r-statistics-foundations

A comprehensive 10-day intensive curriculum on Statistics and R for Biological Sciences. Covers data wrangling, probability distributions, hypothesis testing (t-test, ANOVA), and linear regression

Foundations of Statistics & R for Biologists

This repository documents an intensive 10-day curriculum designed to build a professional foundation in Data Science and Statistical Inference using R.

Course Syllabus

Week 1: Core Fundamentals & Exploration

• Day 1-2: R Environment, Data Structures (Vectors, DataFrames), and I/O (CSV/Excel).
• Day 3-4: Descriptive Statistics (SD, IQR) and the Grammar of Graphics with ggplot2.
• Day 5: Probability & Simulations (Normal distribution, Z-scores, and pnorm/qnorm).

Week 2: Inferential Statistics & Modeling

• Day 6: Central Limit Theorem (CLT) simulations and Sampling Theory.
• Day 7: Hypothesis Testing: Single, Independent, and Paired T-tests.
• Day 8: Categorical Analysis (Chi-square) and Variance Analysis (ANOVA with Tukey Post-hoc).
• Day 9: Correlation (Pearson/Spearman) and Linear Regression modeling.

Capstone Project

The final day focuses on an end-to-end biological analysis:

1. Data Cleaning: Factorizing treatments and handling missing values.
2. Comparative Analysis: Running ANOVA to determine treatment efficacy.
3. Predictive Modeling: Building multiple linear regression models to predict plant weight based on height and leaf count.

Skills Demonstrated

• Statistical Tests: t-test, ANOVA, Chi-square, Tukey HSD.
• Modeling: Linear Regression (lm).
• Visualization: ggplot2 (Boxplots, Histograms, Scatter plots).
• Simulations: replicate(), rnorm(), rchisq().