plot
• Bar Chart
• Pie Charts
• Histogram
• Line Plot
• Scatter Plot
• Boxplot
• Q-Q Plot
• Correlation Plot
• Density Plot

To get a detailed overview of R programming, you can refer to: R Programming Tutorial