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. 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






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






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






4. Some commonly used symbols for population parameters






5. Is a sample and the associated data points.






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






7. 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.






8. 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.






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






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






11. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.






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






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






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






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






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






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






18. 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






19. Failing to reject a false null hypothesis.






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






21. Probability of rejecting a true null hypothesis.






22. 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.






23. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.

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


24. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).






25. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as






26. 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.






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






28. 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






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






30. 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.






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






32. Probability of accepting a false null hypothesis.






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






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






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






36. Cov[X - Y] :






37. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data






38. A group of individuals sharing some common features that might affect the treatment.






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






40. 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.






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






42. E[X] :






43. 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


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






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






46. 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






47. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P






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






49. 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.






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