Engineering Statistics Interactive Dashboards

This repository contains a collection of interactive analytical dashboards developed for an Engineering Statistics academic project. Built primarily with R and Shiny , these applications transform theoretical statistical concepts and quality control methods into user-friendly, dynamic web interfaces.

📊 Project Highlights

• Hypothesis Testing & ANOVA: In-depth analysis of datasets (e.g., lifestyle, manufacturing sensors) using statistical tests to draw actionable conclusions.
• Quality Control Tools: Interactive visualization of Operating Characteristic (OC) curves for acceptance sampling plans.
• Normality & Simulation: Dynamic generation of normal distributions, histogram fitting, and Box-Cox transformations using QQ plots.
• Interactive UI: Clean and responsive interfaces built with bslib, DT, and ggplot2 for real-time data exploration.

📸 Snapshots

1. Operating Characteristic (OC) Curve Analysis

Interactive dashboard for evaluating acceptance sampling plans based on sample size and acceptance numbers.

2. SECOM Manufacturing Data Analysis

Analyzing sensor independence and evaluating pass/fail composite indexes using real-world manufacturing datasets.

📂 Repository Structure

The project is divided into two distinct phases, each addressing specific statistical scenarios:

Phase 1

• 1.R: Comprehensive Lifestyle Analysis (Data filtering, ANOVA, Chi-Square).
• 2.R: Quality Control - OC Curve visualizer and probability calculator.
• 3.R: Simulation & Normality Analysis (Dynamic rnorm simulation, QQ plots, Box-Cox).
• 4.R: Statistical Analysis of Polymer vs. Synthetic Oils (Student's and Welch's T-Tests).
• 5.R: SECOM Dataset Analysis (Composite indexing, variable independence).

Phase 2

• Contains advanced statistical applications and Python notebooks (1.R, 2.R, etc.) building upon Phase 1 concepts.

🚀 How to Run Locally

To run these Shiny applications on your local machine, ensure you have R installed.

1. Clone the repository:

   git clone https://github.com/AliMajedi-83/Statistics_Project.git

2. Install required R packages: Open your R console or RStudio and run:

   R
   install.packages(c("shiny", "tidyverse", "DT", "bslib", "corrplot", "readxl", "MASS"))

3. Run an application: You can launch any of the scripts directly. For example:

   cd Phase_1
   Rscript 2.R

🛠️ Technologies & Tools

• Languages: R
• Frameworks: R Shiny
• Libraries: tidyverse, ggplot2, bslib, DT, corrplot

Developed for the Engineering Statistics coursework