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






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






3. Var[X] :






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






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






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






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






9. A numerical measure that assesses the strength of a linear relationship between two variables.






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






11. Cov[X - Y] :






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






13. 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.






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






15. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit






16. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe






17. 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.






18. 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






19. 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.






20. 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.






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






22. 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.






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






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






25. 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'






26. 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.






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






28. 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.






29. Have no meaningful rank order among values.






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






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






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






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






34. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.






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






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






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






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






39. 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






40. Two variables such that their effects on the response variable cannot be distinguished from each other.






41. ?r






42. The standard deviation of a sampling distribution.






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






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






45. Probability of accepting a false null hypothesis.






46. Rejecting a true null hypothesis.






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






48. 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






49. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.






50. Is a sample and the associated data points.