Continuous
The graph of a continuous function will not have any 'breaks' or 'gaps' in it. You can draw it without lifting your pencil or pen. The graph of a discrete function will just be a set of lines.
A discrete random variable (RV) can only take a selected number of values whereas a continuous rv can take infinitely many.
Some manufacturing is discrete, some continuous.
Exponential distribution is a function of probability theory and statistics. This kind of distribution deals with continuous probability distributions and is part of the continuous analogue of the geometric distribution in math.
Continuous
The graph of a continuous function will not have any 'breaks' or 'gaps' in it. You can draw it without lifting your pencil or pen. The graph of a discrete function will just be a set of lines.
fist disply your anser
Demand schedule: a list of demand/price equivalencies. It can best be seen as a table with discrete points. Demand function: a continuous function of price-demand interaction. Main difference: schedule is discrete; function is continuous.
A discrete random variable (RV) can only take a selected number of values whereas a continuous rv can take infinitely many.
Into nothing at all? No, but it can decay from one thing into another completely. Using the exponential function to model out decay is an accurate estimate for large quantities of a substance, but if there are only a few hundred particles or so of something, the process is discrete and not continuous, so the exponential model is inaccurate.
Some manufacturing is discrete, some continuous.
ocean depth is a continuous or discrete variable?
Exponential distribution is a function of probability theory and statistics. This kind of distribution deals with continuous probability distributions and is part of the continuous analogue of the geometric distribution in math.
I will assume that you are asking about probability distribution functions. There are two types: discrete and continuous. Some might argue that a third type exists, which is a mix of discrete and continuous distributions. When representing discrete random variables, the probability distribution is probability mass function or "pmf." For continuous distributions, the theoretical distribution is the probability density function or "pdf." Some textbooks will call pmf's as discrete probability distributions. Common pmf's are binomial, multinomial, uniform discrete and Poisson. Common pdf's are the uniform, normal, log-normal, and exponential. Two common pdf's used in sample size, hypothesis testing and confidence intervals are the "t distribution" and the chi-square. Finally, the F distribution is used in more advanced hypothesis testing and regression.
These terms describe functions.A continuous function looks like a straight line or a curve, depending on if it is linear or quadratic.A discrete function looks like dots on a number line, only covering the integers, instead of numbers in between.
continuous discrete