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AI detection technologies are swiftly becoming the guardians of authenticity in academic writing, ensuring that the use of AI in circumventing academic rigor is not only discouraged but actively detected.

Understanding how to create conditional histograms in R.

Crafting Custom Scatter Plots in R: A Guide for Students

Let's explore what scatter plots are, why they are indispensable in research, and how to use them in R to reveal the underlying stories in your data.

One common challenge in visualizing data is the overlapping of data points, which can obscure patterns and relationships. Jittering can help! See how it works in R.

Here is a foundational understanding of heteroskedasticity, with practical steps to identify and correct it in your analyses, using R.

Here's how to use Python to identify heteroskedasticity in an OLS regression and run an RSE regression instead.

Python and Jupyter are two tools that, when mastered, can significantly enhance your academic and research capabilities.

RStudio is a pivotal tool, especially for those involved in data science, statistics, or any field that relies heavily on data analysis. However, while RStudio is excellent for data analysis, visualization, and statistical computing, it's crucial to address the elephant in the room: The inherent complexity and steep learning curve associated with R programming.

When it comes to non-STEM graduate students, the proposition of uding Python for statistical analysis warrants a closer examination.

Non-stationarity, where the statistical properties of a time series change over time, can significantly impede the reliability and validity of any inferential statistics derived from the data. This is where differencing, a deceptively simple yet profoundly impactful technique, comes into play. Differencing transforms a non-stationary time series into a stationary one, enabling the application of various time series forecasting and analysis methods.

Log transformation, a cornerstone in the preprocessing of time series data, involves applying the logarithm function to each observation in the time series. Learn how to apply a log transformation to time-series data in R.

This blog post provides an in-depth look at time-series decomposition in R, offering graduate students a comprehensive guide to mastering this essential technique.

Navigating Time-Series Forecasting with ARIMA in R

While the significance of  p values in denoting statistical significance is well-known, the importance of effect sizes, specifically odds ratios in logistic regression, cannot be overstated. This blog post discusses effect sizes through practical logistic regression examples using R, highlighting scenarios prevalent in graduate theses, advanced undergraduate work, and beyond.

Even otherwise conscientious and skilled graduate students can forget the need to provide effect sizes as a complement to statistical significance. Of course, everyone knows that you need a p value to accompany your statistical results, outside of descriptive statistics, but what about effect size? Learn about effect sizes through an independent samples t-test in R.

In this blog, we look at ANOVA effect sizes using practical R examples and scenarios commonly encountered in graduate theses, advanced undergraduate projects, and PhD research.

In this blog, we’ll walk you through what effect sizes are through some practical regression examples using R. 

In this blog, we’ll explore why cluster analysis in R could be an integral part of the statistical arsenal for any graduate student looking to make data-driven discoveries.

Here's an example, in R, of how principal component analysis, or PCA, can offer unexpected and useful insights into data.