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Statistics

Explore tools for advanced data analytics, including statistical evaluations, data modeling, and visualizations to enhance decision-making.

Basic Statistical Analysis

Descriptive Statistics

Summarize and describe datasets with key metrics:

  • Mean, Median, Mode
  • Range and Interquartile Range (IQR)
  • Skewness and Kurtosis
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Inferential Statistics

Make predictions and test hypotheses:

  • t-tests, z-tests, chi-square tests
  • Confidence intervals and p-value calculation
  • ANOVA and regression analysis
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Probability Tools

Analyze uncertainty and distributions:

  • Probability Density and Cumulative Distribution Functions
  • Distributions: Binomial, Poisson, Normal, and more
  • Z-Score and related calculations
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Time Series and Modeling

Time Series Analysis

Analyze trends and patterns over time:

  • Moving averages and exponential smoothing
  • Autocorrelation analysis
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Markov Chains and Random Walks

Simulate and model stochastic processes:

  • Transition matrices for Markov chains
  • Simulate random walks
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Simulation and Bayesian Analysis

Monte Carlo Simulations

Simulate outcomes for advanced analysis:

  • Generate random samples
  • Expected value and risk analysis
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Bayesian Analysis

Update beliefs using Bayesian methods:

  • Compute prior, posterior, and likelihood
  • Perform Bayesian updating
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Linear Algebra and Advanced Calculations

Linear Algebra Tools

Perform matrix operations and decompositions:

  • Matrix multiplication and inverses
  • Singular Value Decomposition (SVD)
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Advanced Calculations

Analyze datasets using advanced techniques:

  • Principal Component Analysis (PCA)
  • Compute eigenvalues, eigenvectors, and determinants
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