Some examples of a quantitative variable are height, weight, number of cars in a household. …

Just because you have a number, doesn’t necessarily make it quantitative. For example, zip codes, phone numbers and bank-accounts are numeric, but it doesn’t make much sense to find the average phone number or median zip-code. These are examples of numbers applied to categorical data.

Qualitative data are generally described by worlds or letters. are always numbers. Quantitative data are the result of counting or measuring. (Ex money, weight, pulse rate (etcetera) Quantitative data may be either discrete or continuous.

For example, the grade you receive in your school exam (A, B, C, D, or E) is an example of discrete data because your grade can only take on one of these 5 possible values and nothing else. Discrete data can be further sub-divided into three categories: binary, nominal and ordinal.

Sample data—measurements made on individuals in a sample—need not be numerical. In the case of automobiles, what is recorded about each car could be its color, its make, its body type, and so on. Such data are categorical or qualitative, as opposed to numerical or quantitative data such as value or age.

There are two types of quantitative variables: discrete and continuous.

Types of Quantitative Variables Let’s consider different features of variables used in quantitative research studies. Here we explore quantitative variables as being categorical, ordinal, or interval in nature. These features have implications for both measurement and data analysis.

(*) Household size, monthly income and number of computers — these are quantitative variables. Income is a continuous variable. Household size and number of computers are discrete variables.

Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level.

A count variable is discrete because it consists of non-negative integers. Even so, there is not one specific probability distribution that fits all count data sets.

2) Continuous Variables: These are sometimes called quantitative or measurement variables; they can take on any value within a range of plausible values. For example, total serum cholesterol level, height, weight and systolic blood pressure are examples of continuous variables.

Count data models have a dependent variable that is counts (0, 1, 2, 3, and so on). Most of the data are concentrated on a few small discrete values. Examples include: the number of children a couple has, the number of doctors visits per year a person makes, and the number of trips per month that a person takes.

Types of Counts. Identifying the Counting Scope and the Application Boundary. Data Functions (Internal Logical Files and External Interface Files) Transactional Functions (External Inputs, External Outputs, and External Inquiries) Source.

Under this system, the higher the number, the finer the yarn. In the United States cotton counts between 1 and 20 are referred to as coarse counts.

1) Teaching them the sub skills of counting is very important like itemising, ordering and naming the objects. 2) Recitation of numbers and knowing the number sequence and number names is also important before teaching them counting. 5) Giving them a practical knowledge of small quantities.

Stats: Counting Techniques

• Arithmetic. Every integer greater than one is either prime or can be expressed as an unique product of prime numbers.
• Algebra.
• Linear Programming.
• Permutations using all the objects.
• Permutations of some of the objects.
• Distinguishable Permutations.
• Pascal’s Triangle.
• Symmetry.

The purpose of counting is to assign a numeric value to a group of objects. What makes counting possible? A simple fact that such a value exists. However we go about counting the number of eggs in a basket the result is always the same.

We could easily count the number of elements in the sample space. If there are a large number of elements in the sample space we can use counting techniques such as permutations or combinations to count them.

Counting methods – usually referred to in GMAT materials as “combinations and permutations” – are generally the lowest-yield math area on the test. In some cases, we can count possibilities simply by enumerating them exhaustively – listing them out. That method is simple and works well on many GMAT questions.

A combination is a grouping together of separate things. Combination is the act of combining, which comes from the Latin for “joining together two by two,” although it’s not necessary that you combine things in pairs.