UsefulLinks
Statistics
Statistics with R
1. Introduction to R and Statistical Computing
2. R Fundamentals and Basic Operations
3. R Package System
4. R Data Structures
5. Data Import and Export
6. Data Cleaning and Preprocessing
7. Data Manipulation with dplyr
8. Descriptive Statistics
9. Data Visualization Fundamentals
10. Advanced Data Visualization with ggplot2
11. Probability Theory
12. Probability Distributions
13. Sampling and Sampling Distributions
14. Statistical Inference Foundations
15. Hypothesis Testing Framework
16. One-Sample Tests
17. Two-Sample Tests
18. Chi-squared Tests
19. Analysis of Variance (ANOVA)
20. Correlation Analysis
21. Simple Linear Regression
22. Regression Diagnostics
23. Multiple Linear Regression
24. Generalized Linear Models
25. Nonparametric Statistics
26. Introduction to Time Series Analysis
27. Introduction to Machine Learning
28. Reproducible Research
29. Statistical Computing Best Practices
26.
Introduction to Time Series Analysis
26.1.
Time Series Concepts
26.1.1.
Time Series Data
26.1.2.
Temporal Dependence
26.1.3.
Stationarity
26.1.4.
Trend and Seasonality
26.2.
Time Series Objects in R
26.2.1.
ts Objects
26.2.2.
zoo Objects
26.2.3.
xts Objects
26.2.4.
Date and Time Handling
26.3.
Time Series Visualization
26.3.1.
Time Plots
26.3.2.
Seasonal Plots
26.3.3.
Lag Plots
26.3.4.
ACF and PACF Plots
26.4.
Time Series Decomposition
26.4.1.
Trend Component
26.4.2.
Seasonal Component
26.4.3.
Irregular Component
26.4.4.
Additive vs Multiplicative
26.5.
Basic Forecasting
26.5.1.
Naive Methods
26.5.2.
Moving Averages
26.5.3.
Exponential Smoothing
26.5.4.
ARIMA Models
26.6.
Model Evaluation
26.6.1.
Forecast Accuracy Measures
26.6.2.
Cross-validation for Time Series
26.6.3.
Residual Analysis
Previous
25. Nonparametric Statistics
Go to top
Next
27. Introduction to Machine Learning