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. 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.






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






4. S^2






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






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






7. 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.






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






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






10. 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.






11. 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.






12. Rejecting a true null hypothesis.






13. 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






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






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






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






17. Any specific experimental condition applied to the subjects






18. 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.






19. A subjective estimate of probability.






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






21. Is a sample and the associated data points.






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






23. 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).






24. 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.






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






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






27. Have no meaningful rank order among values.






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






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






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






31. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






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






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






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






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






36. 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






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






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






39. Probability of accepting a false null hypothesis.






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






41. 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.






42. The standard deviation of a sampling distribution.






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






44. 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.






45. 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.






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






47. Failing to reject a false null hypothesis.






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






49. 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






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