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Which detail from Heart of Darkness shows the ineffectiveness of the colonizers. All Rights Reserved. The material on this site can not be reproduced, distributed, transmitted, cached or otherwise used, except with prior written permission of Multiply.

School Subjects. Math and Arithmetic. Wiki User When we use money for purchases, it is in discrete units, so many dollars and cents. When we use money in calculations, it is a continuous value, and we have to use rounding to get discrete values.

I must give the person 5 dollars and 40 cents. This is a discrete value.

Related Questions Asked in Motorcycles Are weight of motorcycles discrete data or continuous data? The weight of the motorcycles is discrete and not the continuous data. It uses continuous data.

It can use either discrete or continuous data. Asked in Math and Arithmetic, Statistics What is a continuous and discrete data set? Asked in Science, Statistics What is non-continuous data? Non-continuous data is called discrete data. Discrete variables have numbers that can be counted.

Continuous data is measurable. Discrete data are data which can only take on a finite or countable number of values within a given range. Continuous data are data which can take on any value. It is measured rather than counted. The mass of a given sample of iron is continuous; the number of marbles in a bag is discrete. Asked in School Subjects, Math and Arithmetic, Statistics How do discrete and continuous data relate to quantitative data?

Asked in Math and Arithmetic What is discrte data in maths? In maths there is discrete data and continuous data. Continuous data can be measured to any degree of accuracy, e. Discrete data cannot I have 2 sisters. Discrete data cannot have halves or decimals, whole numbers only. Discrete and continuous.Variable refers to the quantity that changes its value, which can be measured.

## Are Space and Time Discrete or Continuous?

It is of two types, i. The former refers to the one that has a certain number of values, while the latter implies the one that can take any value between a given range. Data can be understood as the quantitative information about a specific characteristic. The characteristic can be qualitative or quantitative, but for the purpose of statistical analysis, the qualitative characteristic is transformed into quantitative one, by providing numerical data of that characteristic.

So, the quantitative characteristic is known as a variable. Here in this article, we are going to talk about the discrete and continuous variable. Basis for Comparison Discrete Variable Continuous Variable Meaning Discrete variable refers to the variable that assumes a finite number of isolated values.

Continuous variable alludes to the a variable which assumes infinite number of different values. Range of specified number Complete Incomplete Values Values are obtained by counting.

Values are obtained by measuring. Classification Non-overlapping Overlapping Assumes Distinct or separate values. Any value between the two values. Represented by Isolated points Connected points.

A discrete variable is a type of statistical variable that can assume only fixed number of distinct values and lacks an inherent order. Also known as a categorical variablebecause it has separate, invisible categories.

However no values can exist in-between two categories, i. So, the number of permitted values that it can suppose is either finite or countably infinite. Hence if you are able to count the set of items, then the variable is said to be discrete. Continuous variable, as the name suggest is a random variable that assumes all the possible values in a continuum. Simply put, it can take any value within the given range.

So, if a variable can take an infinite and uncountable set of values, then the variable is referred as a continuous variable. A continuous variable is one that is defined over an interval of values, meaning that it can suppose any values in between the minimum and maximum value. It can be understood as the function for the interval and for each function, the range for the variable may vary.

The difference between discrete and continuous variable can be drawn clearly on the following grounds:. By and large, both discrete and continuous variable can be qualitative and quantitative. However, these two statistical terms are diametrically opposite to one another in the sense that the discrete variable is the variable with the well-defined number of permitted values whereas a continuous variable is a variable that can contain all the possible values between two numbers.

Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Key Differences Between Discrete and Continuous Variable The difference between discrete and continuous variable can be drawn clearly on the following grounds: The statistical variable that assumes a finite set of data and a countable number of values, then it is called as a discrete variable.

As against this, the quantitative variable which takes on an infinite set of data and a uncountable number of values is known as a continuous variable.

For non-overlapping or otherwise known as mutually inclusive classification, wherein the both the class limit are included, is applicable for the discrete variable. On the contrary, for overlapping or say mutually exclusive classification, wherein the upper class-limit is excluded, is applicable for a continuous variable.Receive emails about upcoming NOVA programs and related content, as well as featured reporting about current events through a science lens.

Split a mile in half, you get half a mile. Can this slicing continue indefinitely, or will you eventually reach a limit: a smallest hatch mark on the universal ruler? The success of some contemporary theories of quantum gravity may hinge on the answer to this question. But the puzzle goes back at least years, to the paradoxes thought up by the Greek philosopher Zeno of Elea, which remained mysterious from the 5th century BC until the early s.

The tortoise gets a head start on the faster-running Achilles. While Achilles pursues the tortoise to cover this additional distance, the tortoise moves yet another bit.

Obviously, in real life, Achilles wins the race. So, Zeno argued, the assumptions underlying the scenario must be wrong. Specifically, Zeno believed that space is not indefinitely divisible but has a smallest possible unit of length.

This allows Achilles to make a final step surpassing the distance to the tortoise, thereby resolving the paradox. After mathematicians understood how to sum an infinite number of progressively smaller steps, they calculated the exact moment Achilles surpasses the tortoise, proving that it does not take forever, even if space is indefinitely divisible. In this case, the infinities were not mistakes but demonstrably a consequence of applying the rules of quantum theory to gravity.

But by positing a smallest unit of length, just like Zeno did, theorists can reduce the infinities to manageable finite numbers. And one way to get a finite length is to chop up space and time into chunks, thereby making it discrete: Zeno would be pleased.

He would also be confused. Think of studying samples with a microscope, for example. Magnify too much, and you encounter a resolution-limit beyond which images remain blurry. And if you zoom into a digital photo, you eventually see single pixels: further zooming will not reveal any more detail.

In both cases there is a limit to resolution, but only in the latter case is it due to discretization. In these examples the limits could be overcome with better imaging technology; they are not fundamental.

But a resolution-limit due to quantum behavior of space-time would be fundamental.

## Is the cost of a loaf of bread discrete or continuous?

It could not be overcome with better technology. So, a resolution-limit seems necessary to avoid the problem with infinities in the development of quantum gravity. But does space-time remain smooth and continuous even on the shortest distance scales, or does it become coarse and grainy? Researchers cannot agree. In string theory, for example, resolution is limited by the extension of the strings roughly speaking, the size of the ball that you could fit the string insidenot because there is anything discrete.

In a competing theory called loop quantum gravity, on the other hand, space and time are broken into discrete blocks, which gives rise to a smallest possible length expressed in units of the Planck length, about 10 metersarea and volume of space-time—the fundamental building blocks of our universe.

Einstein taught us that space and time are joined in one entity: space-time. But some dissidents argue that only space or only time should be discrete. So how can physicists find out whether space-time is discrete or continuous? Directly measuring the discrete structure is impossible because it is too tiny.

But according to some models, the discreteness should affect how particles move through space. It is a miniscule effect, but it adds up for particles that travel over very long distances. If true, this would distort images from far-away stellar objects, either by smearing out the image or by tearing apart the arrival times of particles that were emitted simultaneously and would otherwise arrive on Earth simultaneously.

Even if the direct effects on particle motion are unmeasurable, defects in the discrete structure could still be observable. Think of space-time like a diamond. If space-time is discrete, there should be imperfections.A random variable is said to be discrete if the total number of values it can take can be counted.

Alternatively, we can say that a discrete random variable can take only a discrete countable value such as 1, 2, 3, 4, etc.

For example, in case of the roll of a die, there could be only 6 outcomes. This is an example of a discrete random variable. Each outcome should also have a positive probability. The probabilities of each of these outcomes are given below:. In contrast to discrete random variable, a random variable will be called continuous if it can take an infinite number of values between the possible values for the random variable.

Examples include measuring the height of a person, or the amount of rain fall that a city receives. The number of possible outcomes is infinite. In that case, what is the probability that the random variable X will get a certain value x? P x will be 0 because we are talking about the possibility of one outcome from an infinite number of outcomes. In finance, some variables such as price change of a stock, or the returns earned by an investor are considered to be continuous, even though they are actually discrete, because the number of possible outcomes is large, and the probability of each outcome is very small.

For example, the probability of an investor earning a return of exactly 8. The probability distribution of a continuous random variable is called probability density function. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. This site uses Akismet to reduce spam.

Learn how your comment data is processed. Skip to primary navigation Skip to main content Skip to primary sidebar Skip to footer. Discrete Random Variable A random variable is said to be discrete if the total number of values it can take can be counted.

The probabilities of each of these outcomes are given below: xi P xi 1 0. Note: What would be the probability of the random variable X being equal to 5? Continuous Random Variable In contrast to discrete random variable, a random variable will be called continuous if it can take an infinite number of values between the possible values for the random variable.

Leave a Reply Cancel reply Your email address will not be published.We are having a heated discussion about whether data that we have is continuous or discrete. We are looking at number of calls coming into the Contact Center on a daily basis and want to lay those daily totals on a control chart. Is this continuous or discrete data? Best Regards, Bob J.

In certain situations, discrete data may take on characteristics of continuous data. Bob J was correct in that the underlying count is discrete.

We have been treating this as continuous data. Have we been treating delivery time as the wrong type of data all along insert gasp here? It is continuous. Kung Fu: I absolutely agree about time being continuous, but what we are measuring is total number of calls per day and then laying that on a control chart. From there you might try fitting your data to a known distribution, which might help. Keep in mind that lots of folks rush to call discrete data continuous so all the stats tools and tests will be available — but many have an assumption of normality, so you will increase your risk of making an incorrect conclusion.

Normal is continuous, but not all continuous data is normal. Most of the computer software out there can help fit distributions…. If you count entire days 5 days then it is discrete…. Hey Luke, hope all is well and glad to see you posting again.

Hope your holidays were good. Must be test day since you have time to post. I still owe you a phone call to catch up. Daddy Darth.

### Are house prices continuous or discrete data?

Darth, Good to see you up and about…. Your insight is always appreciated… Have a great weekend! Thanks for the report. Yes, I understand the front 30 acres have a tendency to flood. It may have washed out the bridge. Say you are measuring the diameter of some steel balls. Continous means infinite possible values between any two values. Continous means that the exact value can never be written down because you would need an infinte number of decimal places.

Continous means that you will never get two identical values. And while all this applies to the balls diameters, nothing of this applies to the DATA you can get measuring the balls diameters.

Why, because your instrument does not have infinite resolution, in never does, and even if it had you would not write down the infinite decimals. Am I splitting hairs? Yes, in some cases. What does it depends on?A clear understanding of the difference between discrete and continuous data is critical to the success of any Six Sigma practitioner. The decision about which statistical test is appropriate under a specific set of circumstances very often depends on whether the underlying data is discrete or continuous.

Discrete data are also referred to as attribute data. Discrete data take on a finite number of pre-determined points. The values that discrete data can take on are restricted to a list of two or more possibilities. Discrete data may be binary, where the value fits into one of two categories. For example, the sex of a person can take on two predetermined values — male or female.

A product may be defective or not defective. Discrete data may be ordinal, where values fit into one of three or more categories and there is an order or rank to the values.

Finally, discrete data may be nominal where the data fit into one of three or more categories where the order of the categories is arbitrary. For example, the color of a new car might fall into one of five categories — red, blue, silver, white and black. It should be noted that count data is discrete data. Items are counted in discrete units — one unit, two units, three units, etc.

For example, the number of correct answers on a 25 question test could be one of 26 values ranging from zero to Continuous data are also referred to as variable data. Continuous data exist on an interval and can take on any value. The number of possibilities for a continuous measurement within an interval is infinite.

Therefore, continuous data are measured on an infinitely divisible continuum. Examples of continuous data are the Ph of a solution, the length of an item in inches, and the weight of an item in pounds.

A good rule of thumb is that if the unit of measure can be divided in half and still make sense, the data is continuous. A special case, and one which often confuses Six Sigma students, is percentage data. Technically speaking, percentage data is discrete because the underlying data that the percentages are calculated from is discrete.

For example, the percentage of defects is calculated by dividing the number of defects discrete count data by the total number of opportunities to have a defect discrete count data.

In addition, dividing a percentage point into two or more parts still makes sense. Discrete data are easy to collect and interpret.A continuous random variable can take all values in an interval, while discrete variable can only take countable values. The variable "cost" is always rounded to 2 decimal places, and that's why it cannot take all possible values in an interval, so this technically should be discrete. But if you measure it accurately it should be treated as continuous.

Also refer to this answer by George. Is the cost of a loaf of bread discrete or continuous? Feb 13, Explanation: A continuous random variable can take all values in an interval, while discrete variable can only take countable values.

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How do you determine whether the quantitative variable is discrete or continuous given the See all questions in What is Statistics? Impact of this question views around the world. You can reuse this answer Creative Commons License.

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