Hi there! I’m Kelvin Kiprono.

Welcome to my blog, where I explore the power of R programming for statistical analysis. As an Applied Statistics professional, I’ve discovered that R is an invaluable tool for transforming data into meaningful insights. Whether you're an experienced analyst, a student, or a curious beginner, this space is designed to make statistical concepts more approachable and practical through the lens of R. Here, I’ll share tutorials, real-world examples, and tips to enhance your data analysis skills and streamline your workflow. Join me as we navigate the world of data and uncover how R can simplify and elevate your analytical journey.Thanks for visiting! Feel free to reach out or connect—let’s learn and explore together.

What I’m up to lately

Recently, I’ve been engaged in working with disaggregated data from surveys, honing my expertise in data analysis and interpretation. This work involves breaking down complex data sets to uncover detailed insights that can inform decision-making. In addition to my current projects, I am continuously enhancing my statistical analysis skills and exploring how these insights can contribute to impactful outcomes across various sectors.
Kelvin Kiprono Blog

Hypothesis Testing

Hypothesis testing is a statistical method used to determine if there is enough evidence to reject a null hypothesis (H0) in favor of an alternative hypothesis (𝐻𝑎 ). By analyzing sample data, the process involves setting a significance level, calculating a test statistic, and comparing it to a critical value or P-value to make data-driven decisions. Common tests include t-tests, z-tests, and chi-square tests, applied in diverse fields to validate assumptions and drive conclusions. Read more

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Kelvin Kiprono Projects

Visual Exploration of the Diamonds Dataset Using ggplot2 in R

This analysis explores the popular diamonds dataset using the versatile ggplot2 package in R. Through a variety of visualizations—such as histograms, scatter plots, boxplots, and heatmaps—it reveals insights into diamond attributes like price, carat, cut, clarity, and their relationships. The study provides an intuitive understanding of the data, highlighting key trends and patterns, such as price distribution, the influence of carat size on cost, and variations across quality grades. Read more

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KELVIN KIPRONO

STATISTICIAN AND DATA ANALYST
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