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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.
  • Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.

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






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






3. Is the probability distribution - under repeated sampling of the population - of a given statistic.






4. 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.






5. 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'






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






7. A numerical measure that describes an aspect of a sample.






8. 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






9. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.






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






11. Cov[X - Y] :






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






13. Is its expected value. The mean (or sample mean of a data set is just the average value.






14. 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






15. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise






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






17. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present






18. Is denoted by - pronounced 'x bar'.






19. 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.






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






21. Error also refers to the extent to which individual observations in a sample differ from a central value - such as






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






23. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to






24. E[X] :






25. When you have two or more competing models - choose the simpler of the two models.






26. ?r






27. Are usually written in upper case roman letters: X - Y - etc.






28. 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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29. 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.






30. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.






31. S^2






32. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a






33. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are






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






35. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.






36. 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.






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






38. (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






39. 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.






40. A subjective estimate of probability.






41. 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.






42. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.






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






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






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






46. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.






47. Statistics involve methods of using information from a sample to draw conclusions regarding the population.






48. 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






49. 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.






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