# What is an Observation in Statistics?

In statistics, an observation is simply one occurrence of something you’re measuring. For example, suppose you’re measuring the weight of a certain species of turtle. Each…

# How to Normalize Data Between 0 and 100

To normalize the values in a dataset to be between 0 and 100, you can use the following formula: zi = (xi – min(x)) / (max(x)…

# How to Calculate a Pearson Correlation Coefficient by Hand

A Pearson Correlation Coefficient measures the linear association between two variables. It always takes on a value between -1 and 1 where: -1 indicates a…

# Sample Mean vs. Population Mean: What’s the Difference?

Often in statistics we’re interested in answering questions like: What is the mean household income in a certain city? What is the mean weight of…

# XGBoost in R: A Step-by-Step Example

Boosting is a technique in machine learning that has been shown to produce models with high predictive accuracy. One of the most common ways to…

# How to Drop the Index Column in Pandas (With Examples)

Occasionally you may want to drop the index column of a pandas DataFrame in Python. Since pandas DataFrames and Series always have an index, you…

# Population vs. Sample: What’s the Difference?

Often in statistics we’re interested in collecting data so that we can answer some research question. For example, we might want to answer the following…

# A Simple Introduction to Boosting in Machine Learning

Most supervised machine learning algorithms are based on using a single predictive model like linear regression, logistic regression, ridge regression, etc.  Methods like bagging and…

# Normal Distribution vs. t-Distribution: What’s the Difference?

The normal distribution is the most commonly used distribution in all of statistics and is known for being symmetrical and bell-shaped. A closely related distribution…

# How to Manually Enter Raw Data in R

R is one of the most popular programming languages for working with data. But before we can work with data, we have to actually get…