Data Analysis and Statistics Learning Materials

This repository contains a comprehensive collection of Excel and R statistical analysis files for learning statistics and data processing techniques.

📁 File Overview

Excel Files

Excel-01-Exercises1.xlsx

• Basic Excel data handling exercises
• Includes fundamental operations such as data cleaning, sorting, and filtering
• Contains simple statistical calculations (sum, average, etc.)

Excel-02-results.xlsx

• Summary file of analysis results
• Outputs from preliminary data analysis
• Includes charts, pivot tables, and other visualizations for data presentation

Excel-03-Exercise.xlsx

• Advanced Excel practice exercises
• Demonstrates complex function applications
• Features conditional statistics and advanced data analysis techniques

STATS20x.xlsx

• Statistical analysis reference file
• Contains:
  • Descriptive statistics (mean, variance, standard deviation, etc.)
  • Data distribution analysis
  • Hypothesis testing and regression analysis

Data Files

camplake.csv

• Raw data file in CSV format
• Used for data import and processing
• Likely contains environmental monitoring data (e.g., water quality, climate, or geographic information)
• Primary data source for statistical analysis

R Analysis Files

EXERCISE3_R.Rmd & EXERCISE3_R.html

• Purpose: Comprehensive linear regression analysis and diagnostics
• Key Analysis Performed:
  • Linear Model Fitting: Fits a linear regression model with Length as the response variable and Age and Scale.Radius as predictors
  • Residual Analysis:
    • Residuals vs Age scatter plot
    • Residuals vs Scale.Radius scatter plot
    • Examines the relationship between predictor variables and model residuals
  • Model Fitting Visualization:
    • Age vs Length with fitted regression line
    • Scale.Radius vs Length with fitted regression line
    • Compares actual values with predicted values
  • Normality Diagnostics:
    • Normal Q-Q plot for predictions (tests if predicted values follow normal distribution)
    • Normal Q-Q plot for residuals (tests if residuals follow normal distribution)
  • Comprehensive Model Diagnostics:
    • Four-panel diagnostic plot including:
      • Residuals vs Fitted values
      • Normal Q-Q plot
      • Scale-location plot
      • Residuals vs Leverage plot
  • Additional Graphics: Demonstrates sine curve visualization as a mathematical plotting example

Author: cden903
Date: 2026-03-17

📊 Overall Purpose

This is a comprehensive learning curriculum progressing from foundational to advanced data analysis skills, covering:

• Excel proficiency and techniques
• R statistical programming and linear regression analysis
• Data processing and visualization
• Model diagnostics and validation
• Real-world data interpretation

🎯 Learning Path

1. Start with Excel-01-Exercises1.xlsx for basic Excel skills
2. Progress to Excel-03-Exercise.xlsx for advanced techniques
3. Learn statistical concepts with STATS20x.xlsx
4. Apply R programming with EXERCISE3_R.Rmd for regression analysis and diagnostics
5. Analyze real data using camplake.csv dataset

📝 Statistical Methods Covered

• Descriptive Statistics: Summary measures of central tendency and dispersion
• Linear Regression: Modeling relationships between variables
• Residual Diagnostics: Assessing model assumptions and fit quality
• Normality Testing: Validating distribution assumptions using Q-Q plots
• Data Visualization: Creating effective plots for data exploration and presentation

---

Last Updated: March 2026