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CLEP General Mathematics: Probability And Statistics

Subjects : clep, math
Instructions:
  • Answer 50 questions in 15 minutes.
  • If you are not ready to take this test, you can study here.
  • 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. A numerical measure that describes an aspect of a sample.






2. (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.






3. Long-term upward or downward movement over time.






4. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.

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5. Is a sample and the associated data points.






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






7. 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






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






9. Cov[X - Y] :






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






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






12. 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.






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






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






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






16. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data






17. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.






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






19. S^2






20. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.






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






22. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.






23. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the






24. Have no meaningful rank order among values.






25. 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






26. 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






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






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






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






30. 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






31. 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.






32. E[X] :






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






34. Are simply two different terms for the same thing. Add the given values






35. 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






36. Is data that can take only two values - usually represented by 0 and 1.






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






38. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.






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






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






41. 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.






42. A subjective estimate of probability.






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






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






45. 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.






46. 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.






47. 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.






48. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.






49. Patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations - and are then used for drawing inferences about the process or population being studied; this is called






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