🔍 Phase 6 | Statistics & Data Interpretation

Welcome to Phase 6 of your data journey! Understanding statistics is crucial for turning data into confident, data-driven decisions. In this phase, you’ll learn the fundamentals of descriptive and inferential statistics, A/B testing, and predictive modeling through regression.

1️⃣ Descriptive & Inferential Statistics

📊 Descriptive Statistics

Start by summarizing and understanding your data:

Measures of Central Tendency
- Mean, Median, Mode
Measures of Dispersion
- Range, Variance, Standard Deviation
Understand distribution shapes: Skewness, Kurtosis

📈 Inferential Statistics

Go beyond the data you have to make broader assumptions:

Probability Distributions
- Normal, Binomial, Poisson, Exponential
Statistical Significance
- Understanding p-values, confidence levels, and hypothesis testing
Formulating and testing assumptions about populations

2️⃣ A/B Testing & Confidence Intervals

Test business ideas and product changes with confidence.

🧪 A/B Testing

What is A/B testing and why it matters
Formulate null and alternative hypotheses
Determine appropriate sample sizes
Understand and manage:
- Type I Error (False Positive)
- Type II Error (False Negative)

📏 Confidence Intervals

How to calculate and interpret confidence intervals
The impact of sample size and data variance
Use CIs to quantify uncertainty in your results

3️⃣ Regression & Predictive Analysis

Use regression models to forecast trends and predict outcomes.

🔢 Linear & Logistic Regression

Simple & Multiple Linear Regression
- Predict continuous outcomes
Logistic Regression for classification problems
- Predict binary outcomes like “yes/no”, “churn/stay”

🔍 Feature Selection & Model Evaluation

Identify important predictors
Avoid multicollinearity using techniques like VIF
Evaluate model performance using:
- R², RMSE for regression
- Precision, Recall, AUC-ROC for classification

💼 Practical Business Use Cases

Apply what you’ve learned in real-world scenarios:

📈 Sales Forecasting – Use linear regression to predict revenue
🔁 Customer Churn Prediction – Use logistic regression for classification
🧪 A/B Test Analysis – Combine hypothesis testing with regression insights

🎯 What’s Next?

By the end of this phase, you’ll be able to apply statistical methods to validate ideas, evaluate experiments, and build predictive models. In Phase 7, we’ll explore how to apply these concepts in a full end-to-end capstone project.

Statistics is the grammar of data. Master it, and you’ll speak data fluently.