Statistics [31]: Summary

This series of posts are notes from Applied Statistics given by Prof. LIANG Heng of Tsinghua University.

The main concents include:

• Commmon distributions & statistical quantities (statistics)
  • Probability vs Statistics
  • Shakespear’s New Poem
  • Some Common Discrete Distributions
  • Some Common Continuous Distributions
  • Statistical Quantities
  • Order Statistics
  • Multivariate Normal Distributions
  • Conditional Distributions and Expectation
  • Problem Set [01] - Probabilities
• Parameter estimation
  • Parameter Point Estimation
  • Evaluation of Point Estimation
  • Parameter Interval Estimation
• Hypothesis test & analysis of variance
  • Parameter Hypothesis Test
  • t Test
  • Chi-Squared Test
  • Analysis of Variance
  • Summary of Statistical Tests
  • Python [01] - Data Representation
  • Python [02] - t Test & F Test
  • Python [03] - Chi-Squared Test
  • Problem Set [02] - Parameter Estimation
• Experimental design
  • Experimental Design
• Regression analysis
  • Unitary Regression Model
  • Multiple Regression Model
• Monte Carlo
  • Monte Carlo
  • Variance Reducing Techniques
  • From Uniform to General Distributions
  • Problem Set [03] - Monte Carlo
• Factor analysis & principle component analysis
  • Factor and Principle Component Analysis
• Clustering analysis
  • Clustering Analysis

All the codes are written in Python, the main packages used include:

• pandas
• numpy
• matplotlib
• seaborn
• scipy
• statsmodels
• sklearn