Test your basic knowledge |

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. Many statistical methods seek to minimize the mean-squared error - and these are called






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






3. 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.






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






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






6. A subjective estimate of probability.






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






8. 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.






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






10. 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).






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






12. Have no meaningful rank order among values.






13. 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.






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






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






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






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






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






19. Failing to reject a false null hypothesis.






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






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






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






23. 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






24. ?






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






26. 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.






27. 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.






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






29. Rejecting a true null hypothesis.






30. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris






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






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






33. Is a subset of the sample space - to which a probability can be assigned. For example - on rolling a die - 'getting a five or a six' is an event (with a probability of one third if the die is fair).






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






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






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






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






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






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






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






41. 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.






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






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






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






45. A numerical measure that describes an aspect of a population.






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






47. Is a sample and the associated data points.






48. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies






49. Any specific experimental condition applied to the subjects






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

Warning: Invalid argument supplied for foreach() in /var/www/html/basicversity.com/show_quiz.php on line 183