Library
Statistics
The subject of Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. After completing these 100 lessons, learners will be able to apply statistical techniques to real-world problems, make informed decisions based on data analysis, and interpret results to drive business or research outcomes. For example, a learner can use regression analysis to predict house prices based on factors like location and size, enabling them to make data-driven decisions in the real estate industry. By mastering statistics, learners can unlock insights from data and drive meaningful change in various fields.
Мини план лекција · 100 лекција
Почетни · лекције 1–25
- Introduction to Statistics
- Types of Data
- Scales of Measurement
- Collecting Data Methods
- Organizing Data Sets
- Frequency Distributions
- Bar Graphs and Histograms
- Pie Charts Basics
- Measures of Central Tendency
- Mean Calculation
- Median Calculation
- Mode Identification
- Range and Variability
- Introduction to Probability
- Simple Probability Rules
- Complement and Addition Rules
- Multiplication Rule Basics
- Independent and Dependent Events
- Introduction to Sampling
- Sampling Methods Overview
- Bias in Sampling
- Data Representation Techniques
- Stem-and-Leaf Plots
- Box-and-Whisker Plots
- Introduction to Distributions
Средњи · лекције 26–50
- Normal Distribution Basics
- Standard Normal Curve
- Z-Scores and Interpretation
- Central Limit Theorem
- Confidence Intervals Introduction
- Calculating Confidence Intervals
- Margin of Error Concepts
- Hypothesis Testing Basics
- Null and Alternative Hypotheses
- Type I and Type II Errors
- P-Values Explained
- One-Sample Z-Test
- One-Sample T-Test
- Two-Sample T-Test
- Paired Sample Tests
- Chi-Square Test Introduction
- Chi-Square Goodness of Fit
- Chi-Square Test for Independence
- Correlation Concepts
- Calculating Pearson Correlation
- Scatterplots and Interpretation
- Simple Linear Regression
- Regression Line Equation
- Coefficient of Determination
- Residual Analysis Basics
Напредни · лекције 51–75
- Multiple Regression Analysis
- Assumptions in Regression
- ANOVA Introduction
- One-Way ANOVA
- Post Hoc Tests
- Two-Way ANOVA
- Nonparametric Tests Overview
- Mann-Whitney U Test
- Wilcoxon Signed-Rank Test
- Kruskal-Wallis Test
- Spearman Rank Correlation
- Logistic Regression Basics
- Odds Ratios Interpretation
- Survival Analysis Introduction
- Kaplan-Meier Estimator
- Cox Proportional Hazards Model
- Time Series Data Concepts
- Moving Averages Technique
- Exponential Smoothing
- Autocorrelation Function
- Stationarity in Time Series
- ARIMA Models Introduction
- Principal Component Analysis
- Factor Analysis Basics
- Cluster Analysis Overview
Стручни (already in the field) · лекције 76–100
- Advanced Bayesian Statistics
- Bayesian Inference Methods
- Markov Chain Monte Carlo
- Hierarchical Models
- Structural Equation Modeling
- Latent Variable Models
- Multilevel Modeling Concepts
- Generalized Linear Models
- Mixed Effects Models
- Robust Statistical Methods
- Bootstrap Techniques
- Permutation Tests
- Advanced Survival Models
- Competing Risks Analysis
- Spatial Statistics Introduction
- Geostatistics Basics
- Functional Data Analysis
- High-Dimensional Data Analysis
- Machine Learning Overview
- Supervised Learning Methods
- Unsupervised Learning Techniques
- Deep Learning in Statistics
- Causal Inference Principles
- Instrumental Variables Method
- Advanced Time Series Modeling
Related subjects
- Basic Probability Concepts
- Random Variables and Distributions
- Descriptive Statistics Measures
- Inferential Statistics Principles
- Regression Analysis Fundamentals
- Time Series Analysis Techniques
- Hypothesis Testing Procedures
- Confidence Intervals Construction
- Non-Parametric Tests Overview
- Regression Diagnostics and Model Selection
- Survival Analysis Methods
- Bayesian Statistics Introduction