What is Selection Bias and How Does It Affect Data Analysis? In data analysis and machine learning, one of the most significant challenges is ensuring that the data used for training models is representative of the population or real-world scenarios the model will encounter in the future. If the data is skewed, incomplete, or unrepresentative,…
What is a Statistical Interaction? In research and statistical modeling, understanding how independent variables affect a dependent variable is key to drawing meaningful conclusions. However, the effect of one independent variable on the dependent variable can be more complex when other independent variables are involved. This complexity is referred to as statistical interaction. A statistical…
Understanding the Assumptions of Linear Regression Linear regression is a powerful statistical technique used for modeling the relationship between a dependent variable and one or more independent variables. While the model can be highly effective for making predictions, its validity and accuracy depend on certain assumptions being met. These assumptions ensure that the model fits…
What is Linear Regression? Linear regression is one of the simplest and most widely used statistical methods in predictive modeling. It is a technique used to understand the relationship between a dependent variable (also called the target or output) and one or more independent variables (also known as predictors or features). The goal is to…
What are Type I and Type II Errors? In statistical hypothesis testing, researchers use tests to make inferences about a population based on sample data. However, as with any process of decision-making, mistakes can occur. Two common types of errors in hypothesis testing are Type I and Type II errors. Understanding these errors is crucial…
What is Sampling? Data sampling is a critical statistical analysis technique used in various fields to efficiently analyze and interpret large data sets. It involves selecting a representative subset of data points from a larger population or dataset. The goal is to identify patterns, trends, and insights that reflect the characteristics of the entire…
Introduction The Central Limit Theorem (CLT) is one of the cornerstones of statistical theory and a powerful concept in probability. This theorem explains why the normal distribution appears so often in real-world data, even when the original data doesn’t follow a normal distribution. Understanding CLT is crucial for statisticians, data analysts, and anyone working with…
What is a large language model (LLM)? Large Language Models are machine learning models that employ Artificial Neural Networks and large data repositories to power Natural Language Processing (NLP) applications. An LLM serves as a type of AI model designed to be able to grasp, create, and manipulate natural language. These models rely on deep…
The introduction of DeepSeek has triggered a paradigm shift in the AI landscape. Its ability to outperform existing technologies at a fraction of the cost has sent ripples throughout the tech industry.
How a Large Language Model (LLM) predicts the next word, including all the mathematical operations involved at each step, with the appropriate vector and tensor manipulations.
