*Statistics 101: Understanding the different type of variables.*

As we enter the latter part of the year 2020, it is safe to say that companies utilize data to assist in making business decisions. For example doing **exploratory data analysis (EDA) **to calculate statistics of where the business stands today, it may include a **simple Linear Regression model** to predict product prices in 2021. Perhaps it utilizes neither and instead uses **clustering** to determine relationships between data points. Regardless of how data is utilized, possessing a strong statistics background can only aid in the decision making process as to which approach is taken to best extract, hypothesize, and interpret data.

With that being said let us start with the very basics of statistics: **variables. **Variables can be broken down into two different categories. **Quantitative (Numerical) and Qualitative (Categorical). Quantitative variables **can be further broken down into two subcategories: **Continuous and Discrete.**

**Continuous** quantitative variable can be defined as a numerical value that may fall within a large range to which one may say “well it could be anything.” Yes I know that may not make sense but lets utilize a few examples: **_numerical values such as age, weight, height, BMI are examples of continuous quantitative variables. _**_These are examples of numbers that are always changing and may be within an extremely large range. _You may be asking “Well age does not seem like it could fall within a range, if someone asked me how old I am I could answer with an exact number.” Well is that true? *Remember age is a form of time, in which it is always changing, therefore age is considered a continuous quantitative variable as well.*

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