Variables in Algebra

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Algebraic Expression Definition

An algebraic expression has a combination of one or more constants, variables, and co-efficient. It also consists of fundamental operations like addition, division, subtraction as well as multiplication. Every single term constitutes the basis of algebra. While studying the topic algebra you will find an alphabet that is used to represent an unknown number. This alphabet represents a value. The variable quantities can also change with the given mathematical problem.

What is Meant by Variable?

A variable is a quantity that can change with the context of a particular mathematical problem or with the context of an experiment. Generally, we use a single letter or alphabet to represent a variable. The alphabets like x, y, a, b, c, m, n, and z are the most commonly used symbols to represent a variable. 

But sometimes, one chooses to use a letter that reminds one of the quantities that it represents like the alphabet  ‘t’ is used to portray time, ‘v’ for voltage, and also ‘b’ for bacteria. The alphabet ‘e’ and ‘i’ have a very special value in algebra and therefore they are not used as variables. The alphabet ‘o’ is also not used as a variable because one might mistake for 0 (zero).

For example,

x + 7 = 17

Here the variable ‘x’ is unknown to us and we have to find its value. The value of the variable x can easily be found by working out the problem.

Here the value of ‘x’ will be 10 that means x=10.

The term variable is also used in topics like statistics. The variables used in statistics are referred to as the data items. These variables represent numbers or characteristics which could be measured. The numbers or characteristics can be age, sex, income, expenditure, etc.

Types of Variables

A variable is a measurable characteristic that may vary from group to group, person to person, or even within one person over a time. There are various types of variables as follows:

1. Dependent Variable

The dependent variables show the effect of manipulating or introducing the independent variables. 

For example, if the independent variable is the use or the non-use of a particular new language teaching procedure then the dependent variable may be the score of the students on a test of the content taught using that procedure. 

In other words, we can say that the variation in the dependent variable depends on the variation of the independent variable.

2. Independent Variable

The independent variables are those that the researcher has control over. This ‘control’ might involve manipulating the existing variables such as modifying the existing methods of the instruction. 

This ‘control’ may also involve introducing new variables, for example, adopting a new method for some sections of the class in research settings. Whatever the case might be, the researchers always expect that the independent variables will have some effect on the dependent variables.  

3. Quantitative Variable 

The numerical variables are called quantitative variables. They always represent a measurable quantity. 

For example, when one speaks about the population of a city or a country the one is talking about the number of people residing in a city or in a country which is the measurable attribute of the city or the country. 

Therefore, in this case, the population will be the quantitative variable. In the algebraic equations, the quantitative variables are represented by the symbols x, y, or z.

4. Categorical Variables 

The variables which take on values that are names or labels are considered as the categorical variables. Categorical variables are also called a qualitative variable. 

For example, the color of a ball can be red, or green, or even blue. The breed of a dog can be a collie, or a shepherd, or a terrier. 

These are categorical or qualitative variables that have no natural order, unlike quantitative variables which have a value and also can be added, subtracted, divided, or multiplied.

FAQ (Frequently Asked Questions)

1. Write about the Correlation Research. What is a Confounding Variable?

Ans: When one does correlation research then the terms ‘dependent’ and ‘independent’ do not apply. This is because one is not trying to establish a cause and effect of the relationship.  However, there may be some cases where one variable might precede the other. 

For example, rainfall leads to mud, rather than the mud leading to the rainfall. In this particular case, you might call the preceding variable that is the rainfall to be the predictor variable and the following variable that is the mud to be the outcome variable.

A confounding variable is a third variable in a study that examines a potential cause and effect relationship. A confounding variable is also called a confounder or the confounding factor. 

A confounding variable is related to both the supposed cause as well as the supposed effect of the study. It may be very difficult to separate the true effect of the independent variable from the effect of the confounding variable. In the research design, it is very important to identify the potential confounding variables and plan accordingly on how to reduce their impact.

2. What are the Other Common Types of Variables?

Ans:  Apart from dependent and independent variables there are other types of variables that help one with interpreting the results.

a. Confounding Variables- Confounding variables are the variables that hide the true effect of the other variable in the experiment. This cannot happen when the other variable is closely related to the variable you are interested in but also you have not controlled it in your experiment.

b. Latent Variable- Latent variables are those variables that cannot be directly measured, but you can represent them via a proxy.

For example- The salt tolerance in the plants is not possible to measure directly.  But the salt tolerance in the plants can be inferred from the measurements of plant health in our salt addition experiment.

c. Composite Variables- Composite Variables are those that are made by combining multiple variables in an experiment. These variables are created exactly when you are analyzing the data, not when you are measuring it.