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Biostatistics in Medical Study with R

In this project, we’ll cover fundamental to advanced biostatistics in R-hypothesis testing, regression, ANOVA, and categorical data analysis, focusing on practical research questions for medical data analysis.

Biostatistics in Medical Study with R

You will learn to:

Impute the missing values.

Perform t-test and ANOVA for mean comparison.

Perform logistic regression for binary response variables.

Create data visualizations using ggplot2.

Perform the chi-square test for contingency tables.


Data Analysis

Data Cleaning

Data Statistics


Basic understanding of the R language

Basic knowledge of statistical analysis




Project Description

Medical data is often complex and multifaceted, encompassing various information such as patient demographics, clinical measurements, medical histories, diagnostic tests, treatment plans, and outcomes. It often presents challenges, such as missing values, unusual distributions, and non-unified data types—all common issues in medical data.

R is a powerful statistical programming language that offers a comprehensive range of statistical models, tests, and algorithms. With its extensive range of statistical techniques, R enables users to perform tasks such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more. Additionally, R offers robust graphical capabilities. It also provides a vast collection of packages specifically designed for medical and healthcare research, allowing researchers to perform complex analyses efficiently.

This project places a strong emphasis on practical learning and fosters a spirit of exploration as we navigate real-world data and analysis challenges. Throughout the project, we’ll explore various subjects, from fundamental to advanced statistics. Topics covered will include hypothesis testing, regression, and categorical data analysis in medical data.

Project Tasks


Initial Setup

Task 0: Get Started

Task 1: Load the Dataset

Task 2: Obtain the Data Summary

Task 3: Impute the Missing Values


Exploratory Data Analysis

Task 4: Visualize the Correlation Plot

Task 5: Create a Boxplot

Task 6: Create a Histogram


T-test and Linear Regression

Task 7: Conduct a T-test

Task 8: Perform the Nonparametric Wilcoxon Test

Task 9: Perform the ANOVA Test

Task 10: Perform the Nonparametric ANOVA Test

Task 11: Perform Linear Regression


Categorical Data Analysis

Task 12: Create a Frequency Table

Task 13: Perform Logistic Regression

Task 14: Create the Logistic Regression Graph

Task 15: Calculate the Odds Ratio