what is the difference between discrete and continuous variable quizlet?
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A discrete variable takes on individual distinct values, and a continuous variable can take on any value within an interval.
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Answer: Continuous if looking for exact age, discrete if going by number of years. If a data set is continuous, then the associated random variable could take on any value within the range. ... In such a case, Y , representing the age of an individual in years, could take on a finite number of values within our range.
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Discrete data involves round, concrete numbers that are determined by counting. Continuous data involves complex numbers that are measured across a specific time interval. A simple way to describe the difference between the two is to visualize a scatter plot graph vs. a line graph.
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The most discernible difference between discrete and process manufacturing is the way the product is created. In discrete manufacturing, identical products are duplicated by way of an assembly line. ... With process manufacturing, the end product is unable to be unassembled to its original raw materials.
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Graph: You can draw a continuous function without lifting your pencil from your paper. Graph: A discrete graph is a series of unconnected points (a scatter plot). Domain: a set of input values consisting of all numbers in an interval. Domain: a set of input values consisting of only certain numbers in an interval.
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Continuous variables are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or date/time. For example, the length of a part or the date and time a payment is received.
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If it can only jump from one category to the next, it's discontinuous. In other words, continuous variation is where the different types of variations are distributed on a continuum, while discontinuous variation is where the different types of variations are placed into discrete, individual categories.
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A variable is something that varies, meaning that it can take on a number of values. ... Interval-level variables are continuous, meaning that each value of the variable is one increment larger than the previous and one smaller than the next value. Age, if measured in years, is a good example; each increment is one year.
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A discrete variable is a variable which can only take a countable number of values. In this example, the number of heads can only take 4 values (0, 1, 2, 3) and so the variable is discrete. The variable is said to be random if the sum of the probabilities is one.
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A special class of discrete variables is periodic variables. A periodic variable is a feature for which the distance relation exists but there is no order relationn. Examples are days of the week, days of the month, or year.
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Discrete Variable. Variables that can only take on a finite number of values are called "discrete variables." All qualitative variables are discrete. Some quantitative variables are discrete, such as performance rated as 1,2,3,4, or 5, or temperature rounded to the nearest degree.
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Discrete data is based on counts. Only a finite number of values is possible, and the values cannot be subdivided meaningfully. ... Attribute data (aka discrete data) is data that can't be broken down into a smaller unit and add additional meaning. It is typically things counted in whole numbers.
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Digital electronics involves quantities with discrete values, and analog electronics involves quantities with continuous values. A digital quantity is one having a discrete set of values. Most things that can be measured quantitatively occur in nature in analog form. For example, S(t) = f(t), Fig.
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Discrete quantitative variables can take on only a countable number of values, there are gaps between the possible choices. These are usually count variables of some kind. In our set above, blood pressure measurements and Body Mass Index (or BMI) would be clearly continuous.
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If a random variable is a discrete variable, its probability distribution is called a discrete probability distribution. ... The random variable X can only take on the values 0, 1, or 2, so it is a discrete random variable. The probability distribution for this statistical experiment appears below.
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A continuous variable is one which can take on an uncountable set of values. is infinite and uncountable. ... In statistical theory, the probability distributions of continuous variables can be expressed in terms of probability density functions.
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There are three main types of variables: continuous variables can take any numerical value and are measured; discrete variables can only take certain numerical values and are counted; and categorical variables involve non-numeric groups or categories.