By statistical Data we mean, the piece of information collected for descriptive or inferential statistical analysis of the data. Data is everywhere. Therefore, everything that has past and/ or features is called statistical data.

**One can find the statistical data**

- Any financial/ economics data
- Transactional data (from stores, or banks)
- The survey, or census (of unemployment, houses, population, roads, etc)
- Medical history
- Price of product
- Production, and yields of a crop
- My history, your history is also statistical data

**Data**

Data is the plural of datum — it is a piece of information. The value of the variable (understudy) associated with one element of a population or sample is called a datum (or data in a singular sense or data point). For example, Mr. Asif entered college at the age of 18 years, his hair is black, has a height of 5 feet 7 inches, and he weighs about 140 pounds. The set of values collected for the variable from each of the elements belonging to the sample is called data (or data in a plural sense). For example, a set of 25 weights was collected from the 25 students.

**Types of Data**

The data can be classified into two general categories: quantitative data and qualitative data. The quantitative data can further be classified as numerical data that can be either discrete or continuous. The qualitative data can be further subdivided into nominal, ordinal, and binary data.

** Qualitative data** represent information that can be classified by some quality, characteristics, or criterion—for example, the color of a car, religion, blood type, and marital status.

When the characteristic being studied is non-numeric it is called a qualitative variable or an attribute. A qualitative variable is also known as a categorical variable. A **categorical variable** is not comparable to taking numerical measurements. Observations falling in each category (group, class) can only be counted for examples, gender (either male or female), general knowledge (poor, moderate, or good), religious affiliation, type of automobile owned, city of birth, eye color (red, green, blue, etc), etc. **Qualitative variables **are often summarized in charts graphs etc. Other examples are what percent of the total number of cars sold last month were Suzuki, what percent of the population has blue eyes?

** Quantitative data **result from a process that quantifies, such as how much or how many. These quantities are measured on a numerical scale. For example, weight, height, length, and volume.

When the variables studied can be reported numerically, the variable is called a quantitative variable. e.g. the age of the company president, the life of an automobile battery, the number of children in a family, etc. Quantitative variables are either discrete or continuous.

Note that some data can be classified as either qualitative or quantitative, depending on how it is used. If a numerical is used as a label for identification, then it is qualitative; otherwise, it is quantitative. For example, if a serial number on a car is used to identify the number of cars manufactured up to that point then it is a quantitative measure. However, if this number is used only for identification purposes then it is qualitative data.

**Binary Data**

The binary data has only two possible values/states; such as, defected or non-defective, yes or no, and true or false, etc. If both of the values are equally important then it is binary symmetric data (for example, gender). However, if both of the values are not equally important then it can be called binary asymmetric data (for example, result: pass or fail, cancer detected: yes or no).

For **quantitative data**, a count will always give discrete data, for example, the number of leaves on a tree. On the other hand, a measure of a quantity will usually be continuous, for example, weigh 160 pounds, to the nearest pound. This weight could be any value in the interval say 159.5 to 160.5.

The following are some examples of Qualitative Data. Note that the outcomes of all examples of **Qualitative Variables** are non-numeric.

- The type of payment (cheque, cash, or credit) used by customers in a store
- The color of your new cell phone
- Your eyes color
- The make of the types on your car
- The obtained exam grade

The following are some examples of Quantitative Data. Note that the outcomes of all examples of **Quantitative Variables** are numeric.

- The age of the customer in a stock
- The length of telephone calls recorded at a switchboard
- The cost of your new refrigerator
- The weight of your watch
- The air pressure in a tire
- the weight of a shipment of tomatoes
- The duration of a flight from place A to B
- The grade point average

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xyzGreat blog post! I found the section on descriptive statistics particularly interesting, as I’m currently working on a project that involves analyzing customer data. It was helpful to see real-life examples of how statistical analysis can be applied in practice. Keep up the good work!