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PROJECT
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.
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.
Skills
Data Analysis
Data Cleaning
Data Statistics
Prerequisites
Basic understanding of the R language
Basic knowledge of statistical analysis
Technologies
Rlang
ggplot2
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
1
Initial Setup
Task 0: Get Started
Task 1: Load the Dataset
Task 2: Obtain the Data Summary
Task 3: Impute the Missing Values
2
Exploratory Data Analysis
Task 4: Visualize the Correlation Plot
Task 5: Create a Boxplot
Task 6: Create a Histogram
3
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
4
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
Congratulations!