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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.

Maikling plano ng aralin · 100 aralin

Baguhan · mga aralin 1–25
  1. Introduction to Statistics
  2. Types of Data
  3. Scales of Measurement
  4. Collecting Data Methods
  5. Organizing Data Sets
  6. Frequency Distributions
  7. Bar Graphs and Histograms
  8. Pie Charts Basics
  9. Measures of Central Tendency
  10. Mean Calculation
  11. Median Calculation
  12. Mode Identification
  13. Range and Variability
  14. Introduction to Probability
  15. Simple Probability Rules
  16. Complement and Addition Rules
  17. Multiplication Rule Basics
  18. Independent and Dependent Events
  19. Introduction to Sampling
  20. Sampling Methods Overview
  21. Bias in Sampling
  22. Data Representation Techniques
  23. Stem-and-Leaf Plots
  24. Box-and-Whisker Plots
  25. Introduction to Distributions
Katamtaman · mga aralin 26–50
  1. Normal Distribution Basics
  2. Standard Normal Curve
  3. Z-Scores and Interpretation
  4. Central Limit Theorem
  5. Confidence Intervals Introduction
  6. Calculating Confidence Intervals
  7. Margin of Error Concepts
  8. Hypothesis Testing Basics
  9. Null and Alternative Hypotheses
  10. Type I and Type II Errors
  11. P-Values Explained
  12. One-Sample Z-Test
  13. One-Sample T-Test
  14. Two-Sample T-Test
  15. Paired Sample Tests
  16. Chi-Square Test Introduction
  17. Chi-Square Goodness of Fit
  18. Chi-Square Test for Independence
  19. Correlation Concepts
  20. Calculating Pearson Correlation
  21. Scatterplots and Interpretation
  22. Simple Linear Regression
  23. Regression Line Equation
  24. Coefficient of Determination
  25. Residual Analysis Basics
Mataas · mga aralin 51–75
  1. Multiple Regression Analysis
  2. Assumptions in Regression
  3. ANOVA Introduction
  4. One-Way ANOVA
  5. Post Hoc Tests
  6. Two-Way ANOVA
  7. Nonparametric Tests Overview
  8. Mann-Whitney U Test
  9. Wilcoxon Signed-Rank Test
  10. Kruskal-Wallis Test
  11. Spearman Rank Correlation
  12. Logistic Regression Basics
  13. Odds Ratios Interpretation
  14. Survival Analysis Introduction
  15. Kaplan-Meier Estimator
  16. Cox Proportional Hazards Model
  17. Time Series Data Concepts
  18. Moving Averages Technique
  19. Exponential Smoothing
  20. Autocorrelation Function
  21. Stationarity in Time Series
  22. ARIMA Models Introduction
  23. Principal Component Analysis
  24. Factor Analysis Basics
  25. Cluster Analysis Overview
Dalubhasa (already in the field) · mga aralin 76–100
  1. Advanced Bayesian Statistics
  2. Bayesian Inference Methods
  3. Markov Chain Monte Carlo
  4. Hierarchical Models
  5. Structural Equation Modeling
  6. Latent Variable Models
  7. Multilevel Modeling Concepts
  8. Generalized Linear Models
  9. Mixed Effects Models
  10. Robust Statistical Methods
  11. Bootstrap Techniques
  12. Permutation Tests
  13. Advanced Survival Models
  14. Competing Risks Analysis
  15. Spatial Statistics Introduction
  16. Geostatistics Basics
  17. Functional Data Analysis
  18. High-Dimensional Data Analysis
  19. Machine Learning Overview
  20. Supervised Learning Methods
  21. Unsupervised Learning Techniques
  22. Deep Learning in Statistics
  23. Causal Inference Principles
  24. Instrumental Variables Method
  25. Advanced Time Series Modeling

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