Continuous data, unlike Discrete data, is not countable, and is infinite. They can be any value within a range. Continuous data is measured and can be divided infinitely.

Continuous Data

Discrete data, unlike Continuous data, is finite and countable. These can only take on certain values. Discrete data is counted and cannot be divided. 

Discrete Data

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Quantitative data is referred to as Continuous or Discrete data because it comprised of numerical data. This data is commonly stored in data types of Integer, Float, and Double. When using Quantitative data for Statistical learning it is often used in regression problems to predict a numerical output such as percent increase or decrease. This data is a an objective measurement.

Quantitative Data 

Often times, statistical methods rely on measuring the distance between quantitative data points. However, there are various ways to measure this distance. These include:

1. Euclidean distance 
2. Manhattan distance
3. Hamming Distance

Measuring Distance Between Quantitative Data Points

Here is a video on the differences between discrete and continuous data.

[MooMoo Math and Science]. (2017, March 24). Continuous vs Discrete Data [Video File]. Retrieved from [www.youtube.com/watch?v=g30cI4Kbhb4](https://www.youtube.com/watch?v=g30cI4Kbhb4) 

Continuous vs Discrete Data (Reference)

The case of quantitative analysis of quantitative data applies when we use numeric data to analyze the data statistically and mathematically.  

Quantitative Analysis of Quantitative Data

The case of quantitative analysis of qualitative data applies when we use qualitative data to analyze the data statistically and mathematically.  


Quantitative Analysis of Qualitative Data

The case of qualitative analysis of quantitative data applies when we use quantitative data to interpret (qualitatively analyze) the data.  It can involve searching for patterns in the data to understand the "meaning."  

Qualitative Analysis of Quantitative Data

If substance A leads to biological reaction B, then in general (but not 100%), a larger dose of A leads to the stronger reaction B. 


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