True
a continous variable is one that can assume different values at each point, so if you were measuring height it could be 187.1, 187.2.. 187.8, but this can not be used for something such as measuring the amount of people in a family, because there can't be 3.4 people in a family. This is when discrete variable is used, this measures full numbers.
No, this is a discrete variable since it can assume only whole number values: 0, 1, 2, 3, ... . A continuous variable would be one such as volume of water in a swimming pool which could be measured in real number units of volume.
There is not enough information to say much. To start with, the correlation may not be significant. Furthermore, a linear relationship may not be an appropriate model. If you assume that a linear model is appropriate and if you assume that there is evidence to indicate that the correlation is significant (by this time you might as well assume anything you want!) then you could say that the dependent variable decreases by 0.13 units for every unit change in the independent variable - within the range of the independent variable.
In the simplest setting, a continuous random variable is one that can assume any value on some interval of the real numbers. For example, a uniform random variable is often defined on the unit interval [0,1], which means that this random variable could assume any value between 0 and 1, including 0 and 1. Some possibilities would be 1/3, 0.3214, pi/4, e/5, and so on ... in other words, any of the numbers in that interval. As another example, a normal random variable can assume any value between -infinity and +infinity (another interval). Most of these values would be extremely unlikely to occur but they would be possible. The random variable could assume values of 3, -10000, pi, 1000*pi, e*e, ... any possible value in the real numbers. It is also possible to define continue random variables that assume values on the entire (x,y) plane, or just on the circumference of a circle, or anywhere that you can imagine that is essentially equivalent (in some sense) to pieces of a real line.
That would be a discrete variable; or, in your case, it would probably be called a discrete random variable.
· A variable whose values are determined by the outcomes of a random experiment is called a random variable. A random variable is a discrete random variable if it can assume values, which are finite or countable infinite. For example, tossing of a die is a random experiment and its outcomes 1, 2, 3, 4, 5 and 6 are discrete random variable. When a coin is tossed, its outcomes head and tail are discrete random variable. Three coins are thrown; the number of heads is example of discrete random variable. Note that the outcomes need ot be integers or even numbers (eg colour of eyes). · If a random variable can assume every possible value in an interval [a, b], a< b, where a and b may be - infinity and + infinity respectively, for example, the points on number line between 0 and 1; Value of 'x' between 0 and 2; Number of heads on a coin when it is tossed infinite times.
True
A random variable such as the number of keys on each student's key chain is discrete because you can list the possible values it can assume. If it was continuous one would not be list a continuous random variable because it would be impossible. The keys on the key chains would be discrete.
No.A discrete variable is a variable that can not assume all possible values within a given range.For example, if I were to conduct a survey on the number of children people had, then my answers would be numbers such as 0, 1, 2 and so forth (i.e. they would be whole numbers). This particular variable could not have a value in between these (a non integer value), such as 1.345, as this makes no sense in this context. This makes the variable discrete.
a continous variable is one that can assume different values at each point, so if you were measuring height it could be 187.1, 187.2.. 187.8, but this can not be used for something such as measuring the amount of people in a family, because there can't be 3.4 people in a family. This is when discrete variable is used, this measures full numbers.
No, this is a discrete variable since it can assume only whole number values: 0, 1, 2, 3, ... . A continuous variable would be one such as volume of water in a swimming pool which could be measured in real number units of volume.
It is a matter of certainty. If you change only one variable and the outcome differs, then you may safely assume that the change in the one variable was responsible for that change in outcome. If you change more than one, then how would you know what was responsible? You wouldn't. You would be left guessing. One of the objectives of good science is reduce the guesswork down to as close to zero as possible.
A continuous variable is one that can assume different values between each point. Put as an example (e.g when looking at height) one can assume a height of 178, 178.1, 178.2. . . 178.9. Thus continuous variables can be used when looking at time or length for example. Continuous variables will differ from discrete variables which assume a fixed value for example number of times you take a shower, how many cars you have or how many kids in a family. Values can not be specified as decimals (e.g. you can not have 1.2 cars or 2.7 kids in a family).
A continuous variable is one that can assume different values between each point. Put as an example (e.g when looking at height) one can assume a height of 178, 178.1, 178.2. . . 178.9. Thus continuous variables can be used when looking at time or length for example. Continuous variables will differ from discrete variables which assume a fixed value for example number of times you take a shower, how many cars you have or how many kids in a family. Values can not be specified as decimals (e.g. you can not have 1.2 cars or 2.7 kids in a family).
variable
discrete distribution is the distribution that can use the value of a whole number only while continuous distribution is the distribution that can assume any value between two numbers.