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






2. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.






3. 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.






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






5. 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






6. Cov[X - Y] :






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






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






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






10. To find the average - or arithmetic mean - of a set of numbers:






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






12. 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






13. 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






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






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






16. Statistical methods can be used for summarizing or describing a collection of data; this is called






17. 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.






18. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o






19. 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).






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






21. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are






22. 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.






23. Another name for elementary event.






24. Is data arising from counting that can take only non-negative integer values.






25. 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.






26. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re






27. 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






28. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)






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






30. Is its expected value. The mean (or sample mean of a data set is just the average value.






31. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a






32. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.






33. Is a sample and the associated data points.






34. 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.






35. 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






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






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






38. 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'






39. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a






40. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.






41. Gives the probability distribution for a continuous random variable.






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






43. 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.






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






45. A measurement such that the random error is small






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






47. Some commonly used symbols for population parameters






48. 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.






49. Rejecting a true null hypothesis.






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