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Applied Statistics Tutorial 2024 - Learn Statistics Online for Free

7 Modules34 Articles162 Learners

Master the basics of statistics with our applied statistics tutorial. Learn applied statistics techniques and concepts to enhance your data analysis skills.

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Welcome to our comprehensive applied statistics tutorial, designed to help you master the fundamentals of applied statistics. Whether you're new to the field or seeking to refine your skills, this tutorial offers an in-depth exploration of key concepts and techniques.

Learn applied statistics from the ground up, covering topics like probability, hypothesis testing, algebra, calculus and much more. With clear explanations and practical examples, this applied statistics course will equip you with the knowledge and tools you need to make informed data-driven decisions. Dive into the world of statistics, enhance your analytical abilities, and gain a competitive edge with our applied statistics tutorial.

The modules covered in our Applied Statistics Tutorial are:

  1. Linear Algebra and Vector Algebra
  2. Calculus
  3. Probability Theory
  4. Random Variables
  5. Probability Distributions
  6. Statistical Inference
  7. Hypothesis Testing

Course Curriculum

Module 1Linear Algebra and Vector Algebra

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Introduction to Linear Algebra

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Vector Algebra: Formulas, Operations, Examples

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Matrices and Matrix Operations

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Determinants and Inverse Matrices

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Eigenvalues, Eigenvectors and Diagonalization

Module 2Calculus

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Limits and Continuity: Definitions, Types, Formulas

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Derivatives: Basics and Application of Derivatives

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Integration: Basics and Application of Integrals

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Multivariate Calculus and Optimization

Module 3Probability Theory

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Probability and Fundamental Principle of Counting

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Random Variables and Probability Distributions

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Joint Probability Distribution and Conditional Probability

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Bayes' Theorem, Conditional Probability and Independence

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Moments and Moment Generating Functions

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Limit Theorems (Central Limit Theorem, Law of Large Numbers)

Module 4Random Variables

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Discrete Random Variable and Probability Mass Function

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Continuous Random Variable and Probability Density Function

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Moment Generating Functions and Expected Values

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Joint Distributions, Covariance and Correlation

Module 5Probability Distributions

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Normal Distribution and Standard Normal Distribution

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Binomial and Poisson Distribution

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Exponential and Gamma Distribution

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Chi-Square Distribution and Student's t-Distribution

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F Distribution: Properties, Applications, Examples

Module 6Statistical Inference

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Point Estimation and Interval Estimation

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Maximum Likelihood Estimation

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Hypothesis Testing and p-Values

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Type I and Type II Errors: Definition, Differences, Example

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Confidence Intervals and Margin of Error

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Power and Sample Size Estimation

Module 7Hypothesis Testing

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Introduction to Hypothesis Testing

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One Sample T-Test and Z-Test

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Two Sample T-Test and Z-Test

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Analysis of Variance (ANOVA): Types, Formula, Examples

Summary

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