RStudio Statistical Analysis Project

Project Overview

This project involves conducting deep statistical analysis using RStudio on traffic and cake datasets. The study explores relationships between variables using regression models, evaluates model significance with statistical tests, and visualizes results using various plots. The project also includes a comprehensive study report created using RMarkdown.

Datasets Used

• Traffic Dataset: Used to analyze relationships between different predictors and the response variable spi.
• Cake Dataset: Used for a two-way ANOVA study to examine the effects of Recipe and Temp on Angle.

Key Analysis Steps

Traffic Dataset Analysis

1. Exploratory Data Analysis (EDA)

   • Correlation analysis with correlation matrices and scatterplot matrices.
   • Identification of relationships between predictors and the response variable spi.

2. Regression Model Fitting

   • Full multiple linear regression model lm(spi ~ ., data = traffic).
   • 95% confidence interval estimation for weather impact.
   • F-test for overall regression significance.

3. Model Diagnostics

   • Residual plots and normality tests.
   • Finding the best regression model through backward elimination.
   • Comparing adjusted R² values to determine optimal model selection.

Cake Dataset Analysis

1. Balanced Study Checking

   • Ensuring equal number of replicates across factor levels.

2. Preliminary Data Visualization

   • Boxplots and interaction plots to analyze factor relationships.

3. Two-Way ANOVA Analysis

   • Interaction model fitting to assess significance.
   • Model validation through diagnostic plots.

4. Main Effects Analysis

   • Hypothesis testing for Recipe and Temp significance.
   • Final model selection without interaction term based on p-values.

Tools & Libraries Used

• RStudio for data analysis and statistical modeling.
• Base R functions for data manipulation and visualization.
• ggplot2 & Base R plotting functions for visual representation.
• RMarkdown for creating a reproducible statistical report.

Results & Findings

• A significant relationship was found between spi and predictors weather, transport, and road in the traffic dataset.
• The final regression model explained 73.8% of variation in spi.
• The cake dataset analysis found that Recipe and Temp had significant main effects on Angle, but their interaction was not significant.

How to Run the Project

1. Ensure R and RStudio are installed.
2. Place datasets (traffic.csv, cake.csv) inside the data/ folder.
3. Open report.Rmd in RStudio.
4. Click Knit to generate the PDF report.