It depends on what you are trying to describe.
These are common words, but in the areas of algebra and statistics, they are terms which need clear definitions.
A variable is represented by a symbol with a value that varies. (see related link)
Attribute data is data which shows the absence or presence of a characteristic. In quality control, a piece is either acceptable or unacceptable. A car is either new or used.
Attribute data can be represented by a binomial variable. Frequently, 0 is off/ no/ absent and 1 is on/yes/ present.
A second definition for "attribute" (not attribute data) when used with a particular object. In this case an attribute can be considered a certain quality or characteristic, like the color of a car.
I have included a related link.
A normal attribute is an attribute present in a schema and which has to be entered while entering a tuple.A derived Attribute is one which can be inferred(derived) from another normal attribute and it need not be a part of a schema.For e.g.-> In a schema, Date-of-Birth is a normal attribute.While Age is a derived attribute which can be derived from the Date-of-Birth
A line graph is most useful for representing how one variable influences another variable.
A Boolean variable is a variable from Boolean algebra having one of only two values.
linear equation in one variable
Basically the fact that some variable is squared - or that one variable is multiplied with another variable.
variable
You can assign a JavaScript variable to the attribute of an object using the assignment operator (=) Here's a quick example. var imageUrl = "image.png"; var img = new Image(); img.src = imageUrl; The above code would set the source attribute of the object named img as the contents of the variable imageUrl.
The main difference in taking the samples is that for a variable sample, measurements of a characteristic of interest are taken, and for an attribute sample, one counts the number of units having or not having specific properties (mostly good/bad or number of flaws). Generally, attribute samples are much larger than variable samples and to be useful, need to be very large, when the proportion of bad units (or flaws) is very small.
An attribute is a class member variable while a behaviour is a class member method.
An attribute describes something. A variable is something that can take on many values. An example in statistics for an attribute could be for a set of data the diameter. The attribute of the data could be the mean is 5 and standard deviation is 1/2. This describes the data. An example of a variable in statistics for the same set of data above is the diameter reading itself. The diameter will vary and is measured for each member of the population or sample, and may be 4.9, 5.1, 4.95, 5.05, etc. The value can vary on each part.
Qualitative 100% its that and not quantitative The variable is qualitative because it is an attribute characteristic.
Potential to have more than one value for an attribute
transitive dependency
This is an attribute that is most likely to show itself. You may have the recessive attribute, but the dominant one takes over.
A Test Variable(s): The variable whose mean will be compared to the hypothesized population mean (i.e., Test Value). You may run multiple One Sample t Tests simultaneously by selecting more than one test variable. Each variable will be compared to the same Test Value. In simple terms, a variable represents a measurable attribute that changes or varies across the experiment whether comparing results between multiple groups, multiple people or even when using a single person in an experiment conducted over time.
Transitive Dependency
There are two types of variables. The first one is called the experimental variable. It is what you are compare everything to or the normal thing. For instance, what plant grows better the one with sunlight or the one without. The one with sunlight would be the experimental variable. The second type of variable is the dependent variable, which is the data you are collecting. Relating back to the plant experiment, how well the plant grows would be the dependent variable.