It also makes sense to think about it in numerical form. Most data fall into one of two groups. Difference between numerical and categorical variables. Phone numbers social security numbers and zip codes are examples of numerical variables. Reference was found in mcelreath. Numerical data examples which are usually expressed in numbers includes.
We gave examples of both categorical variables and the numerical variables. Age height and weight are examples of numerical data. The data contained in data howell1 are partial census data for the dobe area kung san compiled from interviews conducted by nancy howell in the late 1960s. In our medical example age is an example of a quantitative variable because it can take on multiple numerical values. The diabetes 130us dataset from the uci repository also has genderageweight not height for some diabetes patients in the us not random in the sense they are all diabetic but may be useful depending on what you are trying to do. That is a person can be 18 years old or 80 years old.
The tool that provides useful information about a data set by breaking it down into subpopulations is. Age height data concerning body measurements from 507 adults retrieved from bodydattxt for more information see bodytxt. 500 person gender height weight body mass index height and weight random generated body mass index calculated. All nominal data may be treated as ordinal data. Weight and height are also examples of quantitative variables. Okay this is a very specific dataset for the kung san people but it has height weight sex and age fields.
These data have meaning as a measurement such as a persons height weight iq or blood pressure. I think youll want the demographic data for the measurements you mention. We are constrained when measuring weight height area distance and time by our technology but in general they can take on any value. These numerical examples either in countable numbers as in discrete data or measurement form like continuous data call all be labelled as an example of numerical data. So these were the types of data. Census data temperature age mark grading annual income time height iq cgpa etc.
Or theyre a count such as the number of stock shares a person owns how many teeth a dog has or how many pages you can read of your favorite book before you fall asleep. In this example we will use the variables of age in years and height in centimeters.