![Chapter 2 Weak Convergence](http://s1.studyres.com/store/data/006885229_1-85086f88799efa37b53db0cc879674cd-300x300.png)
Central Limit Theorem
... occupied or not and X be the total number of rooms occupied per night, then X=X1+X2+…+X100, and X~BIN(100, 0.6) P(X>50)=1-P(X ≤50)=1[P(X=0)+P(X=1)+…+P(X=50)] or =P(X=51)+P(X=52)+…+P(X=100) That is a workable problem but requires a lot of ...
... occupied or not and X be the total number of rooms occupied per night, then X=X1+X2+…+X100, and X~BIN(100, 0.6) P(X>50)=1-P(X ≤50)=1[P(X=0)+P(X=1)+…+P(X=50)] or =P(X=51)+P(X=52)+…+P(X=100) That is a workable problem but requires a lot of ...
Random Sampling Versus Representative Samples
... Students resist some aspects of confidence intervals. They like the benefits (the confidence level and margin of error), but they resist accepting one of the basic ideas -- the idea that any random sample is sufficient for a confidence interval. They resist letting go of a related idea -- the idea t ...
... Students resist some aspects of confidence intervals. They like the benefits (the confidence level and margin of error), but they resist accepting one of the basic ideas -- the idea that any random sample is sufficient for a confidence interval. They resist letting go of a related idea -- the idea t ...
Set Prob 7 - Non-Mutually Exclusive
... a) Are skipping breakfast and skipping lunch mutually exclusive events? Explain. ...
... a) Are skipping breakfast and skipping lunch mutually exclusive events? Explain. ...
Probability
... basketball player on the North Side High School team is most likely to make her next free throw. What?! This is bizarre! She says if you get it right she will release you from “the game” for the night. But if you are wrong, she will put you one step closer to being stuck forever in . . . ...
... basketball player on the North Side High School team is most likely to make her next free throw. What?! This is bizarre! She says if you get it right she will release you from “the game” for the night. But if you are wrong, she will put you one step closer to being stuck forever in . . . ...
Randomness
![](https://en.wikipedia.org/wiki/Special:FilePath/RandomBitmap.png?width=300)
Randomness is the lack of pattern or predictability in events. A random sequence of events, symbols or steps has no order and does not follow an intelligible pattern or combination. Individual random events are by definition unpredictable, but in many cases the frequency of different outcomes over a large number of events (or ""trials"") is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will occur twice as often as 4. In this view, randomness is a measure of uncertainty of an outcome, rather than haphazardness, and applies to concepts of chance, probability, and information entropy.The fields of mathematics, probability, and statistics use formal definitions of randomness. In statistics, a random variable is an assignment of a numerical value to each possible outcome of an event space. This association facilitates the identification and the calculation of probabilities of the events. Random variables can appear in random sequences. A random process is a sequence of random variables whose outcomes do not follow a deterministic pattern, but follow an evolution described by probability distributions. These and other constructs are extremely useful in probability theory and the various applications of randomness.Randomness is most often used in statistics to signify well-defined statistical properties. Monte Carlo methods, which rely on random input (such as from random number generators or pseudorandom number generators), are important techniques in science, as, for instance, in computational science. By analogy, quasi-Monte Carlo methods use quasirandom number generators.Random selection is a method of selecting items (often called units) from a population where the probability of choosing a specific item is the proportion of those items in the population. For example, with a bowl containing just 10 red marbles and 90 blue marbles, a random selection mechanism would choose a red marble with probability 1/10. Note that a random selection mechanism that selected 10 marbles from this bowl would not necessarily result in 1 red and 9 blue. In situations where a population consists of items that are distinguishable, a random selection mechanism requires equal probabilities for any item to be chosen. That is, if the selection process is such that each member of a population, of say research subjects, has the same probability of being chosen then we can say the selection process is random.