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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. 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.






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






3. 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.






4. Where the null hypothesis is falsely rejected giving a 'false positive'.






5. Some commonly used symbols for sample statistics






6. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.






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






8. The collection of all possible outcomes in an experiment.






9. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively






10. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.






11. ?r






12. A pairwise independent collection of random variables is a set of random variables any two of which are independent.






13. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.






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






15. Probability of rejecting a true null hypothesis.






16. Is a function that gives the probability of all elements in a given space: see List of probability distributions






17. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.






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






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






20. When there is an even number of values...






21. ?






22. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.






23. A numerical facsimilie or representation of a real-world phenomenon.






24. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.






25. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).






26. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then






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






28. Failing to reject a false null hypothesis.






29. Var[X] :






30. Working from a null hypothesis two basic forms of error are recognized:






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






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






33. S^2






34. 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.






35. Data are gathered and correlations between predictors and response are investigated.






36. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.






37. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as






38. (or multivariate random variable) is a vector whose components are random variables on the same probability space.






39. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data






40. Have imprecise differences between consecutive values - but have a meaningful order to those values






41. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.






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






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






44. A measure that is relevant or appropriate as a representation of that property.






45. 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.






46. Is defined as the expected value of random variable (X -






47. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that






48. 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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49. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.






50.