Data Science

Applied Statistics Tutorial 2024 - Learn Statistics Online for Free

7 Modules34 Lessons2379 Learners

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

Start LearningLast Updated: 17th April, 2024

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:

Linear Algebra and Vector Algebra
Calculus
Probability Theory
Random Variables
Probability Distributions
Statistical Inference
Hypothesis Testing

Course Curriculum

Module 1Linear Algebra and Vector Algebra

Introduction to Linear Algebra

Vector Algebra: Formulas, Operations, Examples

Matrices and Matrix Operations

Determinants and Inverse Matrices

Eigenvalues, Eigenvectors and Diagonalization

Module 2Calculus

Limits and Continuity: Definitions, Types, Formulas

Derivatives: Basics and Application of Derivatives

Integration: Basics and Application of Integrals

Multivariate Calculus and Optimization

Module 3Probability Theory

Probability and Fundamental Principle of Counting

Random Variables and Probability Distributions

Joint Probability Distribution and Conditional Probability

Bayes' Theorem, Conditional Probability and Independence

Moments and Moment Generating Functions

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

Module 4Random Variables

Discrete Random Variable and Probability Mass Function

Continuous Random Variable and Probability Density Function

Moment Generating Functions and Expected Values

Joint Distributions, Covariance and Correlation

Module 5Probability Distributions

Normal Distribution and Standard Normal Distribution

Binomial and Poisson Distribution

Exponential and Gamma Distribution

Chi-Square Distribution and Student's t-Distribution

F Distribution: Properties, Applications, Examples

Module 6Statistical Inference

Point Estimation and Interval Estimation

Maximum Likelihood Estimation

Hypothesis Testing and p-Values

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

Confidence Intervals and Margin of Error

Power and Sample Size Estimation

Module 7Hypothesis Testing

Introduction to Hypothesis Testing

One Sample T-Test and Z-Test

Two Sample T-Test and Z-Test

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

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Summary

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