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. 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






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






3. 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.






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






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






6. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.






7. 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






8. Some commonly used symbols for population parameters






9. 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.






10. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).






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






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






13. Var[X] :






14. Any specific experimental condition applied to the subjects






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






16. 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.






17. 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.






18. 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.






19. 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


20. 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






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






22. 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.






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






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






25. Rejecting a true null hypothesis.






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






27. Probability of rejecting a true null hypothesis.






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






29. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.






30. 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






31. A data value that falls outside the overall pattern of the graph.






32. Data are gathered and correlations between predictors and response are investigated.






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






34. 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






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






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






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






38. 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.






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






40. Gives the probability of events in a probability space.






41. ?r






42. 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






43. Cov[X - Y] :






44. E[X] :






45. 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.






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






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






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






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