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CLEP General Mathematics: Probability And Statistics

Subjects : clep, math
Instructions:
  • Answer 50 questions in 15 minutes.
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  • Match each statement with the correct term.
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This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. Is defined as the expected value of random variable (X -






2. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.






3. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.






4. Of a group of numbers is the center point of all those number values.






5. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.






6. The probability of correctly detecting a false null hypothesis.






7. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.






8. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.






9. A variable describes an individual by placing the individual into a category or a group.






10. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.






11. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.






12. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.






13. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a






14. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






15. Describes a characteristic of an individual to be measured or observed.






16. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.






17. Is the length of the smallest interval which contains all the data.






18. A subjective estimate of probability.






19. In particular - the pdf of the standard normal distribution is denoted by






20. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl






21. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.






22. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.






23. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.






24. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.






25. Is a parameter that indexes a family of probability distributions.






26. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).






27. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)






28.






29. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.

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30. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.






31. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






32. Is a sample space over which a probability measure has been defined.






33. A measurement such that the random error is small






34. Rejecting a true null hypothesis.






35. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.






36. A list of individuals from which the sample is actually selected.






37. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.






38. A data value that falls outside the overall pattern of the graph.






39. Describes the spread in the values of the sample statistic when many samples are taken.






40. Is that part of a population which is actually observed.






41. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'






42. (cdfs) are denoted by upper case letters - e.g. F(x).






43. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re






44. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as






45. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.






46. Is the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).






47. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no






48. Any specific experimental condition applied to the subjects






49. Gives the probability distribution for a continuous random variable.






50. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).